diff --git "a/notebooks/00_Data Analysis.ipynb" "b/notebooks/00_Data Analysis.ipynb"
--- "a/notebooks/00_Data Analysis.ipynb"
+++ "b/notebooks/00_Data Analysis.ipynb"
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":17,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":18,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":19,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-7B-Instruct None False datasets/mac/mac.tsv results/mac-results.csv False 300\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path, use_english_datasets, max_new_tokens)"]},{"cell_type":"code","execution_count":20,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 8.99 ms, sys: 15.6 ms, total: 24.6 ms\n","Wall time: 1.82 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 58 columns):\n"," #   Column                                              Non-Null Count  Dtype \n","---  ------                                              --------------  ----- \n"," 0   chinese                                             1133 non-null   object\n"," 1   english                                             1133 non-null   object\n"," 2   Qwen/Qwen2-7B-Instruct/rpp-1.00                     1133 non-null   object\n"," 3   Qwen/Qwen2-7B-Instruct/rpp-1.02                     1133 non-null   object\n"," 4   Qwen/Qwen2-7B-Instruct/rpp-1.04                     1133 non-null   object\n"," 5   Qwen/Qwen2-7B-Instruct/rpp-1.06                     1133 non-null   object\n"," 6   Qwen/Qwen2-7B-Instruct/rpp-1.08                     1133 non-null   object\n"," 7   Qwen/Qwen2-7B-Instruct/rpp-1.10                     1133 non-null   object\n"," 8   Qwen/Qwen2-72B-Instruct/rpp-1.00                    1133 non-null   object\n"," 9   Qwen/Qwen2-7B-Instruct/rpp-1.12                     1133 non-null   object\n"," 10  Qwen/Qwen2-7B-Instruct/rpp-1.14                     1133 non-null   object\n"," 11  Qwen/Qwen2-7B-Instruct/rpp-1.16                     1133 non-null   object\n"," 12  Qwen/Qwen2-7B-Instruct/rpp-1.18                     1133 non-null   object\n"," 13  Qwen/Qwen2-7B-Instruct/rpp-1.20                     1133 non-null   object\n"," 14  Qwen/Qwen2-7B-Instruct/rpp-1.22                     1133 non-null   object\n"," 15  Qwen/Qwen2-7B-Instruct/rpp-1.24                     1133 non-null   object\n"," 16  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00  1133 non-null   object\n"," 17  Qwen/Qwen2-7B-Instruct/rpp-1.26                     1133 non-null   object\n"," 18  Qwen/Qwen2-72B-Instruct/rpp-1.02                    1133 non-null   object\n"," 19  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02  1133 non-null   object\n"," 20  Qwen/Qwen2-7B-Instruct/rpp-1.28                     1133 non-null   object\n"," 21  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04  1133 non-null   object\n"," 22  Qwen/Qwen2-7B-Instruct/rpp-1.30                     1133 non-null   object\n"," 23  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06  1133 non-null   object\n"," 24  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00      1133 non-null   object\n"," 25  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02      1133 non-null   object\n"," 26  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08  1133 non-null   object\n"," 27  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04      1133 non-null   object\n"," 28  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10  1133 non-null   object\n"," 29  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06      1133 non-null   object\n"," 30  Qwen/Qwen2-72B-Instruct/rpp-1.04                    1133 non-null   object\n"," 31  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  1133 non-null   object\n"," 32  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08      1133 non-null   object\n"," 33  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  1133 non-null   object\n"," 34  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10      1133 non-null   object\n"," 35  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  1133 non-null   object\n"," 36  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12      1133 non-null   object\n"," 37  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  1133 non-null   object\n"," 38  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14      1133 non-null   object\n"," 39  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16      1133 non-null   object\n"," 40  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  1133 non-null   object\n"," 41  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18      1133 non-null   object\n"," 42  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  1133 non-null   object\n"," 43  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20      1133 non-null   object\n"," 44  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  1133 non-null   object\n"," 45  Qwen/Qwen2-72B-Instruct/rpp-1.06                    1133 non-null   object\n"," 46  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22      1133 non-null   object\n"," 47  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  1133 non-null   object\n"," 48  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  1133 non-null   object\n"," 49  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24      1133 non-null   object\n"," 50  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  1133 non-null   object\n"," 51  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26      1133 non-null   object\n"," 52  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28      1133 non-null   object\n"," 53  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30      1133 non-null   object\n"," 54  Qwen/Qwen2-72B-Instruct/rpp-1.08                    1133 non-null   object\n"," 55  Qwen/Qwen2-72B-Instruct/rpp-1.10                    1133 non-null   object\n"," 56  Qwen/Qwen2-72B-Instruct/rpp-1.12                    1133 non-null   object\n"," 57  internlm/internlm2_5-7b-chat/rpp-1.00               1133 non-null   object\n","dtypes: object(58)\n","memory usage: 513.5+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"get_metrics() got an unexpected keyword argument 'max_new_tokens'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m metrics_df \u001b[38;5;241m=\u001b[39m \u001b[43mget_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m metrics_df\n","\u001b[0;31mTypeError\u001b[0m: get_metrics() got an unexpected keyword argument 'max_new_tokens'"]}],"source":["metrics_df = get_metrics(df, max_new_tokens=max_new_tokens)\n","metrics_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(\n","    lambda x: x[\"meteor\"] / math.log10(10 + x[\"total_repetitions\"]), axis=1\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>rpp</th>\n","      <th>meteor</th>\n","      <th>bleu_1</th>\n","      <th>rouge_l</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>num_entries_with_max_output_tokens</th>\n","      <th>rap</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.00</td>\n","      <td>0.393169</td>\n","      <td>0.122732</td>\n","      <td>0.384361</td>\n","      <td>0.0</td>\n","      <td>0.363636</td>\n","      <td>0.363636</td>\n","      <td>0</td>\n","      <td>0.387164</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.02</td>\n","      <td>0.392567</td>\n","      <td>0.124211</td>\n","      <td>0.383536</td>\n","      <td>0.0</td>\n","      <td>0.345984</td>\n","      <td>0.345984</td>\n","      <td>0</td>\n","      <td>0.386853</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.04</td>\n","      <td>0.392359</td>\n","      <td>0.124027</td>\n","      <td>0.383951</td>\n","      <td>0.0</td>\n","      <td>0.356575</td>\n","      <td>0.356575</td>\n","      <td>0</td>\n","      <td>0.386478</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.06</td>\n","      <td>0.390993</td>\n","      <td>0.123245</td>\n","      <td>0.383145</td>\n","      <td>0.0</td>\n","      <td>0.356575</td>\n","      <td>0.356575</td>\n","      <td>0</td>\n","      <td>0.385133</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.08</td>\n","      <td>0.391984</td>\n","      <td>0.122016</td>\n","      <td>0.383606</td>\n","      <td>0.0</td>\n","      <td>0.345984</td>\n","      <td>0.345984</td>\n","      <td>0</td>\n","      <td>0.386278</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.10</td>\n","      <td>0.391010</td>\n","      <td>0.120610</td>\n","      <td>0.381963</td>\n","      <td>0.0</td>\n","      <td>0.376876</td>\n","      <td>0.376876</td>\n","      <td>0</td>\n","      <td>0.384827</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.12</td>\n","      <td>0.389989</td>\n","      <td>0.118383</td>\n","      <td>0.381737</td>\n","      <td>0.0</td>\n","      <td>0.406884</td>\n","      <td>0.406884</td>\n","      <td>0</td>\n","      <td>0.383349</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.00</td>\n","      <td>0.377314</td>\n","      <td>0.117482</td>\n","      <td>0.368777</td>\n","      <td>0.0</td>\n","      <td>0.263901</td>\n","      <td>0.263901</td>\n","      <td>0</td>\n","      <td>0.373093</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.02</td>\n","      <td>0.377679</td>\n","      <td>0.116432</td>\n","      <td>0.369318</td>\n","      <td>0.0</td>\n","      <td>0.263901</td>\n","      <td>0.263901</td>\n","      <td>0</td>\n","      <td>0.373454</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.04</td>\n","      <td>0.377817</td>\n","      <td>0.115458</td>\n","      <td>0.368963</td>\n","      <td>0.0</td>\n","      <td>0.255075</td>\n","      <td>0.255075</td>\n","      <td>0</td>\n","      <td>0.373729</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.06</td>\n","      <td>0.377353</td>\n","      <td>0.115099</td>\n","      <td>0.367135</td>\n","      <td>0.0</td>\n","      <td>0.249779</td>\n","      <td>0.249779</td>\n","      <td>0</td>\n","      <td>0.373352</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.08</td>\n","      <td>0.374707</td>\n","      <td>0.111649</td>\n","      <td>0.364109</td>\n","      <td>0.0</td>\n","      <td>0.241836</td>\n","      <td>0.241836</td>\n","      <td>0</td>\n","      <td>0.370858</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.10</td>\n","      <td>0.372693</td>\n","      <td>0.109254</td>\n","      <td>0.360194</td>\n","      <td>0.0</td>\n","      <td>0.250662</td>\n","      <td>0.250662</td>\n","      <td>0</td>\n","      <td>0.368729</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.12</td>\n","      <td>0.370982</td>\n","      <td>0.106475</td>\n","      <td>0.358556</td>\n","      <td>0.0</td>\n","      <td>0.250662</td>\n","      <td>0.250662</td>\n","      <td>0</td>\n","      <td>0.367036</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.14</td>\n","      <td>0.368016</td>\n","      <td>0.104374</td>\n","      <td>0.356445</td>\n","      <td>0.0</td>\n","      <td>0.250662</td>\n","      <td>0.250662</td>\n","      <td>0</td>\n","      <td>0.364101</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.16</td>\n","      <td>0.367392</td>\n","      <td>0.102063</td>\n","      <td>0.354277</td>\n","      <td>0.0</td>\n","      <td>0.285084</td>\n","      <td>0.285084</td>\n","      <td>0</td>\n","      <td>0.362961</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.18</td>\n","      <td>0.363967</td>\n","      <td>0.098785</td>\n","      <td>0.350097</td>\n","      <td>0.0</td>\n","      <td>0.275375</td>\n","      <td>0.275375</td>\n","      <td>0</td>\n","      <td>0.359723</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.20</td>\n","      <td>0.359735</td>\n","      <td>0.095148</td>\n","      <td>0.346421</td>\n","      <td>0.0</td>\n","      <td>0.285966</td>\n","      <td>0.285966</td>\n","      <td>0</td>\n","      <td>0.355384</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.22</td>\n","      <td>0.357408</td>\n","      <td>0.091995</td>\n","      <td>0.345115</td>\n","      <td>0.0</td>\n","      <td>0.205649</td>\n","      <td>0.205649</td>\n","      <td>0</td>\n","      <td>0.354276</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.24</td>\n","      <td>0.353440</td>\n","      <td>0.086439</td>\n","      <td>0.339449</td>\n","      <td>0.0</td>\n","      <td>0.179170</td>\n","      <td>0.179170</td>\n","      <td>0</td>\n","      <td>0.350735</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.26</td>\n","      <td>0.347960</td>\n","      <td>0.081427</td>\n","      <td>0.334751</td>\n","      <td>0.0</td>\n","      <td>0.213592</td>\n","      <td>0.213592</td>\n","      <td>0</td>\n","      <td>0.344795</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.28</td>\n","      <td>0.343811</td>\n","      <td>0.073416</td>\n","      <td>0.330192</td>\n","      <td>0.0</td>\n","      <td>0.219771</td>\n","      <td>0.219771</td>\n","      <td>0</td>\n","      <td>0.340595</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.30</td>\n","      <td>0.340523</td>\n","      <td>0.072306</td>\n","      <td>0.326872</td>\n","      <td>0.0</td>\n","      <td>0.212710</td>\n","      <td>0.212710</td>\n","      <td>1</td>\n","      <td>0.337439</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.00</td>\n","      <td>0.357698</td>\n","      <td>0.101524</td>\n","      <td>0.345662</td>\n","      <td>0.0</td>\n","      <td>0.370697</td>\n","      <td>0.370697</td>\n","      <td>0</td>\n","      <td>0.352132</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.02</td>\n","      <td>0.358107</td>\n","      <td>0.101070</td>\n","      <td>0.345484</td>\n","      <td>0.0</td>\n","      <td>0.339806</td>\n","      <td>0.339806</td>\n","      <td>0</td>\n","      <td>0.352985</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.04</td>\n","      <td>0.356593</td>\n","      <td>0.100771</td>\n","      <td>0.344864</td>\n","      <td>0.0</td>\n","      <td>0.360106</td>\n","      <td>0.360106</td>\n","      <td>0</td>\n","      <td>0.351197</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.06</td>\n","      <td>0.356111</td>\n","      <td>0.099382</td>\n","      <td>0.343229</td>\n","      <td>0.0</td>\n","      <td>0.330980</td>\n","      <td>0.330980</td>\n","      <td>0</td>\n","      <td>0.351146</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.08</td>\n","      <td>0.354627</td>\n","      <td>0.096925</td>\n","      <td>0.342915</td>\n","      <td>0.0</td>\n","      <td>0.355693</td>\n","      <td>0.355693</td>\n","      <td>0</td>\n","      <td>0.349324</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.10</td>\n","      <td>0.352866</td>\n","      <td>0.096716</td>\n","      <td>0.341288</td>\n","      <td>0.0</td>\n","      <td>0.318623</td>\n","      <td>0.318623</td>\n","      <td>0</td>\n","      <td>0.348124</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.12</td>\n","      <td>0.351496</td>\n","      <td>0.094758</td>\n","      <td>0.340665</td>\n","      <td>0.0</td>\n","      <td>0.338923</td>\n","      <td>0.338923</td>\n","      <td>0</td>\n","      <td>0.346480</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.14</td>\n","      <td>0.352047</td>\n","      <td>0.094639</td>\n","      <td>0.340098</td>\n","      <td>0.0</td>\n","      <td>0.360106</td>\n","      <td>0.360106</td>\n","      <td>0</td>\n","      <td>0.346720</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.16</td>\n","      <td>0.350526</td>\n","      <td>0.093526</td>\n","      <td>0.338469</td>\n","      <td>0.0</td>\n","      <td>0.375993</td>\n","      <td>0.375993</td>\n","      <td>0</td>\n","      <td>0.344995</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.18</td>\n","      <td>0.348900</td>\n","      <td>0.092894</td>\n","      <td>0.337621</td>\n","      <td>0.0</td>\n","      <td>0.383054</td>\n","      <td>0.383054</td>\n","      <td>0</td>\n","      <td>0.343295</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.20</td>\n","      <td>0.347743</td>\n","      <td>0.091333</td>\n","      <td>0.335723</td>\n","      <td>0.0</td>\n","      <td>0.383054</td>\n","      <td>0.383054</td>\n","      <td>0</td>\n","      <td>0.342157</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.22</td>\n","      <td>0.346087</td>\n","      <td>0.090244</td>\n","      <td>0.334642</td>\n","      <td>0.0</td>\n","      <td>0.403354</td>\n","      <td>0.403354</td>\n","      <td>0</td>\n","      <td>0.340244</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.24</td>\n","      <td>0.344097</td>\n","      <td>0.083233</td>\n","      <td>0.332546</td>\n","      <td>0.0</td>\n","      <td>0.488085</td>\n","      <td>0.488085</td>\n","      <td>0</td>\n","      <td>0.337120</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.26</td>\n","      <td>0.342644</td>\n","      <td>0.085137</td>\n","      <td>0.330630</td>\n","      <td>0.0</td>\n","      <td>0.349515</td>\n","      <td>0.349515</td>\n","      <td>0</td>\n","      <td>0.337607</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.28</td>\n","      <td>0.340342</td>\n","      <td>0.083787</td>\n","      <td>0.328182</td>\n","      <td>0.0</td>\n","      <td>0.297440</td>\n","      <td>0.297440</td>\n","      <td>0</td>\n","      <td>0.336064</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.30</td>\n","      <td>0.339350</td>\n","      <td>0.081988</td>\n","      <td>0.327286</td>\n","      <td>0.0</td>\n","      <td>0.280671</td>\n","      <td>0.280671</td>\n","      <td>0</td>\n","      <td>0.335319</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.00</td>\n","      <td>0.325602</td>\n","      <td>0.083401</td>\n","      <td>0.315822</td>\n","      <td>0.0</td>\n","      <td>0.172109</td>\n","      <td>0.172109</td>\n","      <td>0</td>\n","      <td>0.323207</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.02</td>\n","      <td>0.325564</td>\n","      <td>0.083978</td>\n","      <td>0.315877</td>\n","      <td>0.0</td>\n","      <td>0.186231</td>\n","      <td>0.186231</td>\n","      <td>0</td>\n","      <td>0.322975</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.04</td>\n","      <td>0.325920</td>\n","      <td>0.080849</td>\n","      <td>0.316605</td>\n","      <td>0.0</td>\n","      <td>5.846425</td>\n","      <td>5.846425</td>\n","      <td>1</td>\n","      <td>0.271615</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.06</td>\n","      <td>0.325645</td>\n","      <td>0.081127</td>\n","      <td>0.315051</td>\n","      <td>0.0</td>\n","      <td>5.836717</td>\n","      <td>5.836717</td>\n","      <td>1</td>\n","      <td>0.271447</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.08</td>\n","      <td>0.325542</td>\n","      <td>0.081767</td>\n","      <td>0.314946</td>\n","      <td>0.0</td>\n","      <td>5.842012</td>\n","      <td>5.842012</td>\n","      <td>1</td>\n","      <td>0.271328</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.10</td>\n","      <td>0.325969</td>\n","      <td>0.085563</td>\n","      <td>0.315208</td>\n","      <td>0.0</td>\n","      <td>0.300971</td>\n","      <td>0.300971</td>\n","      <td>0</td>\n","      <td>0.321824</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.12</td>\n","      <td>0.324518</td>\n","      <td>0.085272</td>\n","      <td>0.313913</td>\n","      <td>0.0</td>\n","      <td>0.446602</td>\n","      <td>0.446602</td>\n","      <td>0</td>\n","      <td>0.318475</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.14</td>\n","      <td>0.322710</td>\n","      <td>0.084580</td>\n","      <td>0.313531</td>\n","      <td>0.0</td>\n","      <td>0.277140</td>\n","      <td>0.277140</td>\n","      <td>0</td>\n","      <td>0.318923</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.16</td>\n","      <td>0.322690</td>\n","      <td>0.083260</td>\n","      <td>0.313136</td>\n","      <td>0.0</td>\n","      <td>0.282436</td>\n","      <td>0.282436</td>\n","      <td>0</td>\n","      <td>0.318833</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.18</td>\n","      <td>0.321639</td>\n","      <td>0.081791</td>\n","      <td>0.311101</td>\n","      <td>0.0</td>\n","      <td>0.156222</td>\n","      <td>0.156222</td>\n","      <td>0</td>\n","      <td>0.319488</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.20</td>\n","      <td>0.320616</td>\n","      <td>0.080396</td>\n","      <td>0.310410</td>\n","      <td>0.0</td>\n","      <td>0.156222</td>\n","      <td>0.156222</td>\n","      <td>0</td>\n","      <td>0.318472</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.22</td>\n","      <td>0.319095</td>\n","      <td>0.079396</td>\n","      <td>0.308024</td>\n","      <td>0.0</td>\n","      <td>0.153575</td>\n","      <td>0.153575</td>\n","      <td>0</td>\n","      <td>0.316997</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.24</td>\n","      <td>0.318944</td>\n","      <td>0.079632</td>\n","      <td>0.307217</td>\n","      <td>0.0</td>\n","      <td>0.100618</td>\n","      <td>0.100618</td>\n","      <td>0</td>\n","      <td>0.317564</td>\n","    </tr>\n","    <tr>\n","      <th>52</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.26</td>\n","      <td>0.317910</td>\n","      <td>0.077231</td>\n","      <td>0.306926</td>\n","      <td>0.0</td>\n","      <td>0.235658</td>\n","      <td>0.235658</td>\n","      <td>0</td>\n","      <td>0.314726</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.28</td>\n","      <td>0.315475</td>\n","      <td>0.075316</td>\n","      <td>0.304750</td>\n","      <td>0.0</td>\n","      <td>0.084731</td>\n","      <td>0.084731</td>\n","      <td>0</td>\n","      <td>0.314323</td>\n","    </tr>\n","    <tr>\n","      <th>54</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.30</td>\n","      <td>0.314237</td>\n","      <td>0.074543</td>\n","      <td>0.303469</td>\n","      <td>0.0</td>\n","      <td>0.125331</td>\n","      <td>0.125331</td>\n","      <td>0</td>\n","      <td>0.312547</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                        model   rpp    meteor    bleu_1  \\\n","0                     Qwen/Qwen2-72B-Instruct  1.00  0.393169  0.122732   \n","1                     Qwen/Qwen2-72B-Instruct  1.02  0.392567  0.124211   \n","2                     Qwen/Qwen2-72B-Instruct  1.04  0.392359  0.124027   \n","3                     Qwen/Qwen2-72B-Instruct  1.06  0.390993  0.123245   \n","4                     Qwen/Qwen2-72B-Instruct  1.08  0.391984  0.122016   \n","5                     Qwen/Qwen2-72B-Instruct  1.10  0.391010  0.120610   \n","6                     Qwen/Qwen2-72B-Instruct  1.12  0.389989  0.118383   \n","7                      Qwen/Qwen2-7B-Instruct  1.00  0.377314  0.117482   \n","8                      Qwen/Qwen2-7B-Instruct  1.02  0.377679  0.116432   \n","9                      Qwen/Qwen2-7B-Instruct  1.04  0.377817  0.115458   \n","10                     Qwen/Qwen2-7B-Instruct  1.06  0.377353  0.115099   \n","11                     Qwen/Qwen2-7B-Instruct  1.08  0.374707  0.111649   \n","12                     Qwen/Qwen2-7B-Instruct  1.10  0.372693  0.109254   \n","13                     Qwen/Qwen2-7B-Instruct  1.12  0.370982  0.106475   \n","14                     Qwen/Qwen2-7B-Instruct  1.14  0.368016  0.104374   \n","15                     Qwen/Qwen2-7B-Instruct  1.16  0.367392  0.102063   \n","16                     Qwen/Qwen2-7B-Instruct  1.18  0.363967  0.098785   \n","17                     Qwen/Qwen2-7B-Instruct  1.20  0.359735  0.095148   \n","18                     Qwen/Qwen2-7B-Instruct  1.22  0.357408  0.091995   \n","19                     Qwen/Qwen2-7B-Instruct  1.24  0.353440  0.086439   \n","20                     Qwen/Qwen2-7B-Instruct  1.26  0.347960  0.081427   \n","21                     Qwen/Qwen2-7B-Instruct  1.28  0.343811  0.073416   \n","22                     Qwen/Qwen2-7B-Instruct  1.30  0.340523  0.072306   \n","23      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.00  0.357698  0.101524   \n","24      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.02  0.358107  0.101070   \n","25      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.04  0.356593  0.100771   \n","26      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.06  0.356111  0.099382   \n","27      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.08  0.354627  0.096925   \n","28      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.10  0.352866  0.096716   \n","29      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.12  0.351496  0.094758   \n","30      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.14  0.352047  0.094639   \n","31      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.16  0.350526  0.093526   \n","32      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.18  0.348900  0.092894   \n","33      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.20  0.347743  0.091333   \n","34      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.22  0.346087  0.090244   \n","35      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.24  0.344097  0.083233   \n","36      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.26  0.342644  0.085137   \n","37      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.28  0.340342  0.083787   \n","38      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.30  0.339350  0.081988   \n","39  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.00  0.325602  0.083401   \n","40  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.02  0.325564  0.083978   \n","41  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.04  0.325920  0.080849   \n","42  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.06  0.325645  0.081127   \n","43  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.08  0.325542  0.081767   \n","44  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.10  0.325969  0.085563   \n","45  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.12  0.324518  0.085272   \n","46  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.14  0.322710  0.084580   \n","47  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.16  0.322690  0.083260   \n","48  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.18  0.321639  0.081791   \n","49  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.20  0.320616  0.080396   \n","50  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.22  0.319095  0.079396   \n","51  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.24  0.318944  0.079632   \n","52  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.26  0.317910  0.077231   \n","53  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.28  0.315475  0.075316   \n","54  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.30  0.314237  0.074543   \n","\n","     rouge_l  ews_score  repetition_score  total_repetitions  \\\n","0   0.384361        0.0          0.363636           0.363636   \n","1   0.383536        0.0          0.345984           0.345984   \n","2   0.383951        0.0          0.356575           0.356575   \n","3   0.383145        0.0          0.356575           0.356575   \n","4   0.383606        0.0          0.345984           0.345984   \n","5   0.381963        0.0          0.376876           0.376876   \n","6   0.381737        0.0          0.406884           0.406884   \n","7   0.368777        0.0          0.263901           0.263901   \n","8   0.369318        0.0          0.263901           0.263901   \n","9   0.368963        0.0          0.255075           0.255075   \n","10  0.367135        0.0          0.249779           0.249779   \n","11  0.364109        0.0          0.241836           0.241836   \n","12  0.360194        0.0          0.250662           0.250662   \n","13  0.358556        0.0          0.250662           0.250662   \n","14  0.356445        0.0          0.250662           0.250662   \n","15  0.354277        0.0          0.285084           0.285084   \n","16  0.350097        0.0          0.275375           0.275375   \n","17  0.346421        0.0          0.285966           0.285966   \n","18  0.345115        0.0          0.205649           0.205649   \n","19  0.339449        0.0          0.179170           0.179170   \n","20  0.334751        0.0          0.213592           0.213592   \n","21  0.330192        0.0          0.219771           0.219771   \n","22  0.326872        0.0          0.212710           0.212710   \n","23  0.345662        0.0          0.370697           0.370697   \n","24  0.345484        0.0          0.339806           0.339806   \n","25  0.344864        0.0          0.360106           0.360106   \n","26  0.343229        0.0          0.330980           0.330980   \n","27  0.342915        0.0          0.355693           0.355693   \n","28  0.341288        0.0          0.318623           0.318623   \n","29  0.340665        0.0          0.338923           0.338923   \n","30  0.340098        0.0          0.360106           0.360106   \n","31  0.338469        0.0          0.375993           0.375993   \n","32  0.337621        0.0          0.383054           0.383054   \n","33  0.335723        0.0          0.383054           0.383054   \n","34  0.334642        0.0          0.403354           0.403354   \n","35  0.332546        0.0          0.488085           0.488085   \n","36  0.330630        0.0          0.349515           0.349515   \n","37  0.328182        0.0          0.297440           0.297440   \n","38  0.327286        0.0          0.280671           0.280671   \n","39  0.315822        0.0          0.172109           0.172109   \n","40  0.315877        0.0          0.186231           0.186231   \n","41  0.316605        0.0          5.846425           5.846425   \n","42  0.315051        0.0          5.836717           5.836717   \n","43  0.314946        0.0          5.842012           5.842012   \n","44  0.315208        0.0          0.300971           0.300971   \n","45  0.313913        0.0          0.446602           0.446602   \n","46  0.313531        0.0          0.277140           0.277140   \n","47  0.313136        0.0          0.282436           0.282436   \n","48  0.311101        0.0          0.156222           0.156222   \n","49  0.310410        0.0          0.156222           0.156222   \n","50  0.308024        0.0          0.153575           0.153575   \n","51  0.307217        0.0          0.100618           0.100618   \n","52  0.306926        0.0          0.235658           0.235658   \n","53  0.304750        0.0          0.084731           0.084731   \n","54  0.303469        0.0          0.125331           0.125331   \n","\n","    num_entries_with_max_output_tokens       rap  \n","0                                    0  0.387164  \n","1                                    0  0.386853  \n","2                                    0  0.386478  \n","3                                    0  0.385133  \n","4                                    0  0.386278  \n","5                                    0  0.384827  \n","6                                    0  0.383349  \n","7                                    0  0.373093  \n","8                                    0  0.373454  \n","9                                    0  0.373729  \n","10                                   0  0.373352  \n","11                                   0  0.370858  \n","12                                   0  0.368729  \n","13                                   0  0.367036  \n","14                                   0  0.364101  \n","15                                   0  0.362961  \n","16                                   0  0.359723  \n","17                                   0  0.355384  \n","18                                   0  0.354276  \n","19                                   0  0.350735  \n","20                                   0  0.344795  \n","21                                   0  0.340595  \n","22                                   1  0.337439  \n","23                                   0  0.352132  \n","24                                   0  0.352985  \n","25                                   0  0.351197  \n","26                                   0  0.351146  \n","27                                   0  0.349324  \n","28                                   0  0.348124  \n","29                                   0  0.346480  \n","30                                   0  0.346720  \n","31                                   0  0.344995  \n","32                                   0  0.343295  \n","33                                   0  0.342157  \n","34                                   0  0.340244  \n","35                                   0  0.337120  \n","36                                   0  0.337607  \n","37                                   0  0.336064  \n","38                                   0  0.335319  \n","39                                   0  0.323207  \n","40                                   0  0.322975  \n","41                                   1  0.271615  \n","42                                   1  0.271447  \n","43                                   1  0.271328  \n","44                                   0  0.321824  \n","45                                   0  0.318475  \n","46                                   0  0.318923  \n","47                                   0  0.318833  \n","48                                   0  0.319488  \n","49                                   0  0.318472  \n","50                                   0  0.316997  \n","51                                   0  0.317564  \n","52                                   0  0.314726  \n","53                                   0  0.314323  \n","54                                   0  0.312547  "]},"execution_count":288,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n","       'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n","       'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":290,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n","    ax.plot(\n","        model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\n","    )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(model_df[\"rpp\"], model_df[\"total_repetitions\"], label=model)\n","\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n","    detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n","    lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.00</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.02</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.04</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.06</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.08</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.10</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.12</th>\n","      <th>Qwen/Qwen2-7B-Instruct/rpp-1.00</th>\n","      <th>...</th>\n","      <th>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20</th>\n","      <th>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22</th>\n","      <th>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24</th>\n","      <th>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26</th>\n","      <th>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28</th>\n","      <th>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>output_tokens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>193</th>\n","      <td>“有…… 没有…… 有…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"Yes... No... Yes... No...\"</td>\n","      <td>...</td>\n","      <td>Yes... No... Yes... No...</td>\n","      <td>Yes... No... Yes... No...</td>\n","      <td>Yes... No... Yes... No...</td>\n","      <td>Yes... No... Yes... No...</td>\n","      <td>Yes... No... Yes... No...</td>\n","      <td>Yes... No... Yes... No...</td>\n","      <td>0</td>\n","      <td>6496</td>\n","      <td>6496</td>\n","      <td>2049</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 61 columns</p>\n","</div>"],"text/plain":["                chinese                                 english  \\\n","193  “有…… 没有…… 有…… 没有……  'Yes . . . no . . . yes . . . no . . .   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.00  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.02  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.04  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.06  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.08  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.10  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.12  \\\n","193  \"There is... There isn't... There is... There ...   \n","\n","    Qwen/Qwen2-7B-Instruct/rpp-1.00  ...  \\\n","193     \"Yes... No... Yes... No...\"  ...   \n","\n","    shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  \\\n","193                          Yes... No... Yes... No...   \n","\n","    shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  \\\n","193                          Yes... No... Yes... No...   \n","\n","    shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  \\\n","193                          Yes... No... Yes... No...   \n","\n","    shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  \\\n","193                          Yes... No... Yes... No...   \n","\n","    shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  \\\n","193                          Yes... No... Yes... No...   \n","\n","    shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 ews_score  \\\n","193                          Yes... No... Yes... No...         0   \n","\n","    repetition_score total_repetitions output_tokens  \n","193             6496              6496          2049  \n","\n","[1 rows x 61 columns]"]},"execution_count":295,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Yes, I can help you with that! Here's the translation:\n","\n","\"Yes, I can help you with that! Here's the translation:\n","\n","有 - Yes\n","没有 - No\n","\n","So, the translated content is:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(159, 6655), match=\":\\n\\nYes, I can help you with that! Here's the tr>\n","Group 1 found at 159-3407: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 3407-6655: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 3407-6655: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 6496, 6496)\n"]},{"data":{"text/plain":["(0, 6496, 6496)"]},"execution_count":299,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>output_tokens</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1133.0</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.0</td>\n","      <td>5.846425</td>\n","      <td>5.846425</td>\n","      <td>33.958517</td>\n","      <td>29.458076</td>\n","      <td>29.458076</td>\n","      <td>29.432480</td>\n","      <td>32.805825</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.0</td>\n","      <td>192.990061</td>\n","      <td>192.990061</td>\n","      <td>63.822891</td>\n","      <td>20.126659</td>\n","      <td>20.126659</td>\n","      <td>20.076666</td>\n","      <td>21.906509</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>17.000000</td>\n","      <td>16.000000</td>\n","      <td>16.000000</td>\n","      <td>16.000000</td>\n","      <td>18.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>27.000000</td>\n","      <td>25.000000</td>\n","      <td>25.000000</td>\n","      <td>25.000000</td>\n","      <td>27.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>42.000000</td>\n","      <td>38.000000</td>\n","      <td>38.000000</td>\n","      <td>38.000000</td>\n","      <td>42.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.0</td>\n","      <td>6496.000000</td>\n","      <td>6496.000000</td>\n","      <td>2049.000000</td>\n","      <td>135.000000</td>\n","      <td>135.000000</td>\n","      <td>135.000000</td>\n","      <td>149.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       ews_score  repetition_score  total_repetitions  output_tokens  \\\n","count     1133.0       1133.000000        1133.000000    1133.000000   \n","mean         0.0          5.846425           5.846425      33.958517   \n","std          0.0        192.990061         192.990061      63.822891   \n","min          0.0          0.000000           0.000000       3.000000   \n","25%          0.0          0.000000           0.000000      17.000000   \n","50%          0.0          0.000000           0.000000      27.000000   \n","75%          0.0          0.000000           0.000000      42.000000   \n","max          0.0       6496.000000        6496.000000    2049.000000   \n","\n","       ground_truth_tokens-Qwen/Qwen2-72B-Instruct  \\\n","count                                  1133.000000   \n","mean                                     29.458076   \n","std                                      20.126659   \n","min                                       1.000000   \n","25%                                      16.000000   \n","50%                                      25.000000   \n","75%                                      38.000000   \n","max                                     135.000000   \n","\n","       ground_truth_tokens-Qwen/Qwen2-7B-Instruct  \\\n","count                                 1133.000000   \n","mean                                    29.458076   \n","std                                     20.126659   \n","min                                      1.000000   \n","25%                                     16.000000   \n","50%                                     25.000000   \n","75%                                     38.000000   \n","max                                    135.000000   \n","\n","       ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","count                                        1133.000000           \n","mean                                           29.432480           \n","std                                            20.076666           \n","min                                             1.000000           \n","25%                                            16.000000           \n","50%                                            25.000000           \n","75%                                            38.000000           \n","max                                           135.000000           \n","\n","       ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \n","count                                        1133.000000              \n","mean                                           32.805825              \n","std                                            21.906509              \n","min                                             2.000000              \n","25%                                            18.000000              \n","50%                                            27.000000              \n","75%                                            42.000000              \n","max                                           149.000000              "]},"execution_count":300,"metadata":{},"output_type":"execute_result"}],"source":["for model in models:\n","    df[f\"ground_truth_tokens-{model}\"] = df[\"english\"].apply(\n","        lambda x: len(tokenizers[model](x)[\"input_ids\"])\n","    )\n","\n","df.describe()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n","    workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":3,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-7B-Instruct None False datasets/mac/mac.tsv results/mac-results.csv False 300\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path, use_english_datasets, max_new_tokens)"]},{"cell_type":"code","execution_count":5,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 8.67 ms, sys: 9.11 ms, total: 17.8 ms\n","Wall time: 1.78 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package wordnet to\n","[nltk_data]     /Users/inflaton/nltk_data...\n","[nltk_data]   Package wordnet is already up-to-date!\n","[nltk_data] Downloading package punkt to /Users/inflaton/nltk_data...\n","[nltk_data]   Package punkt is already up-to-date!\n","[nltk_data] Downloading package omw-1.4 to\n","[nltk_data]     /Users/inflaton/nltk_data...\n","[nltk_data]   Package omw-1.4 is already up-to-date!\n"]},{"name":"stdout","output_type":"stream","text":["loading: /Users/inflaton/code/engd/papers/rapget-translation/eval_modules/calc_repetitions.py\n","loading /Users/inflaton/code/engd/papers/rapget-translation/llm_toolkit/translation_utils.py\n"]},{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package wordnet to\n","[nltk_data]     /Users/inflaton/nltk_data...\n","[nltk_data]   Package wordnet is already up-to-date!\n","[nltk_data] Downloading package punkt to /Users/inflaton/nltk_data...\n","[nltk_data]   Package punkt is already up-to-date!\n","[nltk_data] Downloading package omw-1.4 to\n","[nltk_data]     /Users/inflaton/nltk_data...\n","[nltk_data]   Package omw-1.4 is already up-to-date!\n"]},{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 60 columns):\n"," #   Column                                              Non-Null Count  Dtype \n","---  ------                                              --------------  ----- \n"," 0   chinese                                             1133 non-null   object\n"," 1   english                                             1133 non-null   object\n"," 2   Qwen/Qwen2-7B-Instruct/rpp-1.00                     1133 non-null   object\n"," 3   Qwen/Qwen2-7B-Instruct/rpp-1.02                     1133 non-null   object\n"," 4   Qwen/Qwen2-7B-Instruct/rpp-1.04                     1133 non-null   object\n"," 5   Qwen/Qwen2-7B-Instruct/rpp-1.06                     1133 non-null   object\n"," 6   Qwen/Qwen2-7B-Instruct/rpp-1.08                     1133 non-null   object\n"," 7   Qwen/Qwen2-7B-Instruct/rpp-1.10                     1133 non-null   object\n"," 8   Qwen/Qwen2-72B-Instruct/rpp-1.00                    1133 non-null   object\n"," 9   Qwen/Qwen2-7B-Instruct/rpp-1.12                     1133 non-null   object\n"," 10  Qwen/Qwen2-7B-Instruct/rpp-1.14                     1133 non-null   object\n"," 11  Qwen/Qwen2-7B-Instruct/rpp-1.16                     1133 non-null   object\n"," 12  Qwen/Qwen2-7B-Instruct/rpp-1.18                     1133 non-null   object\n"," 13  Qwen/Qwen2-7B-Instruct/rpp-1.20                     1133 non-null   object\n"," 14  Qwen/Qwen2-7B-Instruct/rpp-1.22                     1133 non-null   object\n"," 15  Qwen/Qwen2-7B-Instruct/rpp-1.24                     1133 non-null   object\n"," 16  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00  1133 non-null   object\n"," 17  Qwen/Qwen2-7B-Instruct/rpp-1.26                     1133 non-null   object\n"," 18  Qwen/Qwen2-72B-Instruct/rpp-1.02                    1133 non-null   object\n"," 19  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02  1133 non-null   object\n"," 20  Qwen/Qwen2-7B-Instruct/rpp-1.28                     1133 non-null   object\n"," 21  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04  1133 non-null   object\n"," 22  Qwen/Qwen2-7B-Instruct/rpp-1.30                     1133 non-null   object\n"," 23  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06  1133 non-null   object\n"," 24  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00      1133 non-null   object\n"," 25  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02      1133 non-null   object\n"," 26  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08  1133 non-null   object\n"," 27  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04      1133 non-null   object\n"," 28  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10  1133 non-null   object\n"," 29  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06      1133 non-null   object\n"," 30  Qwen/Qwen2-72B-Instruct/rpp-1.04                    1133 non-null   object\n"," 31  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12  1133 non-null   object\n"," 32  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08      1133 non-null   object\n"," 33  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14  1133 non-null   object\n"," 34  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10      1133 non-null   object\n"," 35  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16  1133 non-null   object\n"," 36  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12      1133 non-null   object\n"," 37  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18  1133 non-null   object\n"," 38  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14      1133 non-null   object\n"," 39  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16      1133 non-null   object\n"," 40  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20  1133 non-null   object\n"," 41  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18      1133 non-null   object\n"," 42  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22  1133 non-null   object\n"," 43  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20      1133 non-null   object\n"," 44  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24  1133 non-null   object\n"," 45  Qwen/Qwen2-72B-Instruct/rpp-1.06                    1133 non-null   object\n"," 46  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22      1133 non-null   object\n"," 47  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26  1133 non-null   object\n"," 48  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28  1133 non-null   object\n"," 49  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24      1133 non-null   object\n"," 50  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30  1133 non-null   object\n"," 51  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26      1133 non-null   object\n"," 52  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28      1133 non-null   object\n"," 53  shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30      1133 non-null   object\n"," 54  Qwen/Qwen2-72B-Instruct/rpp-1.08                    1133 non-null   object\n"," 55  Qwen/Qwen2-72B-Instruct/rpp-1.10                    1133 non-null   object\n"," 56  Qwen/Qwen2-72B-Instruct/rpp-1.12                    1133 non-null   object\n"," 57  internlm/internlm2_5-7b-chat/rpp-1.00               1133 non-null   object\n"," 58  Qwen/Qwen2-72B-Instruct/rpp-1.14                    1133 non-null   object\n"," 59  Qwen/Qwen2-72B-Instruct/rpp-1.16                    1133 non-null   object\n","dtypes: object(60)\n","memory usage: 531.2+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'internlm/internlm2_5-7b-chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.3931693232556192, 'bleu_scores': {'bleu': 0.12273151341458781, 'precisions': [0.4199273774494459, 0.16226917210268393, 0.07941374663072777, 0.04192938209331652], 'brevity_penalty': 1.0, 'length_ratio': 1.0581649552832064, 'translation_length': 31946, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44256389227683435, 'rouge2': 0.19256890797949983, 'rougeL': 0.3841780658533895, 'rougeLsum': 0.3845384116964501}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.02: {'meteor': 0.3925672197170406, 'bleu_scores': {'bleu': 0.12421056155279153, 'precisions': [0.4254972181364712, 0.16363093460734549, 0.08028819635962493, 0.042581432056249105], 'brevity_penalty': 1.0, 'length_ratio': 1.0359059291156012, 'translation_length': 31274, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4429970821375776, 'rouge2': 0.19206601115320354, 'rougeL': 0.38335469905546626, 'rougeLsum': 0.38384675906546384}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.04: {'meteor': 0.39235866930301305, 'bleu_scores': {'bleu': 0.12402693297052149, 'precisions': [0.4284005689164727, 0.16380901251551858, 0.07997907220090687, 0.04215992446800784], 'brevity_penalty': 1.0, 'length_ratio': 1.0247101689301092, 'translation_length': 30936, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44260368830242075, 'rouge2': 0.19198196296100534, 'rougeL': 0.3835641818715294, 'rougeLsum': 0.38408418573427067}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.06: {'meteor': 0.39099278006036825, 'bleu_scores': {'bleu': 0.1232450878300488, 'precisions': [0.4272606426093441, 0.16253786603837092, 0.07929176289453425, 0.04189893248806791], 'brevity_penalty': 1.0, 'length_ratio': 1.0216296787015569, 'translation_length': 30843, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4414071060355808, 'rouge2': 0.19134983960208077, 'rougeL': 0.3827609054316208, 'rougeLsum': 0.3834559199177233}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.08: {'meteor': 0.3919843215003691, 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'correct_ids': []}\n","shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24: {'meteor': 0.3188662188138966, 'bleu_scores': {'bleu': 0.07877965659256216, 'precisions': [0.3695673695673696, 0.11004456633527597, 0.045509665454026675, 0.020810881117841615], 'brevity_penalty': 1.0, 'length_ratio': 1.0037429612454456, 'translation_length': 30303, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35976606237561193, 'rouge2': 0.12560399746791165, 'rougeL': 0.30696739962431174, 'rougeLsum': 0.30737455060045693}, 'accuracy': 0.0, 'correct_ids': []}\n","shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26: {'meteor': 0.31805084189335, 'bleu_scores': {'bleu': 0.07777595035895293, 'precisions': [0.36718209093007154, 0.10867182683745462, 0.04475165680895033, 0.020491498997698417], 'brevity_penalty': 1.0, 'length_ratio': 1.0046704206690957, 'translation_length': 30331, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.358995677327433, 'rouge2': 0.12485871218877395, 'rougeL': 0.30650414534075454, 'rougeLsum': 0.3069741139019529}, 'accuracy': 0.0, 'correct_ids': []}\n","shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28: {'meteor': 0.31564132115319793, 'bleu_scores': {'bleu': 0.07471248687074669, 'precisions': [0.3653415084388186, 0.1064959079546622, 0.0426418723949984, 0.018780388226997735], 'brevity_penalty': 1.0, 'length_ratio': 1.004836038423319, 'translation_length': 30336, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35775695327285484, 'rouge2': 0.12384028192732648, 'rougeL': 0.30478290978616157, 'rougeLsum': 0.3053393078600185}, 'accuracy': 0.0, 'correct_ids': []}\n","shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30: {'meteor': 0.31448483374273595, 'bleu_scores': {'bleu': 0.07484673889486904, 'precisions': [0.36305669679539854, 0.10600163867267513, 0.04272017045454545, 0.01908848771825984], 'brevity_penalty': 1.0, 'length_ratio': 1.007784034448493, 'translation_length': 30425, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.35612794444075657, 'rouge2': 0.12285072991854483, 'rougeL': 0.30335650415176074, 'rougeLsum': 0.303729491278946}, 'accuracy': 0.0, 'correct_ids': []}\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>rpp</th>\n","      <th>meteor</th>\n","      <th>bleu_1</th>\n","      <th>rouge_l</th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>num_entries_with_max_output_tokens</th>\n","      <th>rap</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.00</td>\n","      <td>0.393169</td>\n","      <td>0.122732</td>\n","      <td>0.384178</td>\n","      <td>0.0</td>\n","      <td>0.363636</td>\n","      <td>0.363636</td>\n","      <td>0</td>\n","      <td>0.387164</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.02</td>\n","      <td>0.392567</td>\n","      <td>0.124211</td>\n","      <td>0.383355</td>\n","      <td>0.0</td>\n","      <td>0.345984</td>\n","      <td>0.345984</td>\n","      <td>0</td>\n","      <td>0.386853</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.04</td>\n","      <td>0.392359</td>\n","      <td>0.124027</td>\n","      <td>0.383564</td>\n","      <td>0.0</td>\n","      <td>0.356575</td>\n","      <td>0.356575</td>\n","      <td>0</td>\n","      <td>0.386478</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.06</td>\n","      <td>0.390993</td>\n","      <td>0.123245</td>\n","      <td>0.382761</td>\n","      <td>0.0</td>\n","      <td>0.356575</td>\n","      <td>0.356575</td>\n","      <td>0</td>\n","      <td>0.385133</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.08</td>\n","      <td>0.391984</td>\n","      <td>0.122016</td>\n","      <td>0.383369</td>\n","      <td>0.0</td>\n","      <td>0.345984</td>\n","      <td>0.345984</td>\n","      <td>0</td>\n","      <td>0.386278</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.10</td>\n","      <td>0.391010</td>\n","      <td>0.120610</td>\n","      <td>0.381597</td>\n","      <td>0.0</td>\n","      <td>0.376876</td>\n","      <td>0.376876</td>\n","      <td>0</td>\n","      <td>0.384827</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.12</td>\n","      <td>0.389989</td>\n","      <td>0.118383</td>\n","      <td>0.381314</td>\n","      <td>0.0</td>\n","      <td>0.406884</td>\n","      <td>0.406884</td>\n","      <td>0</td>\n","      <td>0.383349</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.14</td>\n","      <td>0.387215</td>\n","      <td>0.111564</td>\n","      <td>0.377934</td>\n","      <td>0.0</td>\n","      <td>4.695499</td>\n","      <td>4.695499</td>\n","      <td>1</td>\n","      <td>0.331752</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Qwen/Qwen2-72B-Instruct</td>\n","      <td>1.16</td>\n","      <td>0.387934</td>\n","      <td>0.115091</td>\n","      <td>0.378033</td>\n","      <td>0.0</td>\n","      <td>1.736099</td>\n","      <td>1.736099</td>\n","      <td>1</td>\n","      <td>0.362716</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.00</td>\n","      <td>0.377314</td>\n","      <td>0.117482</td>\n","      <td>0.368535</td>\n","      <td>0.0</td>\n","      <td>0.263901</td>\n","      <td>0.263901</td>\n","      <td>0</td>\n","      <td>0.373093</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.02</td>\n","      <td>0.377679</td>\n","      <td>0.116432</td>\n","      <td>0.369171</td>\n","      <td>0.0</td>\n","      <td>0.263901</td>\n","      <td>0.263901</td>\n","      <td>0</td>\n","      <td>0.373454</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.04</td>\n","      <td>0.377817</td>\n","      <td>0.115458</td>\n","      <td>0.368966</td>\n","      <td>0.0</td>\n","      <td>0.255075</td>\n","      <td>0.255075</td>\n","      <td>0</td>\n","      <td>0.373729</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.06</td>\n","      <td>0.377353</td>\n","      <td>0.115099</td>\n","      <td>0.367206</td>\n","      <td>0.0</td>\n","      <td>0.249779</td>\n","      <td>0.249779</td>\n","      <td>0</td>\n","      <td>0.373352</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.08</td>\n","      <td>0.374707</td>\n","      <td>0.111649</td>\n","      <td>0.364064</td>\n","      <td>0.0</td>\n","      <td>0.241836</td>\n","      <td>0.241836</td>\n","      <td>0</td>\n","      <td>0.370858</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.10</td>\n","      <td>0.372693</td>\n","      <td>0.109254</td>\n","      <td>0.360136</td>\n","      <td>0.0</td>\n","      <td>0.250662</td>\n","      <td>0.250662</td>\n","      <td>0</td>\n","      <td>0.368729</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.12</td>\n","      <td>0.370982</td>\n","      <td>0.106475</td>\n","      <td>0.358362</td>\n","      <td>0.0</td>\n","      <td>0.250662</td>\n","      <td>0.250662</td>\n","      <td>0</td>\n","      <td>0.367036</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.14</td>\n","      <td>0.368016</td>\n","      <td>0.104374</td>\n","      <td>0.356334</td>\n","      <td>0.0</td>\n","      <td>0.250662</td>\n","      <td>0.250662</td>\n","      <td>0</td>\n","      <td>0.364101</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.16</td>\n","      <td>0.367392</td>\n","      <td>0.102063</td>\n","      <td>0.354280</td>\n","      <td>0.0</td>\n","      <td>0.285084</td>\n","      <td>0.285084</td>\n","      <td>0</td>\n","      <td>0.362961</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.18</td>\n","      <td>0.363967</td>\n","      <td>0.098785</td>\n","      <td>0.350212</td>\n","      <td>0.0</td>\n","      <td>0.275375</td>\n","      <td>0.275375</td>\n","      <td>0</td>\n","      <td>0.359723</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.20</td>\n","      <td>0.359735</td>\n","      <td>0.095148</td>\n","      <td>0.346405</td>\n","      <td>0.0</td>\n","      <td>0.285966</td>\n","      <td>0.285966</td>\n","      <td>0</td>\n","      <td>0.355384</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.22</td>\n","      <td>0.357408</td>\n","      <td>0.091995</td>\n","      <td>0.345027</td>\n","      <td>0.0</td>\n","      <td>0.205649</td>\n","      <td>0.205649</td>\n","      <td>0</td>\n","      <td>0.354276</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.24</td>\n","      <td>0.353440</td>\n","      <td>0.086439</td>\n","      <td>0.339539</td>\n","      <td>0.0</td>\n","      <td>0.179170</td>\n","      <td>0.179170</td>\n","      <td>0</td>\n","      <td>0.350735</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.26</td>\n","      <td>0.347960</td>\n","      <td>0.081427</td>\n","      <td>0.334934</td>\n","      <td>0.0</td>\n","      <td>0.213592</td>\n","      <td>0.213592</td>\n","      <td>0</td>\n","      <td>0.344795</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.28</td>\n","      <td>0.343811</td>\n","      <td>0.073416</td>\n","      <td>0.330449</td>\n","      <td>0.0</td>\n","      <td>0.219771</td>\n","      <td>0.219771</td>\n","      <td>2</td>\n","      <td>0.340595</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>Qwen/Qwen2-7B-Instruct</td>\n","      <td>1.30</td>\n","      <td>0.340523</td>\n","      <td>0.072306</td>\n","      <td>0.326943</td>\n","      <td>0.0</td>\n","      <td>0.212710</td>\n","      <td>0.212710</td>\n","      <td>3</td>\n","      <td>0.337439</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>internlm/internlm2_5-7b-chat</td>\n","      <td>1.00</td>\n","      <td>0.365433</td>\n","      <td>0.110086</td>\n","      <td>0.358268</td>\n","      <td>0.0</td>\n","      <td>0.300971</td>\n","      <td>0.300971</td>\n","      <td>0</td>\n","      <td>0.360787</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.00</td>\n","      <td>0.357698</td>\n","      <td>0.101524</td>\n","      <td>0.345205</td>\n","      <td>0.0</td>\n","      <td>0.370697</td>\n","      <td>0.370697</td>\n","      <td>0</td>\n","      <td>0.352132</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.02</td>\n","      <td>0.358107</td>\n","      <td>0.101070</td>\n","      <td>0.345272</td>\n","      <td>0.0</td>\n","      <td>0.339806</td>\n","      <td>0.339806</td>\n","      <td>0</td>\n","      <td>0.352985</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.04</td>\n","      <td>0.356593</td>\n","      <td>0.100771</td>\n","      <td>0.344649</td>\n","      <td>0.0</td>\n","      <td>0.360106</td>\n","      <td>0.360106</td>\n","      <td>0</td>\n","      <td>0.351197</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.06</td>\n","      <td>0.356111</td>\n","      <td>0.099382</td>\n","      <td>0.343023</td>\n","      <td>0.0</td>\n","      <td>0.330980</td>\n","      <td>0.330980</td>\n","      <td>0</td>\n","      <td>0.351146</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.08</td>\n","      <td>0.354627</td>\n","      <td>0.096925</td>\n","      <td>0.342643</td>\n","      <td>0.0</td>\n","      <td>0.355693</td>\n","      <td>0.355693</td>\n","      <td>0</td>\n","      <td>0.349324</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.10</td>\n","      <td>0.352866</td>\n","      <td>0.096716</td>\n","      <td>0.341078</td>\n","      <td>0.0</td>\n","      <td>0.318623</td>\n","      <td>0.318623</td>\n","      <td>0</td>\n","      <td>0.348124</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.12</td>\n","      <td>0.351496</td>\n","      <td>0.094758</td>\n","      <td>0.340491</td>\n","      <td>0.0</td>\n","      <td>0.338923</td>\n","      <td>0.338923</td>\n","      <td>0</td>\n","      <td>0.346480</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.14</td>\n","      <td>0.352047</td>\n","      <td>0.094639</td>\n","      <td>0.339958</td>\n","      <td>0.0</td>\n","      <td>0.360106</td>\n","      <td>0.360106</td>\n","      <td>0</td>\n","      <td>0.346720</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.16</td>\n","      <td>0.350526</td>\n","      <td>0.093526</td>\n","      <td>0.338255</td>\n","      <td>0.0</td>\n","      <td>0.375993</td>\n","      <td>0.375993</td>\n","      <td>0</td>\n","      <td>0.344995</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.18</td>\n","      <td>0.348900</td>\n","      <td>0.092894</td>\n","      <td>0.337278</td>\n","      <td>0.0</td>\n","      <td>0.383054</td>\n","      <td>0.383054</td>\n","      <td>0</td>\n","      <td>0.343295</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.20</td>\n","      <td>0.347743</td>\n","      <td>0.091333</td>\n","      <td>0.335440</td>\n","      <td>0.0</td>\n","      <td>0.383054</td>\n","      <td>0.383054</td>\n","      <td>0</td>\n","      <td>0.342157</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.22</td>\n","      <td>0.346087</td>\n","      <td>0.090244</td>\n","      <td>0.334349</td>\n","      <td>0.0</td>\n","      <td>0.403354</td>\n","      <td>0.403354</td>\n","      <td>0</td>\n","      <td>0.340244</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.24</td>\n","      <td>0.344097</td>\n","      <td>0.083233</td>\n","      <td>0.332371</td>\n","      <td>0.0</td>\n","      <td>0.488085</td>\n","      <td>0.488085</td>\n","      <td>2</td>\n","      <td>0.337120</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.26</td>\n","      <td>0.342644</td>\n","      <td>0.085137</td>\n","      <td>0.330541</td>\n","      <td>0.0</td>\n","      <td>0.349515</td>\n","      <td>0.349515</td>\n","      <td>2</td>\n","      <td>0.337607</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.28</td>\n","      <td>0.340342</td>\n","      <td>0.083787</td>\n","      <td>0.327979</td>\n","      <td>0.0</td>\n","      <td>0.297440</td>\n","      <td>0.297440</td>\n","      <td>0</td>\n","      <td>0.336064</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>shenzhi-wang/Llama3.1-8B-Chinese-Chat</td>\n","      <td>1.30</td>\n","      <td>0.339350</td>\n","      <td>0.081988</td>\n","      <td>0.327035</td>\n","      <td>0.0</td>\n","      <td>0.280671</td>\n","      <td>0.280671</td>\n","      <td>1</td>\n","      <td>0.335319</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.00</td>\n","      <td>0.325664</td>\n","      <td>0.083313</td>\n","      <td>0.316052</td>\n","      <td>0.0</td>\n","      <td>0.177405</td>\n","      <td>0.177405</td>\n","      <td>0</td>\n","      <td>0.323196</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.02</td>\n","      <td>0.326164</td>\n","      <td>0.084372</td>\n","      <td>0.315924</td>\n","      <td>0.0</td>\n","      <td>0.180936</td>\n","      <td>0.180936</td>\n","      <td>0</td>\n","      <td>0.323643</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.04</td>\n","      <td>0.326127</td>\n","      <td>0.084103</td>\n","      <td>0.315900</td>\n","      <td>0.0</td>\n","      <td>0.935569</td>\n","      <td>0.935569</td>\n","      <td>1</td>\n","      <td>0.313933</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.06</td>\n","      <td>0.326102</td>\n","      <td>0.084409</td>\n","      <td>0.315455</td>\n","      <td>0.0</td>\n","      <td>0.800530</td>\n","      <td>0.800530</td>\n","      <td>1</td>\n","      <td>0.315548</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.08</td>\n","      <td>0.325191</td>\n","      <td>0.085735</td>\n","      <td>0.315275</td>\n","      <td>0.0</td>\n","      <td>0.800530</td>\n","      <td>0.800530</td>\n","      <td>1</td>\n","      <td>0.314667</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.10</td>\n","      <td>0.325109</td>\n","      <td>0.085722</td>\n","      <td>0.314190</td>\n","      <td>0.0</td>\n","      <td>0.300971</td>\n","      <td>0.300971</td>\n","      <td>0</td>\n","      <td>0.320976</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.12</td>\n","      <td>0.325322</td>\n","      <td>0.085010</td>\n","      <td>0.313562</td>\n","      <td>0.0</td>\n","      <td>0.303619</td>\n","      <td>0.303619</td>\n","      <td>0</td>\n","      <td>0.321150</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.14</td>\n","      <td>0.322462</td>\n","      <td>0.083893</td>\n","      <td>0.313147</td>\n","      <td>0.0</td>\n","      <td>0.190644</td>\n","      <td>0.190644</td>\n","      <td>0</td>\n","      <td>0.319839</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.16</td>\n","      <td>0.323546</td>\n","      <td>0.083900</td>\n","      <td>0.313610</td>\n","      <td>0.0</td>\n","      <td>0.277140</td>\n","      <td>0.277140</td>\n","      <td>0</td>\n","      <td>0.319750</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.18</td>\n","      <td>0.322746</td>\n","      <td>0.082375</td>\n","      <td>0.312328</td>\n","      <td>0.0</td>\n","      <td>0.156222</td>\n","      <td>0.156222</td>\n","      <td>0</td>\n","      <td>0.320588</td>\n","    </tr>\n","    <tr>\n","      <th>52</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.20</td>\n","      <td>0.321348</td>\n","      <td>0.080215</td>\n","      <td>0.310804</td>\n","      <td>0.0</td>\n","      <td>0.137688</td>\n","      <td>0.137688</td>\n","      <td>0</td>\n","      <td>0.319451</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.22</td>\n","      <td>0.319397</td>\n","      <td>0.080273</td>\n","      <td>0.308835</td>\n","      <td>0.0</td>\n","      <td>0.153575</td>\n","      <td>0.153575</td>\n","      <td>0</td>\n","      <td>0.317297</td>\n","    </tr>\n","    <tr>\n","      <th>54</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.24</td>\n","      <td>0.318866</td>\n","      <td>0.078780</td>\n","      <td>0.306967</td>\n","      <td>0.0</td>\n","      <td>0.090909</td>\n","      <td>0.090909</td>\n","      <td>0</td>\n","      <td>0.317618</td>\n","    </tr>\n","    <tr>\n","      <th>55</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.26</td>\n","      <td>0.318051</td>\n","      <td>0.077776</td>\n","      <td>0.306504</td>\n","      <td>0.0</td>\n","      <td>0.082083</td>\n","      <td>0.082083</td>\n","      <td>0</td>\n","      <td>0.316926</td>\n","    </tr>\n","    <tr>\n","      <th>56</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.28</td>\n","      <td>0.315641</td>\n","      <td>0.074712</td>\n","      <td>0.304783</td>\n","      <td>0.0</td>\n","      <td>0.218005</td>\n","      <td>0.218005</td>\n","      <td>0</td>\n","      <td>0.312712</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</td>\n","      <td>1.30</td>\n","      <td>0.314485</td>\n","      <td>0.074847</td>\n","      <td>0.303357</td>\n","      <td>0.0</td>\n","      <td>0.076787</td>\n","      <td>0.076787</td>\n","      <td>0</td>\n","      <td>0.313444</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                        model   rpp    meteor    bleu_1  \\\n","0                     Qwen/Qwen2-72B-Instruct  1.00  0.393169  0.122732   \n","1                     Qwen/Qwen2-72B-Instruct  1.02  0.392567  0.124211   \n","2                     Qwen/Qwen2-72B-Instruct  1.04  0.392359  0.124027   \n","3                     Qwen/Qwen2-72B-Instruct  1.06  0.390993  0.123245   \n","4                     Qwen/Qwen2-72B-Instruct  1.08  0.391984  0.122016   \n","5                     Qwen/Qwen2-72B-Instruct  1.10  0.391010  0.120610   \n","6                     Qwen/Qwen2-72B-Instruct  1.12  0.389989  0.118383   \n","7                     Qwen/Qwen2-72B-Instruct  1.14  0.387215  0.111564   \n","8                     Qwen/Qwen2-72B-Instruct  1.16  0.387934  0.115091   \n","9                      Qwen/Qwen2-7B-Instruct  1.00  0.377314  0.117482   \n","10                     Qwen/Qwen2-7B-Instruct  1.02  0.377679  0.116432   \n","11                     Qwen/Qwen2-7B-Instruct  1.04  0.377817  0.115458   \n","12                     Qwen/Qwen2-7B-Instruct  1.06  0.377353  0.115099   \n","13                     Qwen/Qwen2-7B-Instruct  1.08  0.374707  0.111649   \n","14                     Qwen/Qwen2-7B-Instruct  1.10  0.372693  0.109254   \n","15                     Qwen/Qwen2-7B-Instruct  1.12  0.370982  0.106475   \n","16                     Qwen/Qwen2-7B-Instruct  1.14  0.368016  0.104374   \n","17                     Qwen/Qwen2-7B-Instruct  1.16  0.367392  0.102063   \n","18                     Qwen/Qwen2-7B-Instruct  1.18  0.363967  0.098785   \n","19                     Qwen/Qwen2-7B-Instruct  1.20  0.359735  0.095148   \n","20                     Qwen/Qwen2-7B-Instruct  1.22  0.357408  0.091995   \n","21                     Qwen/Qwen2-7B-Instruct  1.24  0.353440  0.086439   \n","22                     Qwen/Qwen2-7B-Instruct  1.26  0.347960  0.081427   \n","23                     Qwen/Qwen2-7B-Instruct  1.28  0.343811  0.073416   \n","24                     Qwen/Qwen2-7B-Instruct  1.30  0.340523  0.072306   \n","25               internlm/internlm2_5-7b-chat  1.00  0.365433  0.110086   \n","26      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.00  0.357698  0.101524   \n","27      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.02  0.358107  0.101070   \n","28      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.04  0.356593  0.100771   \n","29      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.06  0.356111  0.099382   \n","30      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.08  0.354627  0.096925   \n","31      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.10  0.352866  0.096716   \n","32      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.12  0.351496  0.094758   \n","33      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.14  0.352047  0.094639   \n","34      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.16  0.350526  0.093526   \n","35      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.18  0.348900  0.092894   \n","36      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.20  0.347743  0.091333   \n","37      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.22  0.346087  0.090244   \n","38      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.24  0.344097  0.083233   \n","39      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.26  0.342644  0.085137   \n","40      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.28  0.340342  0.083787   \n","41      shenzhi-wang/Llama3.1-8B-Chinese-Chat  1.30  0.339350  0.081988   \n","42  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.00  0.325664  0.083313   \n","43  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.02  0.326164  0.084372   \n","44  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.04  0.326127  0.084103   \n","45  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.06  0.326102  0.084409   \n","46  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.08  0.325191  0.085735   \n","47  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.10  0.325109  0.085722   \n","48  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.12  0.325322  0.085010   \n","49  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.14  0.322462  0.083893   \n","50  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.16  0.323546  0.083900   \n","51  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.18  0.322746  0.082375   \n","52  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.20  0.321348  0.080215   \n","53  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.22  0.319397  0.080273   \n","54  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.24  0.318866  0.078780   \n","55  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.26  0.318051  0.077776   \n","56  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.28  0.315641  0.074712   \n","57  shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  1.30  0.314485  0.074847   \n","\n","     rouge_l  ews_score  repetition_score  total_repetitions  \\\n","0   0.384178        0.0          0.363636           0.363636   \n","1   0.383355        0.0          0.345984           0.345984   \n","2   0.383564        0.0          0.356575           0.356575   \n","3   0.382761        0.0          0.356575           0.356575   \n","4   0.383369        0.0          0.345984           0.345984   \n","5   0.381597        0.0          0.376876           0.376876   \n","6   0.381314        0.0          0.406884           0.406884   \n","7   0.377934        0.0          4.695499           4.695499   \n","8   0.378033        0.0          1.736099           1.736099   \n","9   0.368535        0.0          0.263901           0.263901   \n","10  0.369171        0.0          0.263901           0.263901   \n","11  0.368966        0.0          0.255075           0.255075   \n","12  0.367206        0.0          0.249779           0.249779   \n","13  0.364064        0.0          0.241836           0.241836   \n","14  0.360136        0.0          0.250662           0.250662   \n","15  0.358362        0.0          0.250662           0.250662   \n","16  0.356334        0.0          0.250662           0.250662   \n","17  0.354280        0.0          0.285084           0.285084   \n","18  0.350212        0.0          0.275375           0.275375   \n","19  0.346405        0.0          0.285966           0.285966   \n","20  0.345027        0.0          0.205649           0.205649   \n","21  0.339539        0.0          0.179170           0.179170   \n","22  0.334934        0.0          0.213592           0.213592   \n","23  0.330449        0.0          0.219771           0.219771   \n","24  0.326943        0.0          0.212710           0.212710   \n","25  0.358268        0.0          0.300971           0.300971   \n","26  0.345205        0.0          0.370697           0.370697   \n","27  0.345272        0.0          0.339806           0.339806   \n","28  0.344649        0.0          0.360106           0.360106   \n","29  0.343023        0.0          0.330980           0.330980   \n","30  0.342643        0.0          0.355693           0.355693   \n","31  0.341078        0.0          0.318623           0.318623   \n","32  0.340491        0.0          0.338923           0.338923   \n","33  0.339958        0.0          0.360106           0.360106   \n","34  0.338255        0.0          0.375993           0.375993   \n","35  0.337278        0.0          0.383054           0.383054   \n","36  0.335440        0.0          0.383054           0.383054   \n","37  0.334349        0.0          0.403354           0.403354   \n","38  0.332371        0.0          0.488085           0.488085   \n","39  0.330541        0.0          0.349515           0.349515   \n","40  0.327979        0.0          0.297440           0.297440   \n","41  0.327035        0.0          0.280671           0.280671   \n","42  0.316052        0.0          0.177405           0.177405   \n","43  0.315924        0.0          0.180936           0.180936   \n","44  0.315900        0.0          0.935569           0.935569   \n","45  0.315455        0.0          0.800530           0.800530   \n","46  0.315275        0.0          0.800530           0.800530   \n","47  0.314190        0.0          0.300971           0.300971   \n","48  0.313562        0.0          0.303619           0.303619   \n","49  0.313147        0.0          0.190644           0.190644   \n","50  0.313610        0.0          0.277140           0.277140   \n","51  0.312328        0.0          0.156222           0.156222   \n","52  0.310804        0.0          0.137688           0.137688   \n","53  0.308835        0.0          0.153575           0.153575   \n","54  0.306967        0.0          0.090909           0.090909   \n","55  0.306504        0.0          0.082083           0.082083   \n","56  0.304783        0.0          0.218005           0.218005   \n","57  0.303357        0.0          0.076787           0.076787   \n","\n","    num_entries_with_max_output_tokens       rap  \n","0                                    0  0.387164  \n","1                                    0  0.386853  \n","2                                    0  0.386478  \n","3                                    0  0.385133  \n","4                                    0  0.386278  \n","5                                    0  0.384827  \n","6                                    0  0.383349  \n","7                                    1  0.331752  \n","8                                    1  0.362716  \n","9                                    0  0.373093  \n","10                                   0  0.373454  \n","11                                   0  0.373729  \n","12                                   0  0.373352  \n","13                                   0  0.370858  \n","14                                   0  0.368729  \n","15                                   0  0.367036  \n","16                                   0  0.364101  \n","17                                   0  0.362961  \n","18                                   0  0.359723  \n","19                                   0  0.355384  \n","20                                   0  0.354276  \n","21                                   0  0.350735  \n","22                                   0  0.344795  \n","23                                   2  0.340595  \n","24                                   3  0.337439  \n","25                                   0  0.360787  \n","26                                   0  0.352132  \n","27                                   0  0.352985  \n","28                                   0  0.351197  \n","29                                   0  0.351146  \n","30                                   0  0.349324  \n","31                                   0  0.348124  \n","32                                   0  0.346480  \n","33                                   0  0.346720  \n","34                                   0  0.344995  \n","35                                   0  0.343295  \n","36                                   0  0.342157  \n","37                                   0  0.340244  \n","38                                   2  0.337120  \n","39                                   2  0.337607  \n","40                                   0  0.336064  \n","41                                   1  0.335319  \n","42                                   0  0.323196  \n","43                                   0  0.323643  \n","44                                   1  0.313933  \n","45                                   1  0.315548  \n","46                                   1  0.314667  \n","47                                   0  0.320976  \n","48                                   0  0.321150  \n","49                                   0  0.319839  \n","50                                   0  0.319750  \n","51                                   0  0.320588  \n","52                                   0  0.319451  \n","53                                   0  0.317297  \n","54                                   0  0.317618  \n","55                                   0  0.316926  \n","56                                   0  0.312712  \n","57                                   0  0.313444  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df, max_output_tokens=max_new_tokens)\n","metrics_df"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n","       'internlm/internlm2_5-7b-chat',\n","       'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n","       'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n","    ax.plot(\n","        model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\n","    )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.6))\n","plt.show()"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0EAAAKTCAYAAADffANJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAADdTUlEQVR4nOzdeXzT9f0H8Nc3d5qm6UFPWm5KKSCgogLzFo8xUedvY56o23SzTpQ556ZOmfPa4d3p1E1088ADFY+pqKAyVJBDxUKh3L2BHkma5vx+fn/kaNOLpE36TdrX82EeSb755pt3a2n6yufzeX8lIYQAERERERHRMKFSugAiIiIiIqLBxBBERERERETDCkMQERERERENKwxBREREREQ0rDAEERERERHRsMIQREREREREwwpDEBERERERDSsapQsYCFmWUVtbC7PZDEmSlC6HiIiIiIgUIoSAzWZDQUEBVKq+x3qSOgTV1taiqKhI6TKIiIiIiChBHDhwAIWFhX3uk9QhyGw2A/B/oWlpaQpXA2DhQmD5cqWr6B/WrgzWrgzWrgzWrgzWrgzWrgzWrowEqd1qtaKoqCiUEfqS1CEoOAUuLS0tMUKQVgskQh39wdqVwdqVwdqVwdqVwdqVwdqVwdqVkWC1R7JMho0RiIiIiIhoWGEIIiIiIiKiYYUhiIiIiIiIhhWGICIiIiIiGlYYgoiIiIiIaFhhCCIiIiIiomGFIYiIiIiIiIYVhiAiIiIiIhpWGIKIiIiIiGhYScoQVF5ejtLSUsyaNUvpUoiIiIiIKMkkZQgqKytDRUUFNmzYoHQpRERERESUZJIyBBEREREREfUXQxAREREREQ0rDEFERERERDSsMAQREREREdGwwhBERERERETDCkMQERERERENKwxBREQUdw99uAMLR58Lh9urdClEREQMQUREFH/P/G8vvjQV4MvdTUqXQkRExBBERETxZXd50druAQBUNtgUroaIiIghiIiI4qy2pT10u7KeIYiIiJTHEERERHFV09wRgrYzBBERUQJgCCIioriq7jQStKvRDq9PVrAaIiIihiAiIoqzziNBbp+MvYfbFKyGiIiIIYiIiOKsptNIEMApcUREpDyGICIiiquaZgcAIN3rBMDmCEREpDyGICIiiqvaFn/4ObntAACGICIiUl5ShqDy8nKUlpZi1qxZSpdCRER9cHtlNNj8Ieg02z4APFcQEREpLylDUFlZGSoqKrBhwwalSyEioj7UtzohBKDXqDCnrRYAsL/JAYfbq3BlREQ0nCVlCCIiouRQ3eJfDzQy3YhsXzuyTDoIAexssCtcGRERDWcMQUREFDfB9tgjM4wAgEl5ZgBcF0RERMpiCCIiorgJtscemR4egtgmm4iIlMQQREREcVPbJQSVBELQDjZHICIiBTEEERFR3ARHggoCIag4lyNBRESkPIYgIiKKm65rgoIh6JDdhcN2l2J1ERHR8MYQREREcSHLInSi1OB0OJNeg1GZKQB4viAiIlIOQxAREcXFIbsLbp8MlQTkWQyh7cHRIHaIIyIipTAEERFRXFQH1gPlpRmgVXe83ZSwTTYRESmMIYiIiOIi1BkusB4oKHSuIE6HIyIihTAEERFRXASbIgQ7wwUFQ9COehtkWQx6XURERAxBREQUF11PlBo0doQJWrWENrcvtA8REdFgYggiIqK46NoeO0irVmF8dioArgsiIiJlMAQREVFc9DYSBHBdEBERKYshiIiI4iI4ElSY0XsI2s6RICIiUgBDEBERxZzV6YHN5QXQvTEC0NEmewdDEBERKSApQ1B5eTlKS0sxa9YspUshIqIeBEeBMlK0SNFpuj0ePGHqroN2uL3yoNZGRESUlCGorKwMFRUV2LBhg9KlEBFRD3prihA0Mt0Is14Dryyw+5B9MEsjIiJKzhBERESJra+mCAAgSRKKg80ROCWOiIgGGUMQERHFXEcISul1n+CUOIYgIiIabAxBREQUc0eaDgd0NEdgCCIiosHGEERERDHXMRJk6HUftskmIiKlMAQREVHMRTIdblJgOlxNSztsTs+g1EVERAQwBBERUYw5PT4ctLkA9D0dLsOkQ45ZDwDY0cAOcURENHgYgoiIKKbqWp0AAKNWjYwUbZ/7TuK6ICIiUgBDEBERxVTnpgiSJPW5b7A5wo4GhiAiIho8DEFERBRTtUc4R1BnwTbZ2+utca2JiIioM4YgIiKKqepACCqIIASV5KUB8E+HE0LEtS4iIqIghiAiIoqp4HS4wj6aIgRNzE2FJAHNDg8O2l3xLo2IiAgAQxAREcVYTYsDQGTT4QxaNcZkmQCwOQIREQ0ehiAiIoqp0DmCIhgJAjrOF8QQREREg4UhiIiIYsYnC9S1+FtkRzISBLBNNhERDT6GICIiipmDNhe8soBaJSE3zRDRc0IhiG2yiYhokDAEERFRzATXA+WlGaBW9X2OoKBJnc4V5JPZIY6IiOIvKUNQeXk5SktLMWvWLKVLISKiTqqbo1sPBABjskzQaVRwemQcaHLEqzQiIqKQpAxBZWVlqKiowIYNG5QuhYiIOgk2RSiMcD0QAKhVEibmpAIAtnNdEBERDYKkDEFERJSYavoxEgSwOQIREQ0uhiAiIoqZUHvsKEaCAKCk07ogIiKieGMIIiKimKmN8hxBQcWBcwVtr7fGvCYiIqKuGIKIiCgmhBCh6XAFUY8EpQEA9h52wOnxxbw2IiKizhiCiIgoJlrbPWhz+wNMtNPhctP0sBi18MkCuw7a41EeERFRCEMQERHFRLA99ohUHQxadVTPlSQJk3LZHIGIiAYHQxAREcVEf5siBLFDHBERDRaGICIiion+tscOCoYgniuIiIjijSGIiIhiojZGI0Fsk01ERPHGEERERDERnA4XbWe4oGCb7LpWJ1odnpjVRURE1BVDEBERxcRA1wRZjFoUWAwAgEqOBhERURwxBBERUUwMdE0QABQHmyMwBBERURwxBBER0YC1u3043OYGABSmp/T7OB0d4qwxqYuIiKgnCROC7rvvPkiShBtuuEHpUoiIKErBqXCpeg3SjJp+H6eEbbKJiGgQJEQI2rBhA/7xj3/gqKOOUroUIiLqh86d4SRJ6vdxJuWmAfCHICFETGojIiLqSvEQZLfbcckll+Cpp55CRkZGn/u6XC5YrdawCxERKa+jM5xhQMcZn2OCWiXB6vSi3uqMRWlERETdSELhj9oWLVqEzMxMPPjggzjllFMwY8YMPPTQQz3ue+edd2Lp0qXdtreefTbStNo4VxqB9euB445Tuor+Ye3KYO3KYO0x99fsWXgs+2hc2vQd/lS/tuedIqz9jPE/RpU+A8/sfxen2g/EuNJ+StDve0RYuzJYuzJYuzISpHarxwPLe++htbUVaWlpfe7b/4nbMfDSSy9h06ZN2LBhQ0T7/+53v8OSJUtC961WK4qKioDly4EjfKGDYsECYOVKpavoH9auDNauDNYeczXLtwCbazDy4guBU27ueacIa5/0wiZUfVOHHdcswaknj49tof2VoN/3iLB2ZbB2ZbB2ZSRK7VYrYLFEtKtiIejAgQNYvHgxVq1aBYMhsukTer0eer0+zpUREVG0YtEeO2hSrhnvoI7NEYiIKG4UC0EbN25EY2Mjjj766NA2n8+HTz/9FI899hhcLhfUarVS5RERURQGeqLUzoJtsrczBBERUZwoFoJOP/10fPvtt2HbrrzySpSUlOC3v/0tAxARUZLw+uRQE4PCGIwEBdtkVx20w+uToVEr3sOHiIiGGMVCkNlsxtSpU8O2mUwmZGVlddtORESJq8Hmgk8W0KolZKcOfMpyUUYKjFo12j0+7D3swISc1BhUSURE1IEfrxER0YAE1wPlW4xQqfp/jqAglUpCca4/+HBdEBERxYOi3eG6WrNmjdIlEBFRlGpaHABisx4oaFKeGV9Xt6KywYb5yI/ZcYmIiACOBBER0QDFsjNcUHGuf11QZT1Pik1ERLHHEERERAMSy85wQSV5/nO/cTocERHFA0MQERENSE2LvzNcLEeCgm2y9zU50O72xey4REREAEMQERENUE1z7NcEjUjVIdOkgxDAzkaOBhERUWwxBBERUb8JIeIyHU6SJEzK5UlTiYgoPhiCiIio35ra3HB6ZABAfrohpscOTonjuiAiIoo1hiAiIuq34ChQjlkPvUYd02MHQ9COBoYgIiKKLYYgIiLqt3i0xw4KhiBOhyMiolhjCCIion6Lx3qgoOC5gg7aXGhqc8f8+ERENHwxBBERUb/FMwSl6jUoDIwwcV0QERHFEkMQERH1WzynwwFASag5gjUuxyciouEpKUNQeXk5SktLMWvWLKVLISIa1uI5EgR06hDH5ghERBRDSRmCysrKUFFRgQ0bNihdChHRsBYKQXEaCZqUlwaA0+GIiCi2kjIEERGR8tpcXrQ4PADiOBKUG2yTbYcQIi6vQUREww9DEBER9UttYBTIbNDAbNDG5TXGZZugVUuwu7yoDqw/IiIiGiiGICIi6pfqOK8HAgCtWoXx2akAeNJUIiKKHYYgIiLql2BnuMI4rQcKCp4viCdNJSKiWGEIIiKifol3Z7igUIc4hiAiIooRhiAiIuqXeJ8jKCh4riBOhyMiolhhCCIion7pGAlKievrBKfD7Tpoh8cnx/W1iIhoeGAIIiKifgl2hytIN8T1dQozjEjVa+DxCew+2BbX1yIiouGBIYiIiKLm8closDoBxH86nCRJKM71d4ir5JQ4IiKKAYYgIiKKWn2rE7IAdBoVRpj0cX+9juYI1ri/FhERDX0MQUREFLXgiUtHphuhUklxf71JuewQR0REscMQREREURus9thBk/LSAHA6HBERxQZDEBERRa2mebBDkH8k6EBTO+wu76C8JhERDV0MQUREFLWOznCDE4IyTTpkm/1rj3i+ICIiGiiGICIiilpoOlycO8N1FjppKtcFERHRADEEERFR1AZ7TRDQcdLU7QxBREQ0QEkZgsrLy1FaWopZs2YpXQoR0bAjyyIUggoHcSSoo002QxAREQ1MUoagsrIyVFRUYMOGDUqXQkQ07Bxqc8HtlaGSgDyLYdBeNzgdrrLBBiHEoL0uERENPUkZgoiISDnBznC5aQZo1YP3NjIxxwxJApra3Dhkdw/a6xIR0dDDEERERFGpbXECGLzOcEFGnRqjM1MAcEocERENDEMQERFFpabFAWBwmyIEBdcFba+3DvprExHR0MEQREREUQmdKHUQmyIETcpLA8BzBRER0cAwBBERUVSUaI8dNCmXHeKIiGjgGIKIiCgq1YqOBAVOmNpghyyzQxwREfUPQxAREUUldI4gBUaCxmSlQKdRod3jw4Fmx6C/PhERDQ0MQUREFDGr0wOb0wtg8LvDAYBGrcKE7FQAwHZOiSMion5iCCIioojVBkaB0lO0MOk1itQQOmkqQxAREfVT1O9gLpcLX375Jfbt2weHw4Hs7GzMnDkTY8eOjUd9RESUQEKd4RQYBQoKrguqZIc4IiLqp4hD0P/+9z88/PDDeOutt+DxeGCxWGA0GtHU1ASXy4Vx48bh6quvxi9+8QuYzeZ41kxERApRsjNcUDFHgoiIaIAimg63YMECLFy4EGPGjMEHH3wAm82Gw4cPo7q6Gg6HAzt37sRtt92Gjz76CMXFxVi1alW86yYiIgUoeY6goOB0uD2H2uDy+hSrg4iIkldEI0Hz58/Ha6+9Bq1W2+Pj48aNw7hx47Bo0SJUVFSgrq4upkUSEVFiqE6AkaC8NAPSDBpYnV7samxDaUGaYrUQEVFyimgk6Jprruk1AHVVWlqK008/fUBFERFRYqpNgBAkSVKndUFWxeogIqLkFbPucHV1dbjuuutidTgiIkpAiTAdDuhojsA22URE1B9RdYf77rvvsHr1auh0Ovz4xz9Geno6Dh06hLvvvhtPPPEExo0bF686iYhIYS6vD402FwBlR4IAYFKefwrcDoYgIiLqh4hHglauXImZM2fi+uuvxy9+8Qsce+yxWL16NSZPnoxt27bh9ddfx3fffRfPWkPKy8tRWlqKWbNmDcrrERERUNfiBAAYtCpkmnSK1jIplx3iiIio/yIOQX/6059QVlYGq9WKBx54ALt378b111+Pd999F++99x7OPvvseNYZpqysDBUVFdiwYcOgvSYR0XDXuT22JEmK1hIMQbWtTrS2exSthYiIkk/EIaiyshJlZWVITU3Fr371K6hUKjz44IMcjSEiGiZCISgjReFKAEuKFvkWAwBgJ0+aSkREUYo4BNlsNqSl+edgq9VqGI1GrgEiIhpGQk0R0g0KV+JXnMvmCERE1D9RNUZ4//33YbFYAACyLOOjjz7C1q1bw/ZZsGBB7KojIqKEUZMA7bE7K8kz45MdB7kuiIiIohZVCFq0aFHY/WuuuSbsviRJ8Pl49m4ioqEoUdpjB4XOFcQQREREUYo4BMmyHM86iIgowXWMBCm/JgjomA5X2WCDEELxZg1ERJQ8Il4TdNVVV8Fm46dtRETDkSwL1LUm1kjQhJxUqFUSWts9aLC6lC6HiIiSSMQh6Nlnn0V7e3s8ayEiogR10O6CxyegVknINeuVLgcAYNCqMSbLPyq1vd6qcDVERJRMIg5BQoh41kFERAmsOrAeKC/NAI064reOuAuuC9rBNtlERBSFqBoj2Gw2GAx9t0YNttEmIqKhI9E6wwVNyk3Du9/Ws002ERFFJaoQVFxc3OtjwUWp7A5HRDT0JFpnuCB2iCMiov6IKgS9+uqryMzMjFctRESUoGpaHAASbySoJBCCdjba4ZP9a5aIiIiOJKoQNHfuXOTk5MSrFiIiSlCJOhJUlJkCg1YFp0fG3sNtGJ+dqnRJRESUBBJndSsRESWs2hYngMQbCVKrpI7zBXFKHBERRSjiEDR69Gio1ep41kJERAlICBFqjFCQYCEIACYxBBERUZQing63Z8+eeNZBREQJytruhd3lBZB4I0EAmyMQEVH0Ig5Bp512WkT7ffzxx/0uhoiIEk91oClClkkHoy7xZgSEQhDPFURERBGKOAStWbMGo0ePxvz586HVauNZ0xGVl5ejvLyc7biJiAZBojZFCAqGoL2H2+D0+GDQJl5QIyKixBJxCLr//vvxzDPP4JVXXsEll1yCq666ClOnTo1nbb0qKytDWVkZrFYrLBaLIjUQEQ0XiXqi1KDsVD0yUrRodniws8GOaYV8XyAior5F3BjhN7/5DSoqKvDGG2/AZrNh7ty5OO644/DEE0/AarXGs0YiIlJQbYKHIEmSQqNB2+v5fkREREcWdYvs2bNn46mnnkJdXR3Kysrwr3/9CwUFBQxCRERDVCJ3hgsqyUsDAOzguiAiIopAv88TtGnTJnzyySfYtm0bpk6dqvg6ISIiio9EXxMEIHSuoO3sEEdERBGIKgTV1tbinnvuQXFxMf7v//4PmZmZ+PLLL/HFF1/AaEzcN0ciIuq/RF8TBLBNNhERRSfixgjf//73sXr1apx55pn4y1/+gvnz50OjifjpRESUhJweHw7Z3QCAwoQeCUoFADTaXGhucyPDpFO4IiIiSmQRp5j33nsP+fn52L9/P5YuXYqlS5f2uN+mTZtiVhwRESkrOApk0qlhMSbutGezQYuR6UbUtLSjssGGE8ZlKV0SERElsIhD0B133BHPOoiIKAHVdmqKIEmSwtX0rSTP7A9B9QxBRETUN4YgIiLqVTI0RQialGfGR9sb2RyBiIiOqN/d4YiIaOhLhqYIQcHmCGyTTURERxJRCDr77LPxxRdfHHE/m82G+++/H+Xl5QMujIiIlJdsI0EAsKPeBiGEwtUQEVEii2g63I9+9CNceOGFsFgsOPfcc3HssceioKAABoMBzc3NqKiowNq1a/Huu+9i/vz5+Mtf/hLvuomIaBBUJ9FI0LgRqdCoJNhcXtS0tKMwI0XpkoiIKEFFFIJ++tOf4tJLL8Urr7yC5cuX48knn0RraysAQJIklJaW4qyzzsKGDRswefLkuBZMRESDJzgSlMjtsYN0GhXGZZuwo8GOHQ02hiAiIupVxI0R9Ho9Lr30Ulx66aUAgNbWVrS3tyMrKwtabeK2TSUiov7xyQL1VicAf3e4ZDApLw07GuzYXm/DaSW5SpdDREQJqt+NESwWC/Ly8gYUgB5//HEcddRRSEtLQ1paGmbPno3//ve//T4eERHFToPVCZ8soFFJyDEblC4nIiWBdUGV7BBHRER9ULQ7XGFhIe677z5s3LgRX331FU477TScd955+O6775Qsi4iI0NEZLj/dALUqsc8RFDQplyGIiIiOLOLpcPFw7rnnht2/++678fjjj+OLL77AlClTuu3vcrngcrlC961Wa9xrJCIarkKd4ZJkKhzQ0SFu10E7PD4ZWjXPBEFERN0pGoI68/l8eOWVV9DW1obZs2f3uM+9996LpUuXdn9g4UIgEdYlrV8PLFigdBX9w9qVwdqVwdojUjNiJpBzHEZu+hxYcO/ADzgItY8EYJp0Jdqgw56fXIliV3NsDsyfGWWwdmWwdmWw9oHzeCLfVyjsm2++ESaTSajVamGxWMQ777zT675Op1O0traGLgcOHBAARGtr6yBW3Idzz1W6gv5j7cpg7cpg7RG55bVvxOjfvi3+9kFlbA44SLWfX75WjP7t22LllprYHZQ/M8pg7cpg7cpg7QPW2toacTaIep7AgQMHUF1dHbq/fv163HDDDXjyySejPRQAYNKkSdiyZQu+/PJL/PKXv8SiRYtQUVHR4756vT7URCF4ISKi+KgNnSMoOZoiBHFdEBERHUnUIejiiy/G6tWrAQD19fWYN28e1q9fj1tvvRV//OMfoy5Ap9NhwoQJOOaYY3Dvvfdi+vTpePjhh6M+DhERxVZNKAQl1/l2guuCtjMEERFRL6IOQVu3bsVxxx0HAHj55ZcxdepUrFu3Ds8//zyWLVs24IJkWQ5rfkBERINPCNHRGCEJTpTaWTAE7WhgCCIiop5F3RjB4/FAr9cDAD788EMsCCyCKikpQV1dXVTH+t3vfodzzjkHo0aNgs1mwwsvvIA1a9bg/fffj7YsIiKKoWaHB+0eHwAg35Kc0+H2NznQ5vLCpE+YHkBERJQgoh4JmjJlCp544gl89tlnWLVqFc4++2wAQG1tLbKysqI6VmNjIy6//HJMmjQJp59+OjZs2ID3338f8+bNi7YsIiKKoeAoULZZD4NWrXA10clK1WNEqv/DOo4GERFRT6L+eOz+++/HBRdcgL/85S9YtGgRpk+fDgBYuXJlaJpcpP75z39G+/JERDQIalocAJLrHEGdleSZsbbKhR0NNswclaF0OURElGCiDkGnnHIKDh06BKvVioyMjjeWq6++GikpybV4loiIelbT4gSQvCGoONeMtVWH2ByBiIh61K+J0mq1OiwAAcCYMWNiUQ8RESWAZG2KEFSSxzbZRETUu6jXBDU0NOCyyy5DQUEBNBoN1Gp12IWIiJJfsk+HY4c4IiLqS9QjQVdccQX279+P22+/Hfn5+ZAkKR51ERGRgjrOEZScIWhibiokCThkd+OQ3RVqlEBERAT0IwStXbsWn332GWbMmBGHcoiIKBEk+3S4FJ0GozJTsO+wA5X1NoyYwBBEREQdop4OV1RUBCFEPGohIqIE4HB70ezwAEjeEAR0nC+I64KIiKirqEPQQw89hFtuuQV79+6NQzlERKS02sBUOLNegzSDVuFq+m8SmyMQEVEvop4Ot3DhQjgcDowfPx4pKSnQasPfIJuammJWHBERDb7qJJ8KFxQMQdvZHIGIiLqIOgQ99NBDcSiDiIgSRbI3RQgKtsne2WCDLAuoVGzkQ0REflGHoEWLFsWjDiIiShDJ3hQhaHSWCTq1Cg63D9XN7RiVxRN6ExGRX79Olurz+fDGG29g27ZtAIApU6ZgwYIFPE8QEdEQMFRGgrRqFcbnpGJbnRXb660MQUREFBJ1Y4SqqipMnjwZl19+OVasWIEVK1bg0ksvxZQpU7Br16541NhNeXk5SktLMWvWrEF5PSKi4WSojAQBHVPi2ByBiIg6izoEXX/99Rg/fjwOHDiATZs2YdOmTdi/fz/Gjh2L66+/Ph41dlNWVoaKigps2LBhUF6PiGg4CXaHK0jykSCgU4c4NkcgIqJOop4O98knn+CLL75AZmZmaFtWVhbuu+8+zJ07N6bFERHR4PL4ZNRbnQCAwqEQgniuICIi6kHUI0F6vR42W/c3E7vdDp1OF5OiiIhIGfWtTsgC0KlVGJGqV7qcAQuOBO0+1AaX16dwNURElCiiDkE/+MEPcPXVV+PLL7+EEAJCCHzxxRf4xS9+gQULFsSjRiIiGiQ1oalwhiHRUjrfYoDZoIFPFth9sE3pcoiIKEFEHYIeeeQRjB8/HrNnz4bBYIDBYMDcuXMxYcIEPPzww/GokYiIBslQaooAAJIkcUocERF1E/WaoPT0dLz55pvYuXMntm/fDgCYPHkyJkyYEPPiiIhocA2V9tidTcoz46t9zdjOEERERAH9Ok8QAEycOBETJ06MZS1ERKSwodQZLijYJnsHO8QREVFARCFoyZIluOuuu2AymbBkyZI+933ggQdiUhgREQ2+oTgSVMzpcERE1EVEIWjz5s3weDyh20RENDQNtTVBAFCSlwbAH/CsTg/SDFqFKyIiIqVFFIJWr17d420iIho6hBChkaDC9BSFq4kdS4oWeWkG1Fud2NlgwzGjM4/8JCIiGtKi7g531VVX9XieoLa2Nlx11VUxKYqIiAbfIbsbLq8MSQLyLAaly4mp4sC6IDZHICIioB8h6Nlnn0V7e3u37e3t7XjuuediUhQREQ2+4ChQrtkAnSbqt4eEFmyOwHVBREQERNEdzmq1hk6OarPZYDB0fEro8/nw7rvvIicnJy5FEhFR/NV2OlHqUMNzBRERUWcRh6D09HRIkgRJklBcXNztcUmSsHTp0pgWR0REg6ejKcLQWQ8UNCk4EtRggxACkiQpXBERESkp4hC0evVqCCFw2mmn4bXXXkNmZsfCUp1Oh9GjR6OgoCAuRRIRUfwNxfbYQRNyUqGSgBaHB402F3LTht5oFxERRS7iEHTyyScDAPbs2YNRo0bxUzQioiGmegi2xw4yaNUYM8KE3QfbUFlvYwgiIhrmIgpB33zzDaZOnQqVSoXW1lZ8++23ve571FFHxaw4IiIaPB3tsYdeCAL864KCIeik4mylyyEiIgVFFIJmzJiB+vp65OTkYMaMGZAkCUKIbvtJkgSfzxfzIrsqLy9HeXn5oLwWEdFwUdPsADA0R4IA/7qg/26tZ5tsIiKKLATt2bMH2dnZodtKKysrQ1lZGaxWKywWi9LlEBElPZvTA6vTCwAoGKIjQaE22Q1WhSshIiKlRRSCRo8eHbq9b98+zJkzBxpN+FO9Xi/WrVsXti8RESWH2hYnAMBi1CJVH/Fy0aRSHGiTvbPBDp8soFZxbSsR0XAV9dnwTj31VDQ1NXXb3trailNPPTUmRRER0eCqaQlMhRuio0AAMDrLBINWBZdXxr7DbUqXQ0RECoo6BPV2foXDhw/DZDLFpCgiIhpcNUO4M1yQWiVhYg5PmkpERFG0yP7hD38IwN/84IorroBerw895vP58M0332DOnDmxr5CIiOKuegifI6iz4lwzvq1pRWWDDedMy1e6HCIiUkjEISjYgEAIAbPZDKOx441Sp9PhhBNOwM9//vPYV0hERHEXHAkqHMIjQUCn5ggcCSIiGtYiDkHPPPMMAGDMmDG46aabOPWNiGgIqQ2MBA3VznBBkxiCiIgI/VgTdMcdd0Cv1+PDDz/EP/7xD9hs/jeS2tpa2O32mBdIRETxVzNMpsMFR4L2Hm6D08NzzRERDVdR90Hdt28fzj77bOzfvx8ulwvz5s2D2WzG/fffD5fLhSeeeCIedRIRUZy4vTIabS4AQ7sxAgBkm/VIT9GixeFBVaMdU0fyXHNERMNR1CNBixcvxrHHHovm5uawdUEXXHABPvroo5gWR0RE8VfX2g4hAINWhSyTTuly4kqSJEwKnC9oO6fEERENW1GPBH322WdYt24ddLrwN8oxY8agpqYmZoUREdHgCDZFKEg39ngKhKGmJM+ML/c0YUcDQxAR0XAV9UiQLMvw+brPo66urobZbI5JUURENHiGy3qgoOI8jgQREQ13UYegM888Ew899FDoviRJsNvtuOOOO/D9738/lrUREdEgGG4hqKNNtlXhSoiISClRT4f729/+hrPOOgulpaVwOp24+OKLsXPnTowYMQIvvvhiPGokIqI4Ck6HGy4hqDiwJqjB6kKLw430lKG9DoqIiLqLOgQVFhbi66+/xksvvYRvvvkGdrsdP/3pT3HJJZeENUogIqLkEBoJGuKd4YLMBi1GphtR09KOynobjh+XpXRJREQ0yKIOQQCg0Whw6aWXxroWIiJSwHCbDgf4T5pa09KOygaGICKi4ahfIaiyshKPPvootm3bBgCYPHkyrrvuOpSUlMS0OCIiii9ZFqhrcQIYPiNBgD8Efby9EZVsjkBENCxF3Rjhtddew9SpU7Fx40ZMnz4d06dPx6ZNmzBt2jS89tpr8aixm/LycpSWlmLWrFmD8npEREPVIbsLbp8MlQTkphmULmfQBM8VxBBERDQ8RT0SdPPNN+N3v/sd/vjHP4Ztv+OOO3DzzTfjwgsvjFlxvSkrK0NZWRmsVissFp7tm4iov6oDU+Hy0gzQqqP+XCxpTQp2iGuwQQgxLM6PREREHaJ+x6urq8Pll1/ebfull16Kurq6mBRFRESDI9QZbhhNhQOA8dmp0Kgk2Jxe1LU6lS6HiIgGWdQh6JRTTsFnn33WbfvatWtx4oknxqQoIiIaHMOxKQIA6DQqjB1hAsApcUREw1HU0+EWLFiA3/72t9i4cSNOOOEEAMAXX3yBV155BUuXLsXKlSvD9iUiosQ1XEeCAP+UuJ2Ndmyvt+HUkhylyyEiokEUdQi69tprAQB///vf8fe//73HxwBAkiT4fL4BlkdERPHUMRKUonAlg68kz4y3v6lDZb1V6VKIiGiQRR2CZFmORx1ERKSA2kAIKkgfPp3hgoqDHeIa7ApXQkREg21ArYCcTi4mJSJKZsHpcIXDcDpcSV4aAGBXox0eHz/gIyIaTqIOQT6fD3fddRdGjhyJ1NRU7N69GwBw++2345///GfMCyQiovhobffA5vICAAqGWWMEwB/8UnRquH0y9h5qU7ocIiIaRFGHoLvvvhvLli3Dn//8Z+h0utD2qVOn4umnn45pcUREFD/BUaBMkw4puqhnRyc9lUrCxNyO8wUREdHwEXUIeu655/Dkk0/ikksugVqtDm2fPn06tm/fHtPiiIgofoZre+zOSoIhiG2yiYiGlahDUE1NDSZMmNBtuyzL8Hg8MSmKiIjir6bZAWB4h6BJef4QtJ0hiIhoWIk6BJWWlvZ4stRXX30VM2fOjElRREQUf7Wt/uY2w3E9UFBJIATt4HQ4IqJhJepJ4H/4wx+waNEi1NTUQJZlrFixApWVlXjuuefw9ttvx6NGIiKKg+F8otSg4kAI2t/kgMPtHZZro4iIhqOoR4LOO+88vPXWW/jwww9hMpnwhz/8Adu2bcNbb72FefPmxaNGIiKKg2quCcKIVD1GpOogBLCD5wsiIho2+vWR14knnohVq1Z12/7VV1/h2GOPHXBRREQUf8P5HEGdTcoz41DVYeyot2FGUbrS5RAR0SCIeiTIbrejvb09bNuWLVtw7rnn4vjjj49ZYUREFD9Ojw+H7C4Aw3skCACKc9kcgYhouIk4BB04cACzZ8+GxWKBxWLBkiVL4HA4cPnll+P444+HyWTCunXr4lkrERHFSG1gKlyKTo30FK3C1Sgr2ByhssGqcCVERDRYIp4O95vf/AZOpxMPP/wwVqxYgYcffhifffYZjj/+eOzatQuFhYXxrDNMeXk5ysvL4fP5Bu01iYiGktqWjs5wkiQpXI2yJuWlAQAq67kmiIhouIg4BH366adYsWIFTjjhBPz4xz9GXl4eLrnkEtxwww1xLK9nZWVlKCsrg9VqhcViGfTXJyJKdjUtPEdQ0MScVADAIbsLh+0uZKXqFa6IiIjiLeLpcA0NDRg7diwAICcnBykpKTjnnHPiVhgREcUP22N3MOk1GJWZAgCo5LogIqJhIarGCCqVKuy2TqeLeUFERBR/bI8dblJoXRBDEBHRcBDxdDghBIqLi0Nzx+12O2bOnBkWjACgqakpthUSEVHMsT12uEm5ZqyqaOBIEBHRMBFxCHrmmWfiWQcREQ2iGo4EhQmOBLFNNhHR8BBxCFq0aFE86yAiokHikwXqWzu6w1FHm+ydDTbIsoBKNbw75hERDXVRnyyViIiSW6PNCa8soFFJyE0zKF1OQhgzwgStWkKb2xcaJSMioqGLIYiIaJgJrgfKsxig5ogHAECrVmF8tr9VNqfEERENfQxBRETDDNcD9Sw4JW4HO8QREQ15DEFERMNMNc8R1KNiNkcgIho2GIKIiIaZ4EhQIUeCwgRHgirrrQpXQkRE8RZRd7glS5ZEfMAHHnig38UQEVH81QZCEDvDhZuUlwYA2H2wDW6vDJ2GnxMSEQ1VEYWgzZs3R3Sw4IlUiYgocdVwOlyPCiwGmPUa2Fxe7D5kR0kgFBER0dATUQhavXp1vOsgIqJBIIRgY4ReSJKE4jwzNu5rRmW9jSGIiGgIU3Ss/95778WsWbNgNpuRk5OD888/H5WVlUqWREQ0pLU4PHC4fQA4Ha4nk9gcgYhoWIhoJKirr776Ci+//DL2798Pt9sd9tiKFSsiPs4nn3yCsrIyzJo1C16vF7///e9x5plnoqKiAiaTqT+lERFRH4KjQCNS9TBo1QpXk3hCbbIZgoiIhrSoQ9BLL72Eyy+/HGeddRY++OADnHnmmdixYwcaGhpwwQUXRHWs9957L+z+smXLkJOTg40bN+Kkk07qtr/L5YLL5Qrdt1rZwYeIKBpsj9234lyOBBERDQdRh6B77rkHDz74IMrKymA2m/Hwww9j7NixuOaaa5Cfnz+gYlpbWwEAmZmZPT5+7733YunSpd0fWLgQ0GoH9NoxsX49sGCB0lX0D2tXBmtXxjCuvTZzKpA3FyO/2wgsuD+GhUUgCb7vJSo9UHIFalraYTv/Qphlj/+BJKi9V6xdGaxdGaxdGYlSu8cT+b4iSikpKWLPnj1CCCEyMzPFN998I4QQoqKiQuTl5UV7uBCfzyfmz58v5s6d2+s+TqdTtLa2hi4HDhwQAERra2u/Xzemzj1X6Qr6j7Urg7UrYxjX/se3vhOjf/u2+NPb38WooCgkyff9uLtXidG/fVt8tbepY2OS1N4j1q4M1q4M1q6MBKm9tbU14mwQdWOEjIwM2Gz+aQIjR47E1q1bAQAtLS1wOBzRHi6krKwMW7duxUsvvdTrPnq9HmlpaWEXIiKKXKg9Npsi9Co4Ja6SU+KIiIasqEPQSSedhFWrVgEAfvSjH2Hx4sX4+c9/josuuginn356v4q47rrr8Pbbb2P16tUoLCzs1zGIiOjIQu2xM1IUriRxBZsjVNZz3SkR0VAV9Zqgxx57DE6nEwBw6623QqvVYt26dbjwwgtx2223RXUsIQR+9atf4fXXX8eaNWswduzYaMshIqIo8BxBRzYpcH6gygaOBBERDVVRh6DOTQtUKhVuueWWfr94WVkZXnjhBbz55pswm82or68HAFgsFhiNfIMmIoolh9uLpjb/aQ3YHa53kzpNhxNCQJIkhSsiIqJYi3o6nFqtRmNjY7fthw8fhlod3TknHn/8cbS2tuKUU05Bfn5+6LJ8+fJoyyIioiOobfGP4qfqNUgz9Os0ccPCxNxUqCSg2eHBQZvryE8gIqKkE/W7oBCix+0ulws6nS4mxyIiotjrPBWOoxu9M2jVGJNlwu5DbahssCEnzaB0SUREFGMRh6BHHnkEACBJEp5++mmkpqaGHvP5fPj0009RUlIS+wqJiCgmanii1IgV55r9IajehhMnZitdDhERxVjEIejBBx8E4B+9eeKJJ8Kmvul0OowZMwZPPPFE7CskIqKYqGnxn8aATRGObFKeGe99V4/tbJNNRDQkRRyC9uzZAwA49dRTsWLFCmRkZMStKCIiij2OBEUu2CZ7BzvEERENSVGvCVq9enXodnBND+eWExElPrbHjlxxpxDkkwWia/tDRESJLurucADw3HPPYdq0aTAajTAajTjqqKPw73//O9a1ERFRDAW7wxUwBB3RmCwT9BoVnB4Z+5scSpdDREQxFvVI0AMPPIDbb78d1113HebOnQsAWLt2LX7xi1/g0KFDuPHGG2NeJBERDYzXJ6Pe6g9BhZwOd0RqlYSJuanYWmNFZb0NPJU3EdHQEnUIevTRR/H444/j8ssvD21bsGABpkyZgjvvvJMhiIgoAdVbnfDJAjq1CtmpeqXLSQrFueZQCDpb6WKIiCimop4OV1dXhzlz5nTbPmfOHNTV1cWkKCIiiq1gU4T8dANUKq7jjESwOUJlg1XhSoiIKNaiDkETJkzAyy+/3G378uXLMXHixJgURUREscWmCNGblJcGAGyTTUQ0BEU8He60007DihUrsHTpUixcuBCffvppaE3Q//73P3z00Uc9hiMiIlJecCSITREiNynXPxK091AbnJIaBoXrISKi2Il4JGjNmjVwu9248MIL8eWXX2LEiBF444038MYbb2DEiBFYv349LrjggnjWGlJeXo7S0lLMmjVrUF6PiCjZ1bZyJChauWl6WIxayAKo0qcrXQ4REcVQ1I0RAOCYY47Bf/7zn1jXErGysjKUlZXBarXCYrEoVgcRUbKo5olSoyZJEiblmbF+TxMq9ZmYqnRBREQUM1GFoIqKCtTX1/e5z1FHHTWggoiIKPaCa4IKORIUlUm5/hC0Q5+pdClERBRDUYWg008/HUKIXh+XJAk+n2/ARRERUewIIVDbwpGg/pgU6BC33cAQREQ0lEQVgr788ktkZ2fHqxYiIoqDw21uOD0yJAnIs3B5fzRCbbI5EkRENKREFYJGjRqFnJyceNVCRERxEOwMl52qh16jVria5FIcCEH12lS0OjywpGgVroiIiGIh6vMEERFRcuFUuP5LM2hREBg9q2zg+YKIiIaKiEPQySefDJ1OF89aiIgoDnii1IFZet5UvLh3JaYUpCldChERxUjE0+FWr14dzzqIiChO2B57YOaV5gKOOkDfr7NKEBFRAuJ0OCKiIY7tsYmIiMIxBBERDXE1HAkiIiIKwxBERDTEBUeCCjgSREREBIAhiIhoSLO7vGht9wBgYwQiIqKgqFd5+nw+LFu2DB999BEaGxshy3LY4x9//HHMiiMiooEJtsdOM2hgNvAcN0REREA/QtDixYuxbNkyzJ8/H1OnToUkSfGoi4iIYqBjPVCKwpUQEREljqhD0EsvvYSXX34Z3//+9+NRDxERxVA1zxFERETUTdRrgnQ6HSZMmBCPWoiIKMaCI0GF7AxHREQUEnUI+vWvf42HH34YQoh41ENERDHU0RnOoHAlREREiSPq6XBr167F6tWr8d///hdTpkyBVhu+0HbFihUxK46IiAamNjQdjmuCiIiIgqIOQenp6bjgggviUUvEysvLUV5eDp/Pp2gdRESJjidKJSIi6i7qEPTMM8/Eo46olJWVoaysDFarFRaLRelyiIgSktsro8HmBMDGCERERJ3xZKlERENUfasTQgB6jQojUnVKl0NERJQwoh4JAoBXX30VL7/8Mvbv3w+32x322KZNm2JSGBERDUx1iwOAfxSI53QjIiLqEPVI0COPPIIrr7wSubm52Lx5M4477jhkZWVh9+7dOOecc+JRIxER9UNti38qXAGnwhEREYWJOgT9/e9/x5NPPolHH30UOp0ON998M1atWoXrr78era2t8aiRiIj6IdQUgSGIiIgoTNQhaP/+/ZgzZw4AwGg0wmazAQAuu+wyvPjii7GtjoiI+q0mOB2OneGIiIjCRB2C8vLy0NTUBAAYNWoUvvjiCwDAnj17eAJVIqIEUtPCkSAiIqKeRB2CTjvtNKxcuRIAcOWVV+LGG2/EvHnzsHDhQsXPH0RERB14jiAiIqKeRd0d7sknn4QsywD85+vJysrCunXrsGDBAlxzzTUxL5CIiKInyyLUGIEjQUREROGiDkEqlQoqVccA0k9+8hP85Cc/iWlRREQ0MIfaXHD7ZKgkIM9iULocIiKihNKvk6V+9tlnuPTSSzF79mzU1NQAAP79739j7dq1MS2OiIj6JzgVLjfNAK2a58UmIiLqLOp3xtdeew1nnXUWjEYjNm/eDJfLBQBobW3FPffcE/MCiYgoemyKQERE1LuoQ9Cf/vQnPPHEE3jqqaeg1WpD2+fOnYtNmzbFtDgiIuofNkUgIiLqXdQhqLKyEieddFK37RaLBS0tLbGoiYiIBogjQURERL3r13mCqqqqum1fu3Ytxo0bF5OiiIhoYDgSRERE1LuoQ9DPf/5zLF68GF9++SUkSUJtbS2ef/553HTTTfjlL38ZjxqJiChKwZGgAo4EERERdRN1i+xbbrkFsizj9NNPh8PhwEknnQS9Xo+bbroJv/rVr+JRIxERRSkYggoZgoiIiLqJOgRJkoRbb70Vv/nNb1BVVQW73Y7S0lKkpqbGo74elZeXo7y8HD6fb9Bek4goWVidHticXgCcDkdERNSTqENQkE6nQ2lpaSxriVhZWRnKyspgtVphsVgUqYGIKFEF1wNlpGiRouv3r3kiIqIhK+J3x6uuuiqi/f71r3/1uxgiIho4NkUgIiLqW8QhaNmyZRg9ejRmzpwJIUQ8ayIiogFge2wiIqK+RRyCfvnLX+LFF1/Enj17cOWVV+LSSy9FZmZmPGsjIqJ+qGVnOCIioj5F3CK7vLwcdXV1uPnmm/HWW2+hqKgIP/7xj/H+++9zZIiIKIFUcySIiIioT1GdJ0iv1+Oiiy7CqlWrUFFRgSlTpuDaa6/FmDFjYLfb41UjERFFIbgmqJBrgoiIiHoU9clSQ09UqSBJEoQQbFVNRJRAOtYEpShcCRERUWKKKgS5XC68+OKLmDdvHoqLi/Htt9/isccew/79+wf1PEFERNQzp8eHgzYXAHaHIyIi6k3EjRGuvfZavPTSSygqKsJVV12FF198ESNGjIhnbUREFKW6VicAwKhVIyNFq3A1REREiSniEPTEE09g1KhRGDduHD755BN88sknPe63YsWKmBVHRETR6egMZ4AkSQpXQ0RElJgiDkGXX34531CJiBJcx4lSuR6IiIioN1GdLJWIiBIb22MTEREdWb+7wxERUeJhe2wiIqIjYwgiIhpCalocADgSRERE1BeGICKiISR0jiCOBBEREfWKIYiIaIjwyQL1gRbZBRwJIiIi6hVDEBHREHHQ5oLHJ6BWScg165Uuh4iIKGElZQgqLy9HaWkpZs2apXQpREQJI7geKC/NAI06KX+9ExERDYqkfJcsKytDRUUFNmzYoHQpREQJo7qZ64GIiIgikZQhiIiIugs2RSjkeiAiIqI+MQQREQ0RwXMEsSkCERFR3xiCiIiGiFq2xyYiIooIQxAR0RAROkcQR4KIiIj6xBBERDQECCFC0+E4EkRERNQ3hiAioiGgtd2DNrcPAEeCiIiIjoQhiIhoCAi2xx6RqoNBq1a4GiIiosTGEERENAQE1wOxMxwREdGRMQQREQ0BtWyKQEREFDGGICKiISDUFIEhiIiI6IgYgoiIhoAaniOIiIgoYgxBRERDAM8RREREFDmGICKiIYDnCCIiIoqcoiHo008/xbnnnouCggJIkoQ33nhDyXKIiJJSu9uHw21uABwJIiIiioSiIaitrQ3Tp09HeXm5kmUQESW12lb/KJBJp4bFqFW4GiIiosSnUfLFzznnHJxzzjkR7+9yueByuUL3rVZrPMoiIkoqnafCSZKkcDVERESJT9EQFK17770XS5cu7f7AwoWANgE+/Vy/HliwQOkq+oe1K4O1K2OI1V6TXgIUnIyRVd8BC/6mUGERGGLf96TB2pXB2pXB2pWRKLV7PBHvmlQh6He/+x2WLFkSum+1WlFUVAQsXw6kpSlYWcCCBcDKlUpX0T+sXRmsXRlDrPaa9yuB1VUYOe9E4PxrFSosAkPs+540WLsyWLsyWLsyEqV2qxWwWCLaNalCkF6vh16vV7oMIqKE0tEeO0XhSoiIiJIDW2QTESW54JqggnSDwpUQERElB4YgIqIkFxwJKuQ5goiIiCKi6HQ4u92Oqqqq0P09e/Zgy5YtyMzMxKhRoxSsjJLFl2/txrd5v0DOI1tQOCkDhSUZGFFkhkrFDlk0PHh9MuqtTgCcDkdERBQpRUPQV199hVNPPTV0P9j0YNGiRVi2bJlCVVGyqN7ehK/e2QuoDDhQ0YQDFU0AAH2KBiMnZYRCUXpuCtsG05DVYHPBJwto1RJyzFwzSUREFAlFQ9App5wCIYSSJVCScrV78dGz2wAAxY5tyFm0ANXbm1CzswUuhxe7Nx/E7s0HAQCmdD0KS/yBqHBSJlIz+IciDR3B9UD5FiNHQImIiCKUVN3hiILWvrwD9mYX0kYYcPK3H0N3ehmmn14En0/GwX02VG9vQvX2ZtTtbkVbiwuVX9Sj8ot6AEB6bkooFI0szoDBlADnmCLqp5oWBwBgZDrXAxEREUWKIYiSzp6vD2L75/WABJx+RSl0SzpOjKVWq5A3zoK8cRYc+/2x8Lh9qK9qRXWlPxQ17rehpcGBlgYHtn5SA0hAdpE5MEqUgfyJ6dDq1Ap+dUTR6egMxxBEREQUKYYgSirtNjdW/2c7AGDmGaNQMCG9z/21OjWKSjNRVJoJAHC2eVC7swXV25tRvb0JzfUOHNxvw8H9Nmz+YD9Uagl54yyhUJQzNg1qNZsoUuKqaQk0RWBnOCIioogxBFHSEEJgzQuVaLd5kFlgwnELxkZ9DINJi3EzsjFuRjYAoK3FherK5tD0OXuzC7U7W1C7swXr39oDrV6NgonpoelzWQWpkLjughJIqD02R4KIiIgixhBESWPH+gbs3nwQKpWEM64ohUY78GlrpnQ9Jh2fh0nH50EIgdbG9lAoqqlsgbPNg31bD2Pf1sMAAEOqNtR1rrAkA2kjjOw8R4qqaQ6sCeJIEBERUcQYgigp2Jud+PSlHQCAWT8Yg+xR5pi/hiRJSM9NQXpuCqaeNBJCFjhUbfdPnatsRm1VC5x2D6o2NqJqYyMAIDVTj8KSzFAwMlnYeY4GjxAiNBLExghERESRYwiihCeEwMf/3g53uxc5Y9Jw9FmjB+V1JZWE7FFmZI8yY+aZo+DzymjYaw2tJ2rYY4W9yYXt6+qwfV0dACAj3xRaTzRyUgb0Rv4To/hpanPD6ZEBAPnpBoWrISIiSh78C40S3nef1uBARRPUWhXOuGIyVAo1KlBrVCiYkI6CCek47gdj4XH5UFvV0WThULUdzXVtaK5rw7erqyFJQPbotNDUufxxFmjYeY5iKDgKlG3WQ6/hzxYREVGkGIIoobU0OvC/16oAALMvGI+MPJPCFXXQ6tUYPSULo6dkAQCcdo9/PVFgTVFrYzsa91rRuNeKTe/tg1qjQt54SygU5Ywyg33naCBqORWOiIioXxiCKGHJssBHy7bB65YxclI6jjqlUOmS+mRI1WLCMTmYcEwOAMDW5ERNZXNopKit1Y2aymbUVDbjyzcBnUGN7KwfQv3o1wpX3j+pltNxvNWNlDSd0qUMW9WBcwSxKQIREVF0kjIElZeXo7y8HD6fT+lSKI62rNqP+t2t0BrUOO3yyUnXmtqcaUDJ7HyUzM6HEAItDY5AIGpGzY5muBxe1OhHAd8dVrrU/jFNQ90Dm3DeDTNhSmdDCCWwPTYREVH/JGUIKisrQ1lZGaxWKywWi9LlUBwcqrbjy7d2AwBO/PFEpGUl9x95kiQhI8+EjDwTpp1SCFkWOHTAhqab7wZuuEHp8qImywIblm1Acz3w+t824bwbZ8KcyYX5g62GI0FERET9kpQhiIY2n1fGh8sqIHsFxhw1AiWz85UuKeZUKgk5o9OQ074NSNKvr/Cvr+CN0mvRerAdr/9tE86/cSbSRvCP8cHE9thERET9w3XZlHA2vL0Hh6vtMKRqceqlJTwZaYJK81lxwa+PhiXbCNthJ1b8dRNaGhxKlzWsBENQAUMQERFRVBiCKKHU727Fpvf3AQBOuXgSF90nOHOmARfcdDQy8lLQ1uLCir9twuFau9JlDQttLi9aHB4AnA5HREQULYYgShgetw8fPbsNQgDFx+Vi/NE5SpdEETBZ9Dh/ydHIGpmKdqsbbzywGQcP2JQua8gLtsc2GzRIM2gVroaIiCi5MARRwvj89V1oaXDAlK7HiQuLlS6HopCSpsP5S2Yie5QZTrsHbz64GQ17rUqXNaRVcz0QERFRvzEEUUI4sL0J366uBgCcdnkJDCZ+sp1sDCYtzrtxJvLGpcHl8OLNhzajrqpF6bKGrGBnuEJOhSMiIooaQxApztXuxcfPbgMATD1pJEaVZilcEfWX3qjBudfPQMHEdHicPqx89GtUVzYrXdaQxM5wRERE/ccQRIpbu3wH7M0upGUbMefCCUqXQwOkM2jwg19NR9HkDHhdPrz92NfYn6wnhE1gwZEgdoYjIiKKHkMQKWr3loPY/kU9IAFnLJoMrV6tdEkUA1qdGt+/9iiMmZYFn0fGO49/gz1fH1S6rCEl2BiBneGIiIiixxBEinFY3Vjz/HYAwNFnjkL+hHRlC6KY0mjVOPuaaRg/MxuyV+C9f2xF1cZGpcsaMjgdjoiIqP8YgkgRQgh88kIl2m0eZI004bgfjFO6JIoDtUaFM382BRNn5UKWBT54eisqv6xXuqyk5/HJaLA6AXAkiIiIqD8YgkgRO76sx+4tB6FSSzj9ilKotfxRHKpUahXOuLIUJXPyIQTw4bIKVPyvVumyklp9qxOyAHQaFUaY9EqXQ0RElHT4lycNOluTE58u3wkAmDV/LLKLzApXRPGmUkk47dISTD1pJCCA1f/ejm/XVCtdVtKqbu6YCqdSSQpXQ0RElHwYgmhQCVng4+e2wd3uRe7YNBx91iilS6JBIqkknHRRMaafXgQA+PSlHdjy4X6Fq0pOwaYIBekGhSshIiJKTgxBNKi2flqD6u3N0GhVOH3RZKjU/BEcTiRJwtz/m4Cjzx4NAPjfq1X46t29yhaVhNgUgYiIaGD4FygNmpYGB9a9VgUAmP3D8cjIMylcESlBkiSccN44HHfuWADAlyt348uVuyGEULiy5FETmg6XonAlREREyYkhiAaF7JPx0bMV8HpkjJyUgWknFypdEilIkiTMmj8Ws384HgDw1bt7sW7FLgahCNXwHEFEREQDkpQhqLy8HKWlpZg1a5bSpVCENq/aj/rdVugMapy+aDIkLuYmAEefORonLpwIANiyaj8+W74TQmYQOhJOhyMiIhqYpAxBZWVlqKiowIYNG5QuhSJwqNqG9W/tAQCcuLAY5kwu5qYOR51ahFMumQRIwLdrqrHm+e2QGYR6JaMjBBVyJIiIiKhfkjIEUfLweWR8+Mw2yD6BsdNHYNIJeUqXRAloyokj/SOEElDxvzp8/Ow2yD5Z6bIS0mG1EW6vDEkCctP4gQIREVF/MARRXK1/Zw8O19hhSNXilEtKIEmcBkc9KzkhH/N+OgWSSkLll/X44J8V8DEIdVOjTQUA5JoN0Gn4K5yIiKg/+A5KcVO/uxWb398HADjlkklISdMpXBEluonH5uLsn0+FSi1h16ZGvP/kVvg8DEKd1Wj9JxdmUwQiIqL+YwiiuPC4fPjwmQoIARQfn4vxM3OULomSxLiZ2TjnF9Og1qiw5+tDePeJb+B1+5QuK2HU6PwjQWyKQERE1H8MQRQXn6+oQuvBdpjS9ThpYbHS5VCSGTNtBOZfdxQ0WhX2f9eEt8u/gcfFIARwJIiIiCgWGIIo5g5sa8K3n9QAAE6/fDL0KVqFK6JkVFSSiXOvnwGtXo2ayma89cgWuNu9SpeluOCaoAKOBBEREfUbQxDFlMvhwcfPbQMATD15JIpKMxWuiJJZwcR0LFg8AzqjBnW7WvHmw1vgbPMoXZaigiGokCGIiGLM19oKx6ZNaHc6IXwcfaehTaN0ATS0fLZ8J+zNLliyjZjzwwlKl0NDQN44C86/cSbefHgzGvda8eZDm7Fg8QwYU4dnow1OhyOigfI2NcFVVQX3rl1wVe2Ca9cuuHZVwXfwUGgf1Zy5MB1/PExz5sA0dw50RUUKVkwUewxBFDO7Nx9E5Zf1kCTg9CtKodWrlS6JhojsUWZcsORovPnQZhw6YMcbD/iDkMmiV7q0QWVzemBV+79mNkYgor4IIeA9eLBT0KmCOxB4fM3NvT5Pk5cHubERcmsrbB98ANsHHwAAtIWF/kA0Zw5MJxwPdXr6IH0lRPHBEEQx4bC6seaF7QCAmWeORv54i8IV0VCTNTIVF/z6aLz54GY01bbhjQc247wbZiI1Y/gEoZqWdgBAeooWJj1/fRNRIOzU13cEnU6jO7LV2uvztIWF0I8fD92E8dCPnwD9hPHQjRsHdWoqxLnnwnnXXbCvW4e2devQvuVreKqr0fLyy2h5+WVAkmCYMiUQimbDePTRUOmG5+g8JS++i9KACSGw5vntaLd5kDUyFcf9YKzSJdEQlZFnwgU3HY03HtyMlgYHXv/bRpx340ykZQ2PUZGaZn8I4igQ0fAjZBme2tpu09jcVVWQHY6en6RSQVdUBN2ECdCPH+8POuPHQz92LFQpKb2+liRJMM6YAeOMGci+9lr47G1wfLUBbYFQ5K7aBefWrXBu3YrDTz4JyWBAyrHHwjR7Nkxz50BfXAxJxWXnlNgYgmjAKr+ox56vD0GllnDGlZOh1vIXH8WPJTslNCJkPeTE63/dhPNunIn0nN7f0IeK4EgQO8MRDV3C54PnwAH/Op3O09h274ZwOnt+kkYD3ejR4UFnwgToxoyBSj/w0XJ1qgnmU06B+ZRTAACehka0fe4PRG2ffw7fwUNoW7sWbWvXAn8B1FlZMJ1wQmg9kTYvb8A1EMUaQxANiK3Jic+W7wAAzPrBWIwoNCtcEQ0HaVlGXPDrY/DmQ8ERoU04/8aZyMgzKV1aXAVDEEeCiJKf8Hjg3r+/23od9549EG53j8+RtFroxo7tCDrBaWyjRkEaxOlo2twcpJ9/PtLPPx9CCLh27gwFIseGr+A7fBjWd96B9Z13AAC6sWNDgSjluOOgTk0dtFqJesMQRP0mZIGPn9sGt9OH3LFpOPrMUUqXRMNIaobePyL0kH+N0Ot/24TzbpiJrJFD9801OB2ukJ3hiJKG7HbDvWcv3LuqwjqxuffuA7w9n/tM0uuhGz/OH3I6je7oioogaRLrTzdJkmAoLoahuBhZV1wB4XbDsWUL2j7/HG3r1sH57Va49+yBe88eND//PKBWw3jUUR3riY46CpKW5xOkwZdY/5IoqXz7SQ2qtzdDo1XhjCtKoVJzGhwNrpQ0Hc5fMhMrH96CQwfseP2BTVhw/QzkjE5TurRe+WQBu9MLq9MDm9MLW+Da7vLftjq9Yds7P773cBsAjgQRDQYhBODxQHY6Ibc7IZztkJ1OiPZ2yO3tHdvanZCd7RCd92t3wlNTC/fZ58C9fz8gyz2+hpSS4g85XaaxaQsKIKmTs8OqpNPBdNxxMB13HLB4MXytrWhbvz60nsizbz/aN29G++bNOFReDpXJhJTjjgutJ9KNGwdJkpT+MmgYYAiifmlpcODzFVUAgNk/nID03KG/HoMSkzFVh/NumIm3H/saDXusePOhLTj3V9ORNy72HQo9PjksmHQEGS/swdu9hBl74Habe2AnINTJXhxVlB6bL4goSQlZ9oeOYCjpEkA6QklHOJGd7RCh6/Yu2zo/t+OYGOgJQ9v8H1yozGboJ0zoNI3Nf9Hk5w/5P/jVFgvS5s1D2rx5AAB3dU1oPZHj8y/ga2mBffVq2FevBgBocnNDo0Sm2bOhGTFCyfJpCEvKEFReXo7y8nL4eDZjRcg+GR8uq4DXI6OwJAPTTh6pdEk0zBlMWiy4fgbeLv8adVWtWPnwFvzguqNQMDEjtI/L6/MHl3YPbIZs2HYe6hhtcXUfeQm7HXjc6en509z+0GtUMBu0MBs0HRd98H6X7YaO7aOuvxqZ6efFrA7qmRACwuOBcLn8F48Horoa8PkgfL7Qdefb/msZkH0QXp//urd9fF4Inwzh8wJh177w53e6FrIPCF73epyur+UDDlQDly9S+lvaL2L/Acg//GFHUAmEk14bBMSLWg2V0QjJaIDKYITKYIBkDF533maAypgCldEA9SuvQv/n+6EbPx6a7OwhH3YipSscCd2PfoSMH/0IQpbh3LYtNErUvnETvA0NaH39dbS+/joAQD9pUmiUKOXYY6EyciScYiMpQ1BZWRnKyspgtVphsSTG+Wiann8Bzvp6qO65B2pzGlTmVKjNZqhSzVCnBa7NqVClpUGdmjqoCxhjbdMH+9GwxwqdUYPTLp8MScVf7BRfXp8Mu8sLa3v4NDJr51GZdg/sRRpkHFQjtdWHVx/YjHX5EqpUPlidHri9nQLMuB8C//yy3/Wk6NRh4SRVr0FaWHDpCC3+x7oGGy10mn5OH/UN8h9/ChE+H4TLBdnlgnC7/UHE7fbfd7kh3K6Ox4P3Oz/uckG4XZCDt10uCI877L7s7rSvywXZ4w67380Z8wb/GxEr69crXUH/VWzr82FJr+8SSvzX/tDSJagYDZAM/tvdtnV+vtEYvq0/a1Y+/BCYPbufX/TwIKlUME6ZAuOUKRjx859Ddjrh2Lgx1GTBVbENrspKuCor0bRsGSStFsaZM0NNFgylpUk7bZCUl5QhKBGte/V9jLfagOf+HdH+Xo0WHqMJXqMJPmMKZFMq5BQTYEqFlJoKKdUMtdl/0VrM0KSlQW9Jgy7dAkO6BYZ0M4x6HbRqaVA/XTpUbcOGt/cAAE5cOBHmTMOAjif7fPA42uFpa4fb4YDX0Q5PmwMeRzt8jnZ4HQ742p3wdZqD7Z/e4J/iIJxO4JANctlNkFNMkI0pna5Tu9w3QU4xQWh1QIJ8IieljYNmax20ahU0ahW0Ksl/rZYC2yRoVCrogrfVErQqFbQaFTQq/z7qOIdQIYT/D0SHw39pa4Pc5gAc7VBV7oA63QJ1enqvbVhlWaDN7e0yfcwDa3tHkLF2GX2xtnfctzo9cEQxhUwD4DyNDuO8asyuldFo8uCQtiMAmfUamO3NMBcWdAstqYYuYaaHkZlUvQaaYbr+TbjdgWlH7WHrIuR2h3/KkaM9fKpRu8P/CX57YHttHcQ1vwgPH52DSiDsyG53rwvGlSJJEmAw+M99otEErtWQVGr/H2Hq3q5VkNSaHp8Xer5GDXQ7jv95oeerVUDoutO+nZ/f47UK0l//Ctx8s9Lfwv7581+g+uMfA0HFH1jCAk/w/wkNCSqDAalz5yJ17lwAgLepyd9gIdBkwVtbB8f69XCsX4+DDz0ElcUC0/HHh6bP6UaxQRNFjiEoRrZO+x4+kUbA5HHC5G33X3v81yleJ1JDt/2fLmq8HmhsLYCtJeLX8AYuwVOiOTR6tGkNaNca0a4zwqlPgdtghFufArfRBJ8hBb4UU1jAQqoZKnMqVKlmaFNN0GvVMGjV0KVPgufLffD6BDw+GR6fgC/wB48IzKkWDic02zVQ+TSQDW14/6038MEKJySnC2q3Eyq323/tcUHjcUPjdkPjcUHjcUHrdUPrdUPvCVz73NB53dDLR/5DR0IEP6gfvRPx99EjqeHQ6uHQGODQGuDQGNDW47U+9LhDa0Bb8DqwzaeKwadPhfOA/2wa0CEkCdAGA5RKQorkg8nnQapww+TzX1J8bqTIbhi9rsDFDYPXBb3XBYPHCZ3HBb3HBZ3bCW2ni8bthNrlhKqXRb04r2NallerQ7vBhDa9CTZdCqzaFDSrjWhSG2DVpcCmTYFdl+K/Hbhv05ngUUf+a8ioVYfCSJpRGwonaQZtYLTFvz1Vq4Lt4wbYd9vwf+0GHHvBREw6Jhepeo0/NC5YAPxt5YC+74lGCNGxRsLh8IcVR3tgnUOUYcXR3j3sOJ2xCSaffBL9czQaqHQ6SHp94KLz39d1vq/v8rgeUug5Oqj0+sD+gdv6wOM6PVT6TsfWdXlcr4ek1UI67zxgZZL+zPzjH8A55yhdRf88/jhw4veUroIUosnMhGX+fFjmz4cQAu69e0OByPHFl5BbW2H74APYPvgAAKAtLISxtQWa++6HJjsbmpycwCUb2pwcqExD+zQKFB2GoBgp+/0VaLv4Mjif+SecXh+cHhkujw9Orwynx4dmjw/1Xhkulxseqx2y3QafzQ5hs0G02SG12aFqa4PKYYe6vQ2adgd0zjbonO3QuRwwutphcLcjxdMOvc8DAEjxuvyhqr21XzX7IIX+qHepddD98hLofW7ofR7ofW5oRPgfvlXjzsP+UWdC67bh+P/9CTqPfcDft85cKg1cah1cGi3cah3cGv/Fo9HBo9XBo9HDq9XBp9PBq9XDp9VD1ukhGhuhz8qA3t0Ovas9/NrthN7l8F+7nZAgoBU+WNwOWNy9nGE7Qh6NDi6dAU59Clw6A9w6I5x6I1w6I1xdrp16I9y6jtvBx9zbK6GeMB6q9naoXE5oXO3QuJxQu9o7hREXdO526Dwu6DyuUJAxBMKM0euC0eeCIXC76/+3WGpX69Cu0cOp0UGWVEh1O2D2tEMtZGg8bpg9bphtzYjmtHgerR4ekxlekxnCbAbSLFBZLNBYLNBlZsKYlQ7jiCykZmdCn5EOdXo61BbLEaeU+mYW4sN/VaBqYyO+emEnMvRaWGblDuwb0E9RLeLuc8F2O8SBasj/96Mew8qg6bw+wpgSPvXIGJheZDT6pxqlBKcVpUBa9gxUi28IBAxtp7DRNZx0elynS7iWwEQ0+CRJgn7sWOjHjkXmxRdDeL1wbt0Ke3A90Zav4amuhgcAli3r8Rgqk6lLOPIHJE22PyRpcnKgyc6GKoXNnoYDvrPEiNmghdnrAEbE/1MG2eWCs9WK9mYrXM0tcLXa4LZa4Wm1wWtthc9mg2zzBy3Y7UBbG1Rtdqja26BxtEHT3gaV7IMaAmZPO8yevv94EpIKTZnF2F90BgBg9MFVcBXko12vB/QGCL0ewmCEpDf4p4sYgnOuA/OpjUaoA38YaU3+29qUFKhTjNCZUqBNSYE2xQCtTgOtSgVVtNO7FiwAnnniiLsJWfZP57LZINvt8NntkAMXn80G2d7mD6d2u/+2zQa5zQ6fLbCP3b9P8I/N4OhWqsMaXb1dfTSwp/dG6PUQxhQIgxGywQhZb4RsNMKnN8CrN8CnM8KjN8Cj81/cOgPcWgPcOj2cWj3cGj3aNQY4NYHgo9bCI6TQSCHWf4m0k+bCrFcjU/Ig3dcOi8cJs7sNqS4HjE479O126Bx2aOxWCKsVcmsrfC0t8LW2wtfaCsgytB4XtC0uoOVQj1+HO3DpGvWllBT/VDxLese1xRIKSWqLBSeMTYdo1WJXlQer/vUdvE43Jp9YFP59CrXA7RJE+uos1W2bM/w5wcXbweue1pcMxNatfT4s6XRhoSQsrKQYA9OKeg8roe2d10akdIQdaLX9m4b7xuvAhT/s5xdNRNRB0mhgnDEDxhkzkH3ttZDb2uD46iu4brsN3gUL4G08CG9jY+jin8rdBndbG9x79/Z5bJXZ3CkshQckTeewZBjYkgBSFkNQElLp9UjJyUZKTna/nh+cNuMLhAHZaoV8002Q/vKX8IWhgWuvrMLyuzcAB9sx6YQ8nHHFYzH+igaHpFJBnZo64DNVC683EIr8oSksRLV1ut0lRIUFL5sNwuPpqE2v9/+RmZIClcnU6XYKVCmmwHWK/w9/kwlS8PHQ/qaO26YU/x+u8V4s+tofgYVl/X66kGX/9yQYjFpaO263tnS67b+Wg49brYAsQzgc8Doc8NbW9fk6oyChvfgnqC34Hj5+fidq774PRQd2Qxw7K3ZTvKLQ+yJuQyic9LmI+9FHIP3hD72ElcBzuFCYiIYZlcmE1JNPRmpGBvCb33R7XG5rg/fgQXgaG7sFJO9B/31PY6P/wyubDW6bDe7du/t+TYsFmuwR/pCUnRMWkDqPNKmSuBnWUMYQNAxJkhT6wwk5Of6NKSnAzJk97v/5i5WwHmxHaoYeJ/544iBWmpgkjcY/2pCePqDjyG43xAUXQPXmm8Nyuo+kUkGdlgZ1WhpQVHTkJwQIWYZss3ULSeEhqtUfpFr815Mb3oVK9qK68BRsH3MhXEIPs20/oJcgIAGQ/AvIdToguJZEpwW0HVOz/Le1kLQ6QKvzd4sKXWs7rjXBfbT+hfAa/3aoNf7/z5IEIQMCAhCBEzICEALd7gvh38f/dQsIALr805CTNx05Y9KgNw6/nxsiov5QmUzQmUzQjRnT6z5CCH9YCgalgx1BKRSeDh6Et6HB38SltRXu1la4q3b1+drq9PSep+Hl5EAb3D5iBBKjZdPwwXdQ6tP+isPY+kkNAOC0yydDn9KPNqHUI5VOB6jVwDAMQAMhqVSh6W6IohNQsdeLdS9vx9efNmLP2B9E/8I+AO2BS59kAM7AJQ7S5gIPbwEkIDPfhNyxacgba0Hu2DRk5Juin05KREQA/B8SB2eM6MeN63U/IQRkmy08IB082H2EqbERwuMJfFDXAtfOnX2+vkqSIM2Z658xoNP5lxd0va0PNIUx6DsavvR0O3DpuG3o1ATGEN4QZph2WORfX9QrZ5sHHz+3HQAw7eSRKJqcqXBFRP2n0mgw96IpMOdasOP5z4CJgVFNSYIkBbumS5BUwc3+7QhuD+4T3N5lW+BQ/rUynY/XdR8VIKHTPpIUfImO5yL8NlQd+7S//REaRk2H9ZATTbVtaKptw7b/+acEag1q5I5JCwtGRjOnYXQj+wBnK+BsAdpbjnjtddghl+yF7vG5gErjv6i1na6DtzX+28HHOj8e9pi60+3gY12e2+Mxuj63r9fXAl3/sJFlwOcGZA/g8wCy138/dNsTeDxwWw7c93k7nhPa3vU5nk77Hek5Pb2mp+fnTLEB//k/YEQxMGJC4LoYMGUnzKkOaPiRJCk0k0E/YUKv+wkhILe29j0F72AjvAcPAR4PZCGApqZB/Er8JK22h9DU5bZBH+rI2ePtlhYk21+JDEHUq89e3oG2FhcsOUbM/mHv/8iJkoUkSZh+ehGmP/wS8K8kbXf8n5uA534Jh9WNhj2tqN9jRcOeVjTstcHj9KF6ezOqtzeHdk/LNiJvbBpyx1qQNy4NWSNToe7viVoTSZRBxn/d7O+m6bIiNM+wE6/Qodk7Ek3eUWjyFqLJW4ombxGsvlwAKmR+tx/52m3I121DvrYCZvXBBP87XPIHohkeYGkGEMfOkXGjB1C1yn/pzGDxh6GsicCIiR3hKHOs/2smSgCSJHVMny8u7nU/Icv+ta8XXwz5kUcC51Fzdjr5szPsRNCyyxnV7Y4TS3fchq/j/HvC4/GvU7bbEflZ+bp/rQxBw9XKXwHT1gF/Hq90Jf0zrRX466TQJ4277DOx48DlkCDjjIzHoP13Q8enkmGfUqo7PoUMfRKp6bRN3Wnf4KeYvTxf3enxsE82NeHP7/wJqUoDaF2Atc7/mKTyX0K31eG3Oz7yJ0pqKWk6jJ2ejbHT/Q1SZFmgqbatIxjtbkVzvQPWg+2wHmzHjvUNAAC1VoWcUWbkdgpGqRkKdTjqV5AJXHoJMpHwCi1avCPRJCagSYxHk28Umlz5sLosEOg9IPrD0Sh8134WAMBkkpGf70Z+jhP5OQ5kWRxQodMIR9iIitd/v+soiuztsl/XbZ0ek73hoyWd9xM9/eki/KMsKgQWnfUgbESqy+iUWtdlFEsXPtoUNkIV2L/zCNWRnh/2nF5ec8m1wJIrgUM7gUM7/JeW/f6fm+oN/kvY16MBMsYGglGncJQ1AUhJtj/RaLiQVCpoMjMBna7PsBRLwuvtIRz5A9cRbzud4Se6djmB1asHpe5YYgiKFZcN0HoAR89tfhOeFoC9HgDg8Fmw5tD5AICZphXIa327e3/iRDINwAMlke8fCkTBsBS8reo9OIXtp+6yvetzuoSwsPtdHhu9HXizLDzUhcJhL/eDrxXNc7pt63rdx/MkVeTBUQj/H7bBP/Zkr/9+5z/Yuv6BJ/s67esN/2Oxr+eHjtHb84PH8HX/g3P8t8DzP+rXj5vieqldBWBE4DJFDWAi4BqrQ4MtBw3WXNTbctFgy4XLY0DdrlbU7WoFcAAAYNLZkWduQG5aA3LNDchOPQStOg5d8yZ8DfzjpI5A4+x/kAnRpgCGdMCYHrjOCN326TLQ7MpGkyMdTdZUNDVr0XRYgrXJ22sm0Js0yMw3IbMgFZn5KaHbuPwy1N/5GOqqWlC3qxUH99nQ1qZCVZUBVVUGAOnQGtTIG2dB/ngL8iekI3dsGrS6QerUJ8tdQpav4/bPfgr869meQ0iifyhkTweOvTJ8m6cdaNodCEU7O13vBDxtwOGd/ktll2OljAiEok7haMREIH2U//ce0TAiafzNemJ2AtkFC2JznEHEEBQr8+4CXjkAPPqo0pX0z6+uAx5+EMLnwerlbXAe9CErGzjukh8CqvNi80err9MfpAP+A7nTxdUOqFW9fBLaAyF3TAvp77hvrGQB2PwfhYuIQNeQJKmBaTbgvtHd/x8lAwuAnR8oXUX/RFG7HsCowAV6QOiAVl8B6j3FaHAXo95TjMPeMWhzp2LX4VTsOuwfyVbBiyzNXuTpdiBXuwO52kpY1PUD/3s5DUBdS/ft2hR/eAkLMxFea3TweWS0NDr8a6Tq2kLXrY2OLmGn4x+8PkWDzAITMvNNyMg3hW6npOl6PgeS3I5xM7IxboZ/5M3j9qFxjxV1u1pQV9WKut2t8Dh9OFDRhAMV/jn9KpWEEaPMyJ9gQcH4dOSNtyAlLU5rtFQqQKUD0MPx3QYgLT8+r6sErRHIneK/dCYEYK3tCEWHOwUka43/Q8r9h4D968Kfp9YDWeO7jxyNmAjozYP3dRHRoGIIipX0IsBpAnJLla6kf5ypQP50bF9Xh707t0GllnDGNbOgLhzYOXUGxYIFwMrA+o7gSITw+YOOHLgWPv8npaHbvi63RZfnBG/LvWz3dYSpbsc6wvbOx3rmn8Dll3UKh95O4a9r4JOPsE+X+8HXjeY5venpcS38n+ZHQuo8jfEIUyB7mvLYr337mK75yGPA4sX9+WlT3sMP97t2CUB64BIcO/V4DuLgQS3qG7VoaNChvlELh0ODg94JOOidgG/xfQCAwSAjN8eNvFwPcnPcyMn2QK+PchTnoUeAW+/qMchEwueV0dLgQNO+NjTV2tFU14Dmuja0NLZDyD3Xok/RdASdYNgp6CPsREirU2PkpAyMnJQBAJBlgcM1dtTvakVdVQtqq1rR1uJC414rGvda8fWH/lG39NyUwEiRBfnj02HJMQ6oDupEkgDLSP9l/Knhj7lswOEq4FBVx7S6Qzv923wuoLHCf+nKXNApHHWaYpc2MvFH0YioTwxBFGJrcmLtyzsAAMedOxYjkiEAdSVJ/j90k+VH+75VwIm/VrqKDt2CVh/B6boy4O9PRLaOK9H+WGh6FZh5idJV9E/T8pjWrgVQELgA/m5G9mYX6ne3oiHQdOHgfjucTmDffgP27Q+sH+pPi+7m5UDxmUesyefzh53mOkcg7PhHd1ob2yH3EnZ0BnVoNMc/lS0QdiwDCzuRUqkkZBeZkV1kxrRTCiGEgK3J6R8lCgSjpto2tDQ40NLgwLZ1/o5+RrMW+RPS/cFofDpGjEqFWj0EGlckGr0ZKJjpv3Qm+4DWA+FrjoJT69oaAVut/7Lnk/DnaU0d3eo6N2fIGu8fqaL4EwLwOLqvG8xoBL5e3ukDyE4fRIbd77K984eHR9q3x+3eCPaVuz3X6rLggH0iahwToB4zA4V3XoGi1B1I0Tk73l9D0+D7uq8JjAh3vh+cqt9lCryk6nI/0uP3dLzA/dQWRX8c+iNJ/lKkeBMAPnp2G9xOH/LGpWHmmaOVLomU0NeUmq6cJv8bPw0pkiTBnGmAOdOAicfmAgB8HhmHqu2o39MRjGLRotvnk9Ha2N59GluDo8+w03n6mv86Fab0wQk7kZIkCWlZRqRlGTHp+DwA/tMO1O9uDQSjFjTstaLd5sHuzQexe/NBAIBGp0Lu2DTkj09H/gQL8sZaoONJceNHpQYyxvgvE+eFP9beHD5ydDhwu2m3f+1R3df+SxjJv8ZoxESgaAfwwW2AzgzoUwFdauC6832z/6JLBTT6xPvAKN6EANxt0TdHCV7Lnu7HHAvg9asHofj+aZfTUO2ahmr3dFS7p8Hqywt7fLt/eTayNHswSr8FRbotyNdtg0bq4WtNJBOS72eXv1kJAPCNaQZqKpuh0alw+qJSnnCRiELUWlWgm1xaaFt/WnSnGCai+Z09obDT0uCA7Os57GgNan/I6RJ4TOn6hAo70TCYtBgzbQTGTBsBAPB6fDi4zxZqVFFX1QKXw4uayhbUVLYA8P9NnFWYGjZalJqhV/CrGEaMGUDRLP+lM58HaN4bPmp0aAdwqNLfta5ln/+SDWBdFOuEVZqOYBQKTF2CUudtR9pnsJo9xCPIREOlCZ9eu70KmD6z04hFl6ZGfW0PjZJEsG/X7Z1HUDrt6/aqUVurQfUBNar3Szh8MPz3l0oF5I7UoHCcFr73P8T+MSfhUL0Ph71jcdg7FpvbLoBGI1BQCBSN9qGoyIPMdA8kBKe8dx7F6jR1PnS/88hTp9kcnUevwqbQ93W/j+Pv2Tew/48KSMoQVF5ejvLycvg69ThXmtfjgyxpgXYvhBD+JSZCAAKh2/61+IHHZBFYsOvfHnwOBHp4fs/HQ2h74L4MiE7Hh/+/jtu9HNfr9uHztO8BAOb8cALSc1MU+z4SUXLoV4vuzPnAW3vCjqPV9zSyY0JqRvKGnUhptGp/uJmQDsD/u7q53tHRbGFXC6yHnDh0wI5DB+z4dnU1ACBthAH5gUYL+RMsyMwzQeIHV4NHre1YH9SZEEDbIX9DhoOVwJMPAheeC7jtgMve6drmv3bZ/Ns8Dv/zZW+ga2JLbOrUpvQSnMx9hyldqn9q07a3O7Wlb+4jzLTGPsj0ep3RfZvOFD6CtmAB8Lc3BlbPAPi8Mhr2WFG9vQnVlc1o2G3tNrKdNTIVhZMzUDgpAwUT06EzBP4cf/FezF52OxxWN6q3+5us7N/WBEerG/v3Avv3+qf7mywWFE3ORFFpJgpLMuPXcCUa7A43OMrKylBWVgar1QqLxaJ0OQCAD5+pwK78MuDGT5UupX8kDYomZ2DqSSOVroSIkpBKJWFEYSpGFKZiyon+3yMuhwcNe61o2GNF/W4rXJu3IuPkmcjMT0VmgQkZ+SkwZxqGfNiJlKSSQo0bgt9De7PLH4p2taJ+VysOHbDBesgJ66F6VH7pnzejT9GE2nLnj7cgZ3Qa1FquKxp0kgSkZvsvo+cAf3wdOOvuIz9P9h05KIWu7d3vd90n2MTG4/Bf2hqj/1qKASyPcu1hJEGmty6QXYNMEhGywOFaO6q3N+PAtmbUVrXA6wr/kD5thAGFkzJQODkTI4szjhhaUtJ0KD4uD8XH5UEI/wdMB7b5Q1HNzha0tbqx/Yt6bP/C/ztgRFEqRpVmomhyJvLHp/Pff4SSMgQlpj7+8Ur++eESAKgCtyUAgeuO+x23JUnq5X5gW+BTP0nyv3H6f3dIgVO6SKHfJcHHuh6v47X923QbP8f3Fl3FTxOJKGb0KVqMKs3CqNIs/4YFtwOLkrQhhUJSM/SYeGxuaH2W2+lFw24ragOjRQ17WuFyeLH328PY++1hAIBao0LOGHNoXVGW2gyt3QO1TgWNRsXf84lGpQYMFv9loIQAvK5egpKt56AV3LfzttpaYPyUYRNkotV6sD000lO9vRlOe/hImCFVi8IS/0hPYUkmLNn9b5YhSRKyRqYia2QqZpwxCl6PD3VVraFRosPV9tBo8ab390OjU6FgYkYoFGXkp/CDpl4wBMXIvCtLccbbt0N67bWOoAOEgkfCW/AHIP2XSldBRER90Bk0KCr1T4MB/M0lDh2wh07iWlfVgnabxz+drqoVeB9A7k+Bmz4LHUOtUUGjU0GjVUGtU0Oj9d/WBG/r1FBrg/sEt3Xa3mVf/3E676vuOL5WlRDvgUIW8HlleD0yfF4ZPk/gduf7gevQY96Ox0P7hvbzwecVnfb1dTumOvsymB/d4m80kmUIXBthzjTAZNHFJ4xKEqA1+C+mEf0/zoIFwH0rY1dXknNY3aipbA4FH+shZ9jjGr0aBRPSUViSgaLJGcgqSI3bhw0ardo/FW5yJuYAaGt1+UehAqGo3erG/u8OY/93/g9FUjP0nabOZcCYmgBT5xIEQ1CM+IcefQCHIImIaJCo1Sp/R74xaZhxhn+tZ2tje2gKXV1VK6z1NshSxyJ5n9f/R7prsGrsFpxUUPcQmMJCWeBaMh0N37t7AkFDBIKIL7LA0ul2bw044kqbhebvmnp8SKWWkJqh7whHYUHJgNQMA9Qa/j2hFLfTi9qdLaFGL4dr7GGPq1QScselhUZ6csemKfb/y2TRY9LxeZh0vH/q3OGaNv9Jm7cdRu3OVtibXdi2rs7fll8CsovMKCrNxKjJmcgbbxnWP2cMQUREREOEJElIz01Bem4KJs8JnP1pwQLIb7wJr9s/WuEJXHvd/pDg9fj8t90doxled2Cbx+cPEp1uR7Jv54XgwbDicvRxQubeWE4CVu458n7RkBAapdJo/NfqwHVwu1oTGOHSSP77WnXHvsH9Oj039LxOx/T89lbYFv8etiYnbIedoWt7iwuyTwTWdjl7rdGUpgsLRuZMA1I73Q4tpqcBC2tmsL0ZDXuiaGaQQCSpY23mzDNHwev2obaqJRCKmnC4pg0H99twcL8Nm97bB41ejZHF6SianIlRpZlIzx1eU+cS7/8gERERxZRKJfn/aDMAg3EqT58vGJwCISkQhDxuX7dA5fP44AlcdwQz//PEx6uhOfP08LDRLbD0EET6CCkqlTQ4f+i59gPfK+i2WfbJaGt1hwUjW1P4bZ/Hv09bqxv1u609Ht5g0vY6kmTONEBv0gyrP2ijIWSBQzX20EhPLJoZJCKNTh22LrOtxYUDga5zB7Y1od3mwb5vD2Pft52mzgXWEhWVZMKQqlWy/LhjCCIiIqKYUqtVUBtV0A00ca24Bbi0LCY1JQqVWhUKLj0RQqDd5uk2gtT52t3uhbPNA2ebBwf323o8jkav7hKQgtPv4rwuKUGFmhlsb0Z1ZXybGSQqU7oeJSfko+SE/FAQDAai2qoW/9S5/9X5T4AtATmjAlPnSrOQOy4NavXQmjrHEERERESUICRJQkqaDilpOuSOSetxH1e7t9eAZGtyot3qhtflQ3NdG5rr2no8hkot+afXdR5FCtw2arIg1bVBpZagUklQqVWdbkuQOt9O0NGmYDODA4HgYzusXDODRCSpJGQXmZFdZMbRZ42Gx+1D7c6OqXNNtW1o3GdD4z4bNv53H7R6NUZOyghNnbPkGBP2/32kGIKIiIiIkojeqIE+sPajJ163D/ZmV49T7cLWJQVOZtxNzmXA0i8jqkWS4A9FalUoGIWu1f5TenQOUpJKgjq4XS2FBa2u2yS1BLWqc+hS9XDcTvurJDSnnYTqu9YndDODRKTVqTF6ShZGT/FPnbM3u/znJgpcnHYP9n5zCHu/OQQAMGcaQlPnCksy0PO4ZmJjCCIiIiIaQjQ6dahBRk9knwx7iwv2HkeSXHDVHoScZoHsExA+AVkWvXbYEwIQXgHZ6+vx8UGXejQQCEDJ0MwgUaVm6DF5Tj4mzwlMnau2Y3/FYRzY1oS6qlbYmpyoWFuLirW1kCQgZ8RCzLe7k6oFN38aiIiIiIYRlVqFtCwj0rKMwMQedliwAHgq/DxBQgh/4PEJ+HwyRCAYBQOS7BOdtslhjwlfp31lAdknd9k//Bhhxw883tMx/LUEjhPYP+Xz1Shc/JOkbWaQiCSVhOxRZmSPMuOYs8fA4/KhZkezf5SoognN9Q5Y1WkwmJKrkQJDEBERERH1SZIkSBIAlRQ4N2KCeu824NjFSlcxpGn1aoyZNgJjpvlPyGtrcsJ69Y2QpHMVriw6DEFERERERNQv5kwDzO4apcuIWkJE+fLycowZMwYGgwHHH3881q9fr3RJREREREQ0RCkegpYvX44lS5bgjjvuwKZNmzB9+nScddZZaGxsVLo0IiIiIiIaghQPQQ888AB+/vOf48orr0RpaSmeeOIJpKSk4F//+pfSpRERERER0RCkaAhyu93YuHEjzjjjjNA2lUqFM844A59//nm3/V0uF6xWa9iFiIiIiIgoGoo2Rjh06BB8Ph9yc3PDtufm5mL79u3d9r/33nuxdOnS7gdauBDQJkBbvvXr/W0lkxFrVwZrVwZrVwZrVwZrVwZrVwZrV0ai1O7xRLxrUnWH+93vfoclS5aE7lutVhQVFQHLlwNpaQpWFrBgAbBy5ZH3S0SsXRmsXRmsXRmsXRmsXRmsXRmsXRmJUrvVClgsEe2qaAgaMWIE1Go1GhoawrY3NDQgLy+v2/56vR56vX6wyiMiIiIioiFI0TVBOp0OxxxzDD766KPQNlmW8dFHH2H27NkKVkZEREREREOV4tPhlixZgkWLFuHYY4/Fcccdh4ceeghtbW248sorlS6NiIiIiIiGIMVD0MKFC3Hw4EH84Q9/QH19PWbMmIH33nuvW7MEIiIiIiKiWFA8BAHAddddh+uuu07pMoiIiIiIaBhQ/GSpREREREREg4khiIiIiIiIhhWGICIiIiIiGlYYgoiIiIiIaFhhCCIiIiIiomGFIYiIiIiIiIaVhGiR3V9CCACA1WpVuJIAjwdIlFqixdqVwdqVwdqVwdqVwdqVwdqVwdqVkSC1BzNBMCP0RRKR7JWgqqurUVRUpHQZRERERESUIA4cOIDCwsI+90nqECTLMmpra2E2myFJUrfHZ82ahQ0bNhzxOJHsd6R9rFYrioqKcODAAaSlpcX99WJ5LNYe3T6xOpYStcfq62Ptsd8nkv1Ye+z3iWQ/1h77fSLZL9LaB/P3dqT7sfbo9mPtrD3a/XrbRwgBm82GgoICqFR9r/pJ6ulwKpWqz5SnVquP+Es/0v0iPVZaWlpMjsXaozsWax/8rw9g7bHcJ5r9WDtrj3a/oVy7Er+3WTtrj8exANYe6X597WOxWI74GsAQb4xQVlYWs/0iPdZgvx5rjxxrj+3XFynWztqjxdpZezSU+L3N2ll7PI4Vy+Ow9iNL6ulwicRqtcJisaC1tTWilJtIWLsyWLsyWLsyWLsyWLsyWLsyWLsykrX2IT0SNJj0ej3uuOMO6PV6pUuJGmtXBmtXBmtXBmtXBmtXBmtXBmtXRrLWzpEgIiIiIiIaVjgSREREREREwwpDEBERERERDSsMQURERERENKwwBBERERER0bDCEERERERERMMKQ1AvPv30U5x77rkoKCiAJEl44403jvicNWvW4Oijj4Zer8eECROwbNmybvuUl5djzJgxMBgMOP7447F+/fqkqP3ee+/FrFmzYDabkZOTg/PPPx+VlZVJUXtn9913HyRJwg033BCzmoH41V1TU4NLL70UWVlZMBqNmDZtGr766quEr93n8+H222/H2LFjYTQaMX78eNx1112IdTPKaGuvq6vDxRdfjOLiYqhUql5/Dl555RWUlJTAYDBg2rRpePfdd2Nad7xqf+qpp3DiiSciIyMDGRkZOOOMMxLid0yk3/egl156CZIk4fzzz49ZzUHxqr2lpQVlZWXIz8+HXq9HcXFxzH9u4lX7Qw89hEmTJsFoNKKoqAg33ngjnE6norWvWLEC8+bNQ3Z2NtLS0jB79my8//773fZLxPfUSGpP1PfUSL/vQfF6TwXiV3sivq9GUnuivq+uXbsWc+fODX0/S0pK8OCDD3bbbzD+rUaLIagXbW1tmD59OsrLyyPaf8+ePZg/fz5OPfVUbNmyBTfccAN+9rOfhf0QL1++HEuWLMEdd9yBTZs2Yfr06TjrrLPQ2NiY8LV/8sknKCsrwxdffIFVq1bB4/HgzDPPRFtbW8LXHrRhwwb84x//wFFHHRXTmoH41N3c3Iy5c+dCq9Xiv//9LyoqKvC3v/0NGRkZCV/7/fffj8cffxyPPfYYtm3bhvvvvx9//vOf8eijjypau8vlQnZ2Nm677TZMnz69x33WrVuHiy66CD/96U+xefNmnH/++Tj//POxdevWWJYel9rXrFmDiy66CKtXr8bnn3+OoqIinHnmmaipqYll6XGpPWjv3r246aabcOKJJ8ai1G7iUbvb7ca8efOwd+9evPrqq6isrMRTTz2FkSNHxrL0uNT+wgsv4JZbbsEdd9yBbdu24Z///CeWL1+O3//+97EsPeraP/30U8ybNw/vvvsuNm7ciFNPPRXnnnsuNm/eHNonUd9TI6k9Ud9TI6k9KJ7vqUB8ak/U99VIak/U91WTyYTrrrsOn376KbZt24bbbrsNt912G5588snQPoP1bzVqgo4IgHj99df73Ofmm28WU6ZMCdu2cOFCcdZZZ4XuH3fccaKsrCx03+fziYKCAnHvvffGtN7OYlV7V42NjQKA+OSTT2JRZo9iWbvNZhMTJ04Uq1atEieffLJYvHhxjKvtEKu6f/vb34rvfe978SixV7Gqff78+eKqq64K2+eHP/yhuOSSS2JWa1eR1N5Zbz8HP/7xj8X8+fPDth1//PHimmuuGWCFvYtV7V15vV5hNpvFs88+2//ijiCWtXu9XjFnzhzx9NNPi0WLFonzzjsvJjX2Jla1P/7442LcuHHC7XbHrrgjiFXtZWVl4rTTTgvbtmTJEjF37twBVti7aGsPKi0tFUuXLg3dT9T31J50rb2rRHlP7UlPtQ/me6oQsas9Ud9Xe9K19mR4Xw264IILxKWXXhq6r8S/1UhwJChGPv/8c5xxxhlh28466yx8/vnnAPyfFG7cuDFsH5VKhTPOOCO0j1KOVHtPWltbAQCZmZlxre1IIq29rKwM8+fP77avUiKpe+XKlTj22GPxox/9CDk5OZg5cyaeeuqpwS61m0hqnzNnDj766CPs2LEDAPD1119j7dq1OOeccwa11v7oz7+HROVwOODxeBT/dxqpP/7xj8jJycFPf/pTpUuJysqVKzF79myUlZUhNzcXU6dOxT333AOfz6d0aUc0Z84cbNy4MTQ1Zffu3Xj33Xfx/e9/X+HKwsmyDJvNFvpZTuT31K661t6TRHlP7aq32hPtPbUnPdWeqO+rXfVUe7K8r27evBnr1q3DySefDCCx/61qFH31IaS+vh65ublh23Jzc2G1WtHe3o7m5mb4fL4e99m+fftgltrNkWo3Go1hj8myjBtuuAFz587F1KlTB7PUbiKp/aWXXsKmTZuwYcMGharsLpK6d+/ejccffxxLlizB73//e2zYsAHXX389dDodFi1apFDlkdV+yy23wGq1oqSkBGq1Gj6fD3fffTcuueQShaqOXG9fX319vUIV9d9vf/tbFBQUJPQfKkFr167FP//5T2zZskXpUqK2e/dufPzxx7jkkkvw7rvvoqqqCtdeey08Hg/uuOMOpcvr08UXX4xDhw7he9/7HoQQ8Hq9+MUvfhHz6XAD9de//hV2ux0//vGPAQCHDh1K2PfUrrrW3lUivad21VPtifie2pOeak/U99Wueqo90d9XCwsLcfDgQXi9Xtx555342c9+BiCx/60yBFHUysrKsHXrVqxdu1bpUo7owIEDWLx4MVatWgWDwaB0OVGRZRnHHnss7rnnHgDAzJkzsXXrVjzxxBMJ9cu6Jy+//DKef/55vPDCC5gyZUpo7VBBQUHC1z5U3HfffXjppZewZs2ahP/Zt9lsuOyyy/DUU09hxIgRSpcTNVmWkZOTgyeffBJqtRrHHHMMampq8Je//CXhQ9CaNWtwzz334O9//zuOP/54VFVVYfHixbjrrrtw++23K10eAP+6paVLl+LNN99ETk6O0uVEJZLaE/U9tafak+U9tbfvezK8r/ZWe6K/r3722Wew2+344osvcMstt2DChAm46KKLlC6rTwxBMZKXl4eGhoawbQ0NDUhLS4PRaIRarYZare5xn7y8vMEstZsj1d7Zddddh7fffhuffvopCgsLB7PMHh2p9o0bN6KxsRFHH3106HGfz4dPP/0Ujz32GFwuF9Rq9WCXHdH3PD8/H6WlpWH7TJ48Ga+99tqg1dmTSGr/zW9+g1tuuQU/+clPAADTpk3Dvn37cO+99ybEL+u+9Pb1Kf3vNBp//etfcd999+HDDz+M26LlWNq1axf27t2Lc889N7RNlmUAgEajQWVlJcaPH69UeUeUn58PrVYb9rtk8uTJqK+vh9vthk6nU7C6vt1+++247LLLQp/aTps2DW1tbbj66qtx6623QqVSdtb8Sy+9hJ/97Gd45ZVXwkY0R4wYkbDvqUG91d5Zor2nBvVWe6K+p3bW1/c9Ud9Xg/qqPdHfV8eOHQvAX1dDQwPuvPNOXHTRRQn9b5VrgmJk9uzZ+Oijj8K2rVq1CrNnzwYA6HQ6HHPMMWH7yLKMjz76KLSPUo5UOwAIIXDdddfh9ddfx8cffxz6YVfakWo//fTT8e2332LLli2hy7HHHotLLrkEW7ZsUeyXdSTf87lz53Zrmbpjxw6MHj16UGrsTSS1OxyObn88qdXq0B+2iSySry+R/fnPf8Zdd92F9957D8cee6zS5USkpKSk27/TBQsWhDoQFhUVKV1in+bOnYuqqqqwn+8dO3YgPz8/oQMQ0Pu/VQAxb70brRdffBFXXnklXnzxRcyfPz/ssUR+TwX6rh1I3PdUoO/aE/U9NehI3/dEfV8Fjlx7Mr2vyrIMl8sFIMH/rSraliGB2Ww2sXnzZrF582YBQDzwwANi8+bNYt++fUIIIW655RZx2WWXhfbfvXu3SElJEb/5zW/Etm3bRHl5uVCr1eK9994L7fPSSy8JvV4vli1bJioqKsTVV18t0tPTRX19fcLX/stf/lJYLBaxZs0aUVdXF7o4HI6Er72reHSyiUfd69evFxqNRtx9991i586d4vnnnxcpKSniP//5T8LXvmjRIjFy5Ejx9ttviz179ogVK1aIESNGiJtvvlnR2oUQof2POeYYcfHFF4vNmzeL7777LvT4//73P6HRaMRf//pXsW3bNnHHHXcIrVYrvv3224Sv/b777hM6nU68+uqrYf9ObTZbwtfeVby6w8Wj9v379wuz2Syuu+46UVlZKd5++22Rk5Mj/vSnPyV87XfccYcwm83ixRdfFLt37xYffPCBGD9+vPjxj3+saO3PP/+80Gg0ory8POxnuaWlJbRPor6nRlJ7or6nRlJ7V/HqDheP2hP1fTWS2hP1ffWxxx4TK1euFDt27BA7duwQTz/9tDCbzeLWW28N7TNY/1ajxRDUi9WrVwsA3S6LFi0SQvh/GE8++eRuz5kxY4bQ6XRi3Lhx4plnnul23EcffVSMGjVK6HQ6cdxxx4kvvvgiKWrv6XgAevwaE632ruLxCztedb/11lti6tSpQq/Xi5KSEvHkk0/GtO541W61WsXixYvFqFGjhMFgEOPGjRO33nqrcLlcitfe0/6jR48O2+fll18WxcXFQqfTiSlTpoh33nknpnXHq/bRo0f3uM8dd9yR8LV3Fa8QFK/a161bJ44//nih1+vFuHHjxN133y28Xm/C1+7xeMSdd94pxo8fLwwGgygqKhLXXnutaG5uVrT2k08+uc/9gxLxPTWS2hP1PTXS73tn8QpB8ao9Ed9XI6k9Ud9XH3nkETFlyhSRkpIi0tLSxMyZM8Xf//534fP5wo47GP9WoyUJofB4NxERERER0SDimiAiIiIiIhpWGIKIiIiIiGhYYQgiIiIiIqJhhSGIiIiIiIiGFYYgIiIiIiIaVhiCiIiIiIhoWGEIIiIiIiKiYYUhiIhoCFq2bBnS09OPuJ8kSXjjjTfiXk8iOOWUU3DDDTcoXQYRESUAhiAion644oorIEkSJEmCVqvF2LFjcfPNN8PpdA56LWPGjMFDDz0Utm3hwoXYsWNH6P6dd96JGTNmdHtuXV0dzjnnnLjWt2zZstD3SqVSobCwEFdeeSUaGxvj+rpH0tP3rT86/yzodDpMmDABf/zjH+H1egdepEKGUzgmouFJo3QBRETJ6uyzz8YzzzwDj8eDjRs3YtGiRZAkCffff7/SpcFoNMJoNB5xv7y8vEGoBkhLS0NlZSVkWcbXX3+NK6+8ErW1tXj//fcH5fXjLfiz4HK58O6776KsrAxarRa/+93voj6Wz+cLBcZk5/F4oNVqlS6DiKib5P8NS0SkEL1ej7y8PBQVFeH888/HGWecgVWrVoUel2UZ9957L8aOHQuj0Yjp06fj1VdfDT2+Zs0aSJKEd955B0cddRQMBgNOOOEEbN26Nex11q5dixNPPBFGoxFFRUW4/vrr0dbWBsA/xWvfvn248cYbQ6MRQPh0uGXLlmHp0qX4+uuvQ/ssW7YMQPdP/L/99lucdtppMBqNyMrKwtVXXw273R56/IorrsD555+Pv/71r8jPz0dWVhbKysrg8Xj6/F5JkoS8vDwUFBTgnHPOwfXXX48PP/wQ7e3tAICnn34akydPhsFgQElJCf7+97+Hnrt3715IkoQVK1bg1FNPRUpKCqZPn47PP/88tM/hw4dx0UUXYeTIkUhJScG0adPw4osv9lpPT9+3trY2pKWlhf0/AoA33ngDJpMJNput1+MFfxZGjx6NX/7ylzjjjDOwcuVKAMADDzyAadOmwWQyoaioCNdee23Y9zT4/2rlypUoLS2FXq/H/v37sWHDBsybNw8jRoyAxWLBySefjE2bNnX7vv7jH//AD37wA6SkpGDy5Mn4/PPPUVVVhVNOOQUmkwlz5szBrl27wp735ptv4uijj4bBYMC4ceOwdOnS0MjVmDFjAAAXXHABJEkK3T/S84L1PP7441iwYAFMJhPuvvvuXr9nRERKYggiIoqBrVu3Yt26ddDpdKFt9957L5577jk88cQT+O6773DjjTfi0ksvxSeffBL23N/85jf429/+hg0bNiA7OxvnnntuKFTs2rULZ599Ni688EJ88803WL58OdauXYvrrrsOALBixQoUFhbij3/8I+rq6lBXV9ettoULF+LXv/41pkyZEtpn4cKF3fZra2vDWWedhYyMDGzYsAGvvPIKPvzww9BrBa1evRq7du3C6tWr8eyzz2LZsmWhUBUpo9EIWZbh9Xrx/PPP4w9/+APuvvtubNu2Dffccw9uv/12PPvss2HPufXWW3HTTTdhy5YtKC4uxkUXXRT6A9zpdOKYY47BO++8g61bt+Lqq6/GZZddhvXr1/f4+j1930wmE37yk5/gmWeeCdv3mWeewf/93//BbDZH9fW53W4AgEqlwiOPPILvvvsOzz77LD7++GPcfPPNYfs7HA7cf//9ePrpp/Hdd98hJycHNpsNixYtwtq1a/HFF19g4sSJ+P73v98tjN111124/PLLsWXLFpSUlODiiy/GNddcg9/97nf46quvIIQI+3/42Wef4fLLL8fixYtRUVGBf/zjH1i2bFkosGzYsCH0ddfV1YXuH+l5QXfeeScuuOACfPvtt7jqqqsi/p4REQ0qQUREUVu0aJFQq9XCZDIJvV4vAAiVSiVeffVVIYQQTqdTpKSkiHXr1oU976c//am46KKLhBBCrF69WgAQL730Uujxw4cPC6PRKJYvXx7a/+qrrw47xmeffSZUKpVob28XQggxevRo8eCDD4bt88wzzwiLxRK6f8cdd4jp06d3+zoAiNdff10IIcSTTz4pMjIyhN1uDz3+zjvvCJVKJerr60Nf9+jRo4XX6w3t86Mf/UgsXLiw1+9V11p27NghiouLxbHHHiuEEGL8+PHihRdeCHvOXXfdJWbPni2EEGLPnj0CgHj66adDj3/33XcCgNi2bVuvrzt//nzx61//OnT/5JNPFosXLw7d7+n79uWXXwq1Wi1qa2uFEEI0NDQIjUYj1qxZ0+vrLFq0SJx33nlCCCFkWRarVq0Ser1e3HTTTT3u/8orr4isrKzQ/WeeeUYAEFu2bOn1NYQQwufzCbPZLN56663QNgDitttuC93//PPPBQDxz3/+M7TtxRdfFAaDIXT/9NNPF/fcc0/Ysf/973+L/Pz8sOMGfy6ifd4NN9zQ59dBRJQIuCaIiKifTj31VDz++ONoa2vDgw8+CI1GgwsvvBAAUFVVBYfDgXnz5oU9x+12Y+bMmWHbZs+eHbqdmZmJSZMmYdu2bQCAr7/+Gt988w2ef/750D5CCMiyjD179mDy5Mkx+3q2bduG6dOnw2QyhbbNnTsXsiyjsrISubm5AIApU6ZArVaH9snPz8e3337b57FbW1uRmpoKWZbhdDrxve99D08//TTa2tqwa9cu/PSnP8XPf/7z0P5erxcWiyXsGEcddVTYawJAY2MjSkpK4PP5cM899+Dll/+/vTuPqzH9/wf+OpXqtJeislS0R6QsSco2ZU8zmIQQw5C9sSshGWTft8JkLGPJMiJNJwlhSD5aT0qGUpQlLeqc6/dHv+6vo+0wM2J6Px+P83g4930t7+s+i/vddd/XOYYnT57g3bt3KCsrg5KS0kcdgy5dusDKygoHDhzAggUL8Msvv8DAwAA9e/ass965c+egoqKC8vJyiMVijBo1CsuWLQMAXL58GUFBQUhJScHr169RUVGB0tJSFBcXc/HJy8tLjA8Anj17hiVLlkAgECAvLw8ikQjFxcXIzs6u9bhUvUbt27eX2FZaWorXr19DTU0N9+7dQ1xcnMQMjkgkqhbTh6StZ2dnV+exIoSQLwElQYQQ8omUlZVhbGwMANi/fz86dOiAffv2wdvbm7vn4/z582jRooVEPQUFBan7KCoqwuTJkzFjxoxq+1q3bv03ov90H97ozuPxIBaL66yjqqqKO3fuQEZGBnp6etyiDc+ePQMA7NmzB127dpWo836i9WG/Vfc+VfW7du1abNq0CRs3buTuv5k1axZ3SdrHmDhxIrZt24YFCxYgJCQE48eP5/qrTVVCLC8vD319fcjJVf73mpWVhUGDBuHHH39EYGAgtLS0cPXqVXh7e+Pdu3dc4sDn86v14eXlhRcvXmDTpk0wMDCAgoIC7O3tq42ppuNS17EqKipCQEAA3N3dq41DUVGx1jFKW+/9JJoQQr5UlAQRQsg/QEZGBosWLcKcOXMwatQoiRvcnZyc6qx748YNLqEpLCxEWloaN8PTqVMnJCUlcclWTeTl5SESiersQ5oyFhYWCA0Nxdu3b7kT2bi4OMjIyMDMzKzOuvWRkZGpcQzNmzeHvr4+Hj58CE9Pz09uPy4uDkOHDsXo0aMBVJ7wp6WlwdLSstY6tR2T0aNHY968edi8eTOSkpLg5eVVb//vJ8Tv+/PPPyEWixEcHMyt9nbs2DGpx7R9+3YMGDAAAPD48WM8f/5cqrp16dSpE1JTU+t8TzVp0qTasZGmHiGEfC1oYQRCCPmHDB8+HLKysti2bRtUVVXh6+uL2bNn48CBA8jIyMCdO3ewZcuWajf8L1++HFFRUfjf//6HcePGQVtbG25ubgCA+fPn49q1a/Dx8UFCQgLS09MRHh4ucaO7oaEhrly5gidPntR6kmxoaIjMzEwkJCTg+fPnKCsrq1bG09MTioqK8PLywv/+9z9ER0dj+vTpGDNmDHeZ1b8hICAAQUFB2Lx5M9LS0nD//n2EhIRg/fr1UrdhYmKCyMhIXLt2DcnJyZg8eTI3y1Sb2o6bpqYm3N3d8dNPP+Gbb75By5YtP3lsxsbGKC8vx5YtW/Dw4UMcOnQIO3fulHpMhw4dQnJyMuLj4+Hp6SnVsuf18fPzw8GDBxEQEIAHDx4gOTkZR44cwZIlS7gyhoaGiIqKQm5uLgoLC6WuRwghXwtKgggh5B8iJycHHx8frFmzBm/fvsWKFSuwdOlSBAUFwcLCAq6urjh//jyMjIwk6q1evRozZ86Era0tcnNzcfbsWW6VOWtra8TExCAtLQ2Ojo6wsbGBn58f9PX1ufrLly9HVlYW2rZtCx0dnRpj+/bbb+Hq6opevXpBR0enxuWjlZSUcPHiRRQUFKBz58747rvv0KdPH2zduvUfPErVTZw4EXv37kVISAjat28PJycnhIaGVjtOdVmyZAk6deoEFxcXODs7Q1dXl0ska1PXcau6XO3vrm7WoUMHrF+/Hj///DPatWuHsLAwBAUFSVV33759KCwsRKdOnTBmzBjMmDEDzZo1+1vxAICLiwvOnTuHS5cuoXPnzujWrRs2bNgAAwMDrkxwcDAiIyPRqlUr7h42aeoRQsjXgscYYw0dBCGENEYCgQC9evVCYWEh95s+5Mtw6NAhzJ49G0+fPpVY9pwQQsh/A90TRAghhPx/xcXFyMnJwerVqzF58mRKgAgh5D+KLocjhBBC/r81a9bA3Nwcurq6WLhwYUOHQwgh5F9Cl8MRQgghhBBCGhWaCSKEEEIIIYQ0KpQEEUIIIYQQQhoVSoIIIYQQQgghjQolQYQQQgghhJBGhZIgQgghhBBCSKNCSRAhhBBCCCGkUaEkiBBCCCGEENKoUBJECCGEEEIIaVQoCSKEEEIIIYQ0KpQEEUIIIYQQQhoVSoIIIYQQQgghjQolQYQQQgghhJBGhZIgQgghhBBCSKNCSRAhhBBCCCGkUaEkiBBCCCGEENKoUBJECCGEEEIIaVQoCSKEEEIIIYQ0KpQEEUIIIYQQQhoVSoIIIYQQQgghjQolQYQQQgghhJBGhZIgQgghhBBCSKNCSRAhhBBCCCGkUaEkiBBCCCGEENKoyDV0AISQL4tIJEJ5eXlDh0EIIY1CkyZNICsr29BhENLoUBJECAEAMMaQm5uLly9fNnQohBDSqGhoaEBXVxc8Hq+hQyGk0aAkiBACAFwC1KxZMygpKdF/xoQQ8i9jjKG4uBh5eXkAAD09vQaOiJDGg5IgQghEIhGXADVt2rShwyGEkEaDz+cDAPLy8tCsWTO6NI6Qz4QWRiCEcPcAKSkpNXAkhBDS+FR999L9mIR8PpQEEUI4dAkcIYR8fvTdS8jnR0kQIYQQQgghpFGhJIgQQgghhBDSqFASRAghhBBCCGlUKAkihPwnPH78GBMmTIC+vj7k5eVhYGCAmTNn4sWLFw0dGh49egQ+n4+ioiIAQEFBAWbNmgUDAwPIy8tDX18fEyZMQHZ2doPGmZWVBW9vbxgZGYHP56Nt27bw9/fHu3fv6qw3btw48Hi8ag8rKyuuTFBQEDp37gxVVVU0a9YMbm5uSE1NlWjH0NCQqysrKwt9fX14e3ujsLCwzv5DQ0OhoaHxyeOuaTxubm7/WHv14fF4OH369GfrjxBCCCVBhJD/gIcPH8LOzg7p6en49ddfIRQKsXPnTkRFRcHe3h4FBQUNGl94eDh69eoFFRUVFBQUoFu3brh8+TJ27twJoVCII0eOQCgUonPnznj48GGDxZmSkgKxWIxdu3bhwYMH2LBhA3bu3IlFixbVWW/Tpk3IycnhHo8fP4aWlhaGDx/OlYmJicG0adNw48YNREZGory8HN988w3evn0r0dby5cuRk5OD7OxshIWF4cqVK5gxY8a/Mt6/i1byIoSQrxgjhDR6JSUlLCkpiZWUlHDbxGIxe1tW3iAPsVj8UfG7urqyli1bsuLiYontOTk5TElJiU2ZMoVt2bKFWVlZcftOnTrFALAdO3Zw2/r06cMWL17MPT99+jSzsbFhCgoKzMjIiC1btoyVl5dz+wGwPXv2MDc3N8bn85mxsTELDw+vFl/v3r25fqZMmcKUlZVZTk6ORJni4mLWokUL5urqyhhj7OzZs0xdXZ1VVFQwxhi7e/cuA8Dmz5/P1fH29maenp7c89jYWNajRw+mqKjIWrZsyaZPn86Kioq4/QYGBiwwMJCNHz+eqaiosFatWrFdu3bVeWzXrFnDjIyM6izzoVOnTjEej8eysrJqLZOXl8cAsJiYGIn4NmzYIFFuxYoVzNLSss7+QkJCmLq6Ovfc39+fdejQgR08eJAZGBgwNTU1NnLkSPb69WuuzPHjx1m7du2YoqIi09LSYn369GFFRUXM39+fAZB4REdHs8zMTAaAHTlyhPXs2ZMpKCiwkJAQrq/3bdiwgRkYGEhs27dvH7O0tGTy8vJMV1eXTZs2jRvz+319WI80DjV9BxNC/l30Y6mEkBqVlItg6XexQfpOWu4CJXnpvp4KCgpw8eJFBAYGcj86WEVXVxeenp44evQoYmJiMGPGDOTn50NHRwcxMTHQ1taGQCDAlClTUF5ejuvXr2PBggUAgNjYWIwdOxabN2+Go6MjMjIy8MMPPwAA/P39uT4CAgKwZs0arF27Flu2bIGnpycePXoELS0tAMDLly9x9epVHDp0CGKxGEeOHIGnpyd0dXUlYuXz+Zg6dSqWLFmCgoICODo64s2bN7h79y7s7Owk4q0SExOD+fPnAwAyMjLg6uqKlStXYv/+/cjPz4ePjw98fHwQEhLC1QkODsaKFSuwaNEi/Pbbb/jxxx/h5OQEMzOzGo/vq1evuLFIa9++fejbty8MDAxqLfPq1SsAqLPtJ0+e4OzZs+jatetH9Q9UHo/Tp0/j3LlzKCwsxIgRI7B69WoEBgYiJycHHh4eWLNmDYYNG4Y3b94gNjYWjDH4+voiOTkZr1+/5o6blpYWnj59CgBYsGABgoODYWNjA0VFRezataveWHbs2IE5c+Zg9erV6N+/P169eoW4uDgAwK1bt9CsWTOEhITA1dWVfiiTEEI+E7ocjhDyVUtPTwdjDBYWFjXut7CwQGFhIZo1awYtLS3ExMQAAAQCAebOncs9v3nzJsrLy9G9e3cAlcnNggUL4OXlhTZt2qBfv35YsWJFtZPecePGwcPDA8bGxli1ahWKiopw8+ZNbv/vv/8Oa2tr6OvrIz8/Hy9fvqwzVsYYhEIh1NXV0bFjRy7pEQgEmD17Nu7evYuioiI8efIEQqEQTk5OACrvufH09MSsWbNgYmKC7t27Y/PmzTh48CBKS0u5PgYMGICpU6fC2NgY8+fPh7a2NqKjo2uMRygUYsuWLZg8eXJ9LwPn6dOnuHDhAiZOnFhrGbFYjFmzZsHBwQHt2rWT2Dd//nyoqKiAz+ejZcuW4PF4WL9+vdT9v99HaGgo2rVrB0dHR4wZMwZRUVEAgJycHFRUVMDd3R2GhoZo3749pk6dChUVFa5vBQUF6OrqQldXF/Ly8ly7s2bNgru7O4yMjKCnpydVLCtXrsTcuXMxc+ZMmJqaonPnzpg1axYAQEdHBwCgoaEBXV1d7jkhhJB/F80EEUJqxG8ii6TlLg3W98dijNW5X0FBAT179oRAIEDfvn2RlJSEqVOnYs2aNUhJSUFMTAw6d+7M/XL7vXv3EBcXh8DAQK4NkUiE0tJSFBcXc+Wsra25/crKylBTU0NeXh63LTw8HEOGDPmoWKtOup2cnLhkLTY2FkFBQTh27BiuXr2KgoIC6Ovrw8TEhIs3MTERYWFhEv2IxWJkZmZyidf78fJ4POjq6krEW+XJkydwdXXF8OHDMWnSJG67iooK9+/Ro0dj586dEvUOHDgADQ2NOhcWmDZtGv73v//h6tWr1fb99NNPGDduHBhjePz4MRYtWoSBAwfiypUrkJWVrbf/KoaGhlBVVeWe6+npcePs0KED+vTpg/bt28PFxQXffPMNvvvuO2hqatYacxU7O7t6y7wvLy8PT58+RZ8+fT6qHiGEkH8XJUGEkBrxeDypL0lrSMbGxuDxeEhOTsawYcOq7U9OToaOjg40NDTg7OyM3bt3IzY2FjY2NlBTU+MSo5iYGG5WBQCKiooQEBAAd3f3am0qKipy/27SpInEPh6PB7FYDAB49+4dIiIiuIUFquJITk6ucSzJycmQk5ODkZERAMDZ2Rn79+/HvXv30KRJE5ibm8PZ2RkCgQCFhYXV4p08eXKNiwi0bt1aqnirPH36FL169UL37t2xe/duiX0JCQncv9XU1CT2Mcawf/9+jBkzRmL25H0+Pj44d+4crly5gpYtW1bbr62tDWNjYwCAiYkJNm7cCHt7e0RHR6Nv37519v++usYpKyuLyMhIXLt2DZcuXcKWLVuwePFixMfHc8e+NsrKyhLPZWRkqiW17y+Y8OElmoQQQr4MdDkcIeSr1rRpU/Tr1w/bt29HSUmJxL7c3FyEhYVh3LhxACpnVpKSknD8+HE4OzsDqEw0Ll++jLi4OG4bAHTq1AmpqakwNjau9pCRke6rUyAQQFNTEx06dABQecI8YsQIHD58GLm5uRJlS0pKsH37dgwbNgzq6uoAwN0XtGHDBi7hqUqCBAJBtXiTkpJqjLe2hKQmT548gbOzM2xtbRESElJtrO+326xZM4l9MTExEAqF8Pb2rtYuYww+Pj44deoU/vjjj3qTjSpV98hUvbZ19f8xeDweHBwcEBAQgLt370JeXh6nTp0CUDkTJxKJpGpHR0cHubm5EonQ+4maqqoqDA0NuUvxatKkSROp+yOEEPLPoCSIEPLV27p1K8rKyuDi4oIrV67g8ePHiIiIQL9+/WBqago/Pz8AlZeCaWpq4vDhwxJJ0OnTp1FWVgYHBweuTT8/Pxw8eBABAQF48OABkpOTceTIESxZskTquM6cOVPtUrjAwEDo6uqiX79+uHDhAh4/fowrV67AxcUFMjIy2LRpE1dWU1MT1tbWCAsL4+Lt2bMn7ty5g7S0NImZoPnz5+PatWvw8fFBQkIC0tPTER4eDh8fH6njrUqAWrdujXXr1iE/Px+5ubnVErba7Nu3D127dq12nw9QeQncL7/8gsOHD0NVVZVr98PE9c2bN8jNzUVOTg5u3ryJn376CTo6Oty9Wv+E+Ph4rFq1Crdv30Z2djZOnjyJ/Px87pJBQ0NDJCYmIjU1Fc+fP69zKWxnZ2fk5+djzZo1yMjIwLZt23DhwgWJMsuWLUNwcDA2b96M9PR03LlzB1u2bOH2VyVJubm59f4mEiGEkH8GJUGEkK+eiYkJbt26hTZt2mDEiBEwMDBA//79YWpqiri4OO4+Eh6PB0dHR/B4PPTo0QNAZWKkpqYGOzs7iUudXFxccO7cOVy6dAmdO3dGt27dsGHDhjpXPPtQTUmQtrY2bty4gV69emHy5MkwMjKCk5MTRCIREhISqt1sX7WvKgnS0tKCpaUldHV1JVZ0s7a2RkxMDNLS0uDo6AgbGxv4+flBX19f6ngjIyMhFAoRFRWFli1bQk9Pj3vU59WrVzhx4kSNs0BA5Qppr169grOzs0S7R48elSjn5+cHPT096OvrY9CgQVBWVsalS5fQtGlTqcdRHzU1NVy5cgUDBgyAqakplixZguDgYPTv3x8AMGnSJJiZmcHOzg46OjrcSm41sbCwwPbt27Ft2zZ06NABN2/ehK+vr0QZLy8vbNy4Edu3b4eVlRUGDRqE9PR0bn9wcDAiIyPRqlUr2NjY/GPjJIQQUjseq+8OXULIf15paSkyMzNhZGQkcb/L18zf3x/r169HZGQkunXr9tn7v3PnDnr37o38/Pxq96d8aN++fZg6dSqOHj1a54IChJD/pv/idzAhX7ov/65nQgj5BAEBATA0NMSNGzfQpUsXqe/j+adUVFRgy5Yt9SZAAODt7Q0tLS0kJyfDxcWFbqYnhBBC/mU0E0QIob9CEkJIA6LvYEI+P7oniBBCCCGEENKoUBJECCGEEEIIaVQoCSKEEEIIIYQ0KpQEEUIIIYQQQhoVSoIIIYQQQgghjQolQYQQQgghhJBGhZIgQgghhBBCSKNCSRAhhBBCCCGkUaEkiBDyn/D48WNMmDAB+vr6kJeXh4GBAWbOnIkXL140dGh49OgR+Hw+ioqKAAAFBQWYNWsWDAwMIC8vD319fUyYMAHZ2dkNGmdWVha8vb1hZGQEPp+Ptm3bwt/fH+/evauz3rhx48Dj8ao9rKysai3TtGlTuLq6IjExsd6YeDweEhIS/okhIjQ0FBoaGv9IW9JwdnbGrFmzPlt/hBBCpENJECHkq/fw4UPY2dkhPT0dv/76K4RCIXbu3ImoqCjY29ujoKCgQeMLDw9Hr169oKKigoKCAnTr1g2XL1/Gzp07IRQKceTIEQiFQnTu3BkPHz5ssDhTUlIgFouxa9cuPHjwABs2bMDOnTuxaNGiOutt2rQJOTk53OPx48fQ0tLC8OHDJcq5urpyZaKioiAnJ4dBgwb9m0P6ZPUlfoQQQr5yjBDS6JWUlLCkpCRWUlLyfxvFYsbKihrmIRZ/VPyurq6sZcuWrLi4WGJ7Tk4OU1JSYlOmTGFbtmxhVlZW3L5Tp04xAGzHjh3ctj59+rDFixdzz0+fPs1sbGyYgoICMzIyYsuWLWPl5eXcfgBsz549zM3NjfH5fGZsbMzCw8Orxde7d2+unylTpjBlZWWWk5MjUaa4uJi1aNGCubq6MsYYO3v2LFNXV2cVFRWMMcbu3r3LALD58+dzdby9vZmnpyf3PDY2lvXo0YMpKiqyli1bsunTp7OioiJuv4GBAQsMDGTjx49nKioqrFWrVmzXrl11Hts1a9YwIyOjOst86NSpU4zH47GsrCxum5eXFxs6dKhEudjYWAaA5eXl1dpWZmYmA8Du3r3LGGMsOjqaAWCXL19mtra2jM/nM3t7e5aSksLVSUhIYM7OzkxFRYWpqqqyTp06sVu3bnF133/4+/tzx2b58uVszJgxTFVVlXl5eXHlCwsLubarXofMzExu29WrV5mTkxPj8/lMQ0ODffPNN6ygoIB5eXlV6+/9eoRUqfE7mBDyr5L7/GkXIeSrUF4MrNJvmL4XPQXklaUqWlBQgIsXLyIwMBB8Pl9in66uLjw9PXH06FHExMRgxowZyM/Ph46ODmJiYqCtrQ2BQIApU6agvLwc169fx4IFCwAAsbGxGDt2LDZv3gxHR0dkZGTghx9+AAD4+/tzfQQEBGDNmjVYu3YttmzZAk9PTzx69AhaWloAgJcvX+Lq1as4dOgQxGIxjhw5Ak9PT+jq6krEyufzMXXqVCxZsgQFBQVwdHTEmzdvcPfuXdjZ2UnEWyUmJgbz588HAGRkZMDV1RUrV67E/v37kZ+fDx8fH/j4+CAkJISrExwcjBUrVmDRokX47bff8OOPP8LJyQlmZmY1Ht9Xr15xY5HWvn370LdvXxgYGNRapqioCL/88guMjY3RtGnTj2ofABYvXozg4GDo6OhgypQpmDBhAuLi4gAAnp6esLGxwY4dOyArK4uEhAQ0adIE3bt3x8aNG+Hn54fU1FQAgIqKCtfmunXr4Ofnx72+jx8/rjeOhIQE9OnTBxMmTMCmTZsgJyeH6OhoiEQibNq0CWlpaWjXrh2WL18OANDR0fnosRJCCPnn0eVwhJCvWnp6OhhjsLCwqHG/hYUFCgsL0axZM2hpaSEmJgYAIBAIMHfuXO75zZs3UV5eju7duwOoTG4WLFgALy8vtGnTBv369cOKFSuwa9cuifbHjRsHDw8PGBsbY9WqVSgqKsLNmze5/b///jusra2hr6+P/Px8vHz5ss5YGWMQCoVQV1dHx44duaRHIBBg9uzZuHv3LoqKivDkyRMIhUI4OTkBAIKCguDp6YlZs2bBxMQE3bt3x+bNm3Hw4EGUlpZyfQwYMABTp06FsbEx5s+fD21tbURHR9cYj1AoxJYtWzB58uT6XgbO06dPceHCBUycOLHavnPnzkFFRQUqKipQVVXFmTNncPToUcjIfPx/RYGBgXBycoKlpSUWLFiAa9eucePMzs5G3759YW5uDhMTEwwfPhwdOnSAvLw81NXVwePxoKurC11dXYkkqHfv3pg7dy7atm2Ltm3bShXHmjVrYGdnh+3bt6NDhw6wsrKCj48PtLW1oa6uDnl5eSgpKXH9ycrKfvRYCSGE/PNoJogQUrMmSpUzMg3V90dijNW5X0FBAT179oRAIEDfvn2RlJSEqVOnYs2aNUhJSUFMTAw6d+4MJaXKvu/du4e4uDgEBgZybYhEIpSWlqK4uJgrZ21tze1XVlaGmpoa8vLyuG3h4eEYMmTIR8UqLy8PAHBycuKStdjYWAQFBeHYsWO4evUqCgoKoK+vDxMTEy7exMREhIWFSfQjFouRmZnJJV7vx1uVDLwfb5UnT57A1dUVw4cPx6RJk7jt7ycNo0ePxs6dOyXqHThwABoaGnBzc6vWZq9evbBjxw4AQGFhIbZv347+/fvj5s2bMDAwQP/+/REbGwsAMDAwwIMHD2o9Ru+PQ09PDwCQl5eH1q1bY86cOZg4cSIOHTqEvn37Yvjw4VIlNXZ2dvWW+VBCQkK1e58IIYR8+SgJIoTUjMeT+pK0hmRsbAwej4fk5GQMGzas2v7k5GTo6OhAQ0MDzs7O2L17N2JjY2FjYwM1NTUuMYqJieFmVYDKy7UCAgLg7u5erU1FRUXu302aNJHYx+PxIBaLAVTeXB8REcEtLFAVR3Jyco1jSU5OhpycHIyMjABUriy2f/9+3Lt3D02aNIG5uTmcnZ0hEAhQWFhYLd7JkydjxowZ1dpt3bq1VPFWefr0KXr16oXu3btj9+7dEvveX6VNTU1NYh9jDPv378eYMWO4RO59ysrKMDY25p7v3bsX6urq2LNnD1auXIm9e/eipKSkxjg/9P5+Ho8HANw4li1bhlGjRuH8+fO4cOEC/P39ceTIkRrfHx/G976qGar3k9by8nKJMh9egkkIIeTrQJfDEUK+ak2bNkW/fv2wfft27gS6Sm5uLsLCwjBu3DgAlTMrSUlJOH78OJydnQFUJhqXL19GXFwctw0AOnXqhNTUVBgbG1d7SHv5lkAggKamJjp06ACg8qR6xIgROHz4MHJzcyXKlpSUYPv27Rg2bBjU1dUBgLsvaMOGDVzCU5UECQSCavEmJSXVGG9NCUltnjx5AmdnZ9ja2iIkJKTaWN9vt1mzZhL7YmJiIBQK4e3tLVVfPB4PMjIy3OvWokULru267ieShqmpKWbPno1Lly7B3d2duy9KXl4eIpFIqjaq7t/Jycnhtn24VLe1tTWioqJqbeNj+iOEEPL5UBJECPnqbd26FWVlZXBxccGVK1fw+PFjREREoF+/fjA1NYWfnx+AyhNWTU1NHD58WCIJOn36NMrKyuDg4MC16efnh4MHDyIgIAAPHjxAcnIyjhw5giVLlkgd15kzZ6pdChcYGAhdXV3069cPFy5cwOPHj3HlyhW4uLhARkYGmzZt4spqamrC2toaYWFhXLw9e/bEnTt3kJaWJjETNH/+fFy7dg0+Pj5ISEhAeno6wsPD4ePjI3W8VQlQ69atsW7dOuTn5yM3N7dawlabffv2oWvXrmjXrl2N+8vKyrj2kpOTMX36dBQVFWHw4MFSx1ifkpIS+Pj4QCAQ4NGjR4iLi8OtW7e4ywENDQ1RVFSEqKgoPH/+HMXFxbW2ZWxsjFatWmHZsmVIT0/H+fPnERwcLFFm4cKFuHXrFqZOnYrExESkpKRgx44deP78OddffHw8srKy8Pz582qzboQQQhoGJUGEkK+eiYkJbt26hTZt2mDEiBHc/SWmpqaIi4vj7mPh8XhwdHQEj8dDjx49AFQmRmpqarCzs5O4HMrFxQXnzp3DpUuX0LlzZ3Tr1g0bNmz4qBmKmpIgbW1t3LhxA7169cLkyZNhZGQEJycniEQiJCQkcPe3VKnaV5UEaWlpwdLSErq6uhIrullbWyMmJgZpaWlwdHSEjY0N/Pz8oK8v/Qp/kZGREAqFiIqKQsuWLaGnp8c96vPq1SucOHGizlmgiIgIrr2uXbvi1q1bErNy/wRZWVm8ePECY8eOhampKUaMGIH+/fsjICAAANC9e3dMmTIFI0eOhI6ODtasWVNrW02aNMGvv/6KlJQUWFtb4+eff8bKlSslypiamuLSpUu4d+8eunTpAnt7e4SHh0NOrvJqc19fX8jKysLS0hI6OjoN/oO4hBBCKvFYfXfoEkL+80pLS5GZmQkjIyOJ+12+Zv7+/li/fj0iIyPRrVu3z97/nTt30Lt3b+Tn59d7f8u+ffswdepUHD16tMYFBQgh/23/xe9gQr50tDACIeQ/KSAgAIaGhrhx4wa6dOnyScsw/x0VFRXYsmVLvQkQAHh7e0NLSwvJyclwcXGhm+0JIYSQfxnNBBFC6K+QhBDSgOg7mJDPj+4JIoQQQgghhDQqlAQRQgghhBBCGhVKggghhBBCCCGNCiVBhBBCCCGEkEaFkiBCCCGEEEJIo0JJECGEEEIIIaRRoSSIEPJVc3Z2xqxZsxo6jFoZGhpi48aNX027/yQej4fTp083dBj/imXLlqFjx47/aJtZWVng8XhISEj4R9slhBBSHSVBhJCv2smTJ7FixQqpyn7tJ5kHDhxAjx49AAC3bt3CDz/8IHVdgUAAHo+Hly9f/kvR/Tv27NkDR0dHaGpqQlNTE3379sXNmzelrj9u3DjweDyJh6ura511QkNDq9WpeuTl5f3dIf3rvvQ/DBBCyJdArqEDIISQv0NLS6tB+i0vL0eTJk0+a5/h4eEYMmQIAEBHR+ez9l2FMQaRSAQ5uc/z34dAIICHhwe6d+8ORUVF/Pzzz/jmm2/w4MEDtGjRQqo2XF1dERISwj1XUFCos/zIkSOrJUrjxo1DaWkpmjVr9vGDIIQQ8sWhmSBCyFft/b96GxoaYtWqVZgwYQJUVVXRunVr7N69mytrZGQEALCxsQGPx4OzszO3b+/evbCwsICioiLMzc2xfft2bl/VDNLRo0fh5OQERUVFhIWFYdy4cXBzc8O6deugp6eHpk2bYtq0aSgvL681Xh6Ph127dmHQoEFQUlKChYUFrl+/DqFQCGdnZygrK6N79+7IyMiQqFdaWopLly5xSdCHl8PxeDzs3bsXw4YNg5KSEkxMTHDmzBku/l69egEANDU1wePxMG7cOACAWCxGUFAQjIyMwOfz0aFDB/z2229cu1UzSBcuXICtrS0UFBRw9epVODs7Y8aMGZg3bx60tLSgq6uLZcuW1TruqmN47NgxODo6gs/no3PnzkhLS8OtW7dgZ2cHFRUV9O/fH/n5+Vy9sLAwTJ06FR07doS5uTn27t0LsViMqKioWvv6kIKCAnR1dbmHpqZmneX5fL5EeVlZWfzxxx/w9vauVnbXrl1o1aoVlJSUMGLECLx69arOtsViMdasWQNjY2MoKCigdevWCAwMlCjz8OFD9OrVC0pKSujQoQOuX7/O7Xvx4gU8PDzQokULKCkpoX379vj111+5/ePGjUNMTAw2bdrEzV5lZWVJcZQIIaSRYYSQRq+kpIQlJSWxkpISbptYLGZv371tkIdYLJY6dicnJzZz5kzGGGMGBgZMS0uLbdu2jaWnp7OgoCAmIyPDUlJSGGOM3bx5kwFgly9fZjk5OezFixeMMcZ++eUXpqenx06cOMEePnzITpw4wbS0tFhoaChjjLHMzEwGgBkaGnJlnj59yry8vJiamhqbMmUKS05OZmfPnmVKSkps9+7dXHwGBgZsw4YN3HMArEWLFuzo0aMsNTWVubm5MUNDQ9a7d28WERHBkpKSWLdu3Zirq6vEOM+dO8dMTU3rbLdly5bs8OHDLD09nc2YMYOpqKiwFy9esIqKCnbixAkGgKWmprKcnBz28uVLxhhjK1euZObm5iwiIoJlZGSwkJAQpqCgwAQCAWOMsejoaAaAWVtbs0uXLjGhUMhevHjBnJycmJqaGlu2bBlLS0tjBw4cYDwej126dEkiplOnTkkcw6q+qsZpa2vLnJ2d2dWrV9mdO3eYsbExmzJlSq2v9+vXr5mioiI7e/asNG8P5uXlxdTV1ZmOjg4zNTVlU6ZMYc+fP5eqbpV169YxdXV1VlxczG3z9/dnysrKrHfv3uzu3bssJiaGGRsbs1GjRtXZ1rx585impiYLDQ1lQqGQxcbGsj179jDGJI/RuXPnWGpqKvvuu++YgYEBKy8vZ4wx9tdff7G1a9eyu3fvsoyMDLZ582YmKyvL4uPjGWOMvXz5ktnb27NJkyaxnJwclpOTwyoqKj5qvOTzq+k7mBDy76LL4QghNSqpKEHXw10bpO/4UfFQaqL0SXUHDBiAqVOnAgDmz5+PDRs2IDo6GmZmZtwlZE2bNoWuri5Xx9/fH8HBwXB3dwdQOWOUlJSEXbt2wcvLiys3a9YsrkwVTU1NbN26FbKysjA3N8fAgQMRFRWFSZMm1Rrj+PHjMWLECC5Ge3t7LF26FC4uLgCAmTNnYvz48RJ13r8Urjbjxo2Dh4cHAGDVqlXYvHkzbt68CVdXV+6ywWbNmkFDQwMAUFZWhlWrVuHy5cuwt7cHALRp0wZXr17Frl274OTkxLW9fPly9OvXT6I/a2tr+Pv7AwBMTEywdetWREVFVSv3Pl9fX4lxenh4ICoqCg4ODgAAb29vhIaG1lp//vz50NfXR9++fes8FlVcXV3h7u4OIyMjZGRkYNGiRejfvz+uX78OWVlZqdrYt28fRo0aBT6fL7G9tLQUBw8e5C7L27JlCwYOHIjg4GCJ91eVN2/eYNOmTdi6dSv3vmrbti13n1cVX19fDBw4EAAQEBAAKysrCIVCmJubo0WLFvD19eXKTp8+HRcvXsSxY8fQpUsXqKurQ15eHkpKSjXGQAghpBIlQYSQ/xRra2vu3zweD7q6unXezP727VtkZGTA29tbInGpqKiAurq6RFk7O7tq9a2srCROpvX09HD//n2pY2zevDkAoH379hLbSktL8fr1a6ipqYExhrNnz+LYsWNSt6usrAw1NbU6xy4UClFcXFwtaXn37h1sbGwkttU09vf7AyrHXt/CAdKMvbY2Vq9ejSNHjkAgEEBRUbHOfqp8//333L/bt28Pa2trtG3bFgKBAH369EH//v0RGxsLADAwMMCDBw8k6l+/fh3Jyck4dOhQtbZbt24tcV+Svb09xGIxUlNTkZ6ejv79+3P7du3aBRMTE5SVlaFPnz51xvz+MdLT0wMA5OXlwdzcHCKRCKtWrcKxY8fw5MkTvHv3DmVlZVBS+rQ/GhBCSGNFSRAhpEZ8OT7iR8U3WN+f6sPFCng8HsRica3li4qKAFSuQta1q+TM14czBcrKyn+7vw/r8Hi8WrdVtXPz5k1UVFSge/fuUrcrTSxVYz9//ny1RQY+XDzgc469pjbWrVuH1atX4/Lly9WSr4/Rpk0baGtrQygUok+fPti7dy9KSkpqHA9Qea9Yx44dYWtr+1H92NnZSaxC2Lx5c6nvzanrvbB27Vps2rQJGzduRPv27aGsrIxZs2bh3bt3HxUfIYQ0dpQEEUJqxOPxPvmStC+VvLw8AEAkEnHbmjdvDn19fTx8+BCenp4NFVqdwsPDMXDgQKkv36pJTWO3tLSEgoICsrOzJS59+9KsWbMGgYGBuHjxYo0zUh/jr7/+wosXL7gZlrpWmCsqKsKxY8cQFBRU4/7s7Gw8ffoU+vr6AIAbN25ARkYGZmZm4PP5MDY2lihvYmICPp+PqKgoTJw48ZPij4uLw9ChQzF69GgAlclRWloaLC0tuTLy8vISrzMhhJDqKAkihDQazZo1A5/PR0REBFq2bAlFRUWoq6sjICAAM2bMgLq6OlxdXVFWVobbt2+jsLAQc+bMaeiwcebMGSxfvvxvtWFgYAAej4dz585hwIAB4PP5UFVVha+vL2bPng2xWIwePXrg1atXiIuLg5qamsT9UA3l559/hp+fHw4fPgxDQ0Pk5uYCAFRUVKCiolJn3aKiIgQEBODbb7+Frq4uMjIyMG/ePBgbG3P3JdXl6NGjqKio4BKODykqKsLLywvr1q3D69evMWPGDIwYMaLWe3EUFRUxf/58zJs3D/Ly8nBwcEB+fj4ePHhQ48pzNTExMcFvv/2Ga9euQVNTE+vXr8ezZ88kkiBDQ0PEx8cjKysLKioq0NLSgowMLQZLCCHvo29FQkijIScnh82bN2PXrl3Q19fH0KFDAQATJ07E3r17ERISgvbt28PJyQmhoaHcktoNKSMjA0KhUKqT9rq0aNECAQEBWLBgAZo3bw4fHx8AwIoVK7B06VIEBQXBwsICrq6uOH/+/BcxdgDYsWMH3r17h++++w56enrcY926dfXWlZWVRWJiIoYMGQJTU1N4e3vD1tYWsbGx9f5WEFC5IIK7uzu3kMSHjI2N4e7ujgEDBuCbb76BtbW1xNLqNVm6dCnmzp0LPz8/WFhYYOTIkR/1A6xLlixBp06d4OLiAmdnZ+jq6sLNzU2ijK+vL2RlZWFpaQkdHR1kZ2dL3T4hhDQWPMYYa+ggCCENq7S0FJmZmTAyMpL6hnPyeaxfvx6XL1/G77//3tChEEL+JfQdTMjnRzNBhBDyBWvZsiUWLlzY0GEQQggh/yl0TxAhhHzBqn5PiFQXGxsrsQz1h6pWvyOEEEI+REkQIYSQr9KHy1ATQggh0qIkiBBCyFeppmWoCSGEEGnQPUGEEEIIIYSQRoWSIEIIIYQQQkijQkkQIYQQQgghpFGhJIgQQgghhBDSqFASRAghhBBCCGlUKAkihPxnjRs3Dm5ubg0ag0AgAI/Hw8uXL2sts2zZMnTs2PGzxdSQnJ2dMWvWrIYOo9HJysoCj8erc0nx0NBQaGhofLaY/m08Hg+nT59u6DAIIV8oSoIIIaSB+fr6IioqqqHD+GQlJSVQVlaGUCj8qk6khwwZgtatW0NRURF6enoYM2YMnj59Wmed3bt3w9nZGWpqavUmt++7desW+vTpAw0NDWhqasLFxQX37t3j9lcly1UPPp8PKysr7N69W6r2hUIhxo8fj5YtW0JBQQFGRkbw8PDA7du3paoPACNHjkRaWprU5RtSbm4upk+fjjZt2kBBQQGtWrXC4MGD//HP0ZfwhxRCyL+DkiBCCGlgKioqaNq0aUOH8ckiIyNhYGDw1f1mT69evXDs2DGkpqbixIkTyMjIwHfffVdnneLiYri6umLRokVS91NUVARXV1e0bt0a8fHxuHr1KlRVVeHi4oLy8nKJsqmpqcjJyUFSUhImT56MH3/8sd4T+9u3b8PW1hZpaWnYtWsXkpKScOrUKZibm2Pu3LlSx8nn89GsWTOpyzeUrKws2Nra4o8//sDatWtx//59REREoFevXpg2bVpDh0cI+VowQkijV1JSwpKSklhJSUlDh/JJjh8/ztq1a8cUFRWZlpYW69OnDysqKmJeXl5s6NChbO3atUxXV5dpaWmxqVOnsnfv3nF1S0tL2dy5c5m+vj5TUlJiXbp0YdHR0dz+kJAQpq6uziIiIpi5uTlTVlZmLi4u7OnTp1wZANUeBgYGjDHGoqOjGQB2+fJlZmtry/h8PrO3t2cpKSlcfX9/f9ahQ4dax3f27Fmmrq7OKioqGGOM3b17lwFg8+fP58p4e3szT09Pxhhjz58/Z99//z3T19dnfD6ftWvXjh0+fFiiTScnJzZ9+nT2008/MU1NTda8eXPm7+8vUSY5OZk5ODgwBQUFZmFhwSIjIxkAdurUKYlyEyZM4GKpOl61cXJyYjNnzuSeHzx4kNna2jIVFRXWvHlz5uHhwZ49e8btrzp+ERERrGPHjkxRUZH16tWLPXv2jP3+++/M3NycqaqqMg8PD/b27Vuu3oULF5iDgwNTV1dnWlpabODAgUwoFNYaF2OMhYeHMx6PJ/H+qE1VXIWFhfWWvXXrFgPAsrOzuW2JiYkMAEtPT6+zvbZt27I1a9bU2rZYLGZWVlbM1taWiUSiavur2svMzGQA2IkTJ5izszPj8/nM2tqaXbt2jSv74WtX9b48ePAgMzAwYGpqamzkyJHs9evXXBmRSMRWrVrFDA0NmaKiIrO2tmbHjx/n9hcUFLBRo0YxbW1tpqioyIyNjdn+/fu5/dnZ2Wz48OFMXV2daWpqsiFDhrDMzMw6j2f//v1ZixYtWFFRUa3jZazyc7lnzx7m5ubG+Hw+MzY2ZuHh4dz+iooKNmHCBC52U1NTtnHjRonxf/i5fv+74Z/0tX8HE/I1opkgQkiNGGMQFxc3yIMxJnWcOTk58PDwwIQJE5CcnAyBQAB3d3eujejoaGRkZCA6OhoHDhxAaGgoQkNDufo+Pj64fv06jhw5gsTERAwfPhyurq5IT0/nyhQXF2PdunU4dOgQrly5guzsbPj6+krEUPUQCoUwNjZGz549JeJcvHgxgoODcfv2bcjJyWHChAlSj9HR0RFv3rzB3bt3AQAxMTHQ1taGQCDgysTExMDZ2RkAUFpaCltbW5w/fx7/+9//8MMPP2DMmDG4efOmRLsHDhyAsrIy4uPjsWbNGixfvhyRkZEAAJFIBDc3NygpKSE+Ph67d+/G4sWLq8UmFotx7tw5DB06VOrxvK+8vBwrVqzAvXv3cPr0aWRlZWHcuHHVyi1btgxbt27FtWvX8PjxY4wYMQIbN27E4cOHcf78eVy6dAlbtmzhyr99+xZz5szB7du3ERUVBRkZGQwbNgxisbjGOAoKChAWFobu3bujSZMmnzSW2piZmaFp06bYt28f3r17h5KSEuzbtw8WFhYwNDSssQ5jDBEREcjOzkbXrl1rbTshIQEPHjzA3LlzISNT/b/0Dy9NXLx4MXx9fZGQkABTU1N4eHigoqKi1vYzMjJw+vRpnDt3DufOnUNMTAxWr17N7Q8KCsLBgwexc+dOPHjwALNnz8bo0aMRExMDAFi6dCmSkpJw4cIFJCcnY8eOHdDW1gZQ+dq7uLhAVVUVsbGxiIuLg4qKClxdXfHu3bsa4ykoKEBERASmTZsGZWXlescbEBCAESNGIDExEQMGDICnpycKCgoAVL53W7ZsiePHjyMpKQl+fn5YtGgRjh07BqDyMtURI0bA1dWV+3x379691mNFCPnKNGwORgj5EtT0V0jR27csycy8QR6i9/6iX58///yTAWBZWVnV9nl5eTEDAwNuBoUxxoYPH85GjhzJGGPs0aNHTFZWlj158kSiXp8+fdjChQsZY5V/HQcgMYuwbds21rx582r9icViNmzYMGZra8uKi4sZY5IzQVXOnz/PAHDHu76ZIMYY69SpE1u7di1jjDE3NzcWGBjI5OXl2Zs3b9hff/3FALC0tLRa6w8cOJDNnTuXe+7k5MR69OghUaZz587cjM6FCxeYnJwcy8nJ4fbXNBMUFxfHmjVrxs1CfOxM0IeqZk3evHnDGKv5+AUFBTEALCMjg9s2efJk5uLiUmu7+fn5DAC7f/++xPZ58+YxJSUlBoB169aNPX/+vNY23vcxM0GMMXb//n3Wtm1bJiMjw2RkZJiZmZnEe7aqPWVlZaasrMzk5OSYjIwMW7lyZZ3tHj16lAFgd+7cqbNc1UzQ3r17uW0PHjxgAFhycjJjrOaZICUlJYmZn59++ol17dqVMVY5i6qkpCQxm8RY5aykh4cHY4yxwYMHs/Hjx9cY06FDh5iZmRkTi8XctrKyMsbn89nFixdrrBMfH88AsJMnT9Y5XsYqZ4KWLFnCPS8qKmIA2IULF2qtM23aNPbtt99yz6tmk/9tNBNEyOdHM0GEkK9ahw4d0KdPH7Rv3x7Dhw/Hnj17UFhYyO23srKCrKws91xPTw95eXkAgPv370MkEsHU1BQqKircIyYmBhkZGVwdJSUltG3btsY23rdo0SJcv34d4eHh4PP5Evusra0l6gOosY3Y2FiJWMLCwgAATk5OEAgEYIwhNjYW7u7usLCwwNWrVxETEwN9fX2YmJgAqJzFWbFiBdq3bw8tLS2oqKjg4sWLyM7OrjWmD8eVmpqKVq1aQVdXl9vfpUuXavGGh4dj0KBBNc5CSOPPP//E4MGD0bp1a6iqqsLJyQkA6oy1efPmUFJSQps2bSS2vX8809PT4eHhgTZt2kBNTY2bcfmw3Z9++gl3797FpUuXICsri7Fjx37UTOSH+vfvz712VlZWACoXjvD29oaDgwNu3LiBuLg4tGvXDgMHDkRJSYlE/djYWCQkJCAhIQF79+7FqlWrsGPHDgBAWFiYxHsjNjb2o2OV9n1YxdDQEKqqqhJ1qsoLhUIUFxejX79+EnEdPHiQ+/z8+OOPOHLkCDp27Ih58+bh2rVrXFv37t2DUCiEqqoqV1dLSwulpaXIyMio8bPwd8arrKwMNTU1ifFu27YNtra20NHRgYqKCnbv3l3tPUII+W+Sa+gACCFfJh6fD7M7fzZY39KSlZVFZGQkrl27xl0StXjxYsTHxwNAtUubeDwed0lUUVERZGVl8eeff0okSkDlYgVVamrjw5OxX375BRs2bIBAIECLFi2qxfl+GzweDwBqvDTLzs5OYhnj5s2bA6hcWnr//v24d+8emjRpAnNzczg7O0MgEKCwsJBLHgBg7dq12LRpEzZu3Ij27dtDWVkZs2bNqnaJUV3HRlpnzpyRuDzqY7x9+xYuLi5wcXFBWFgYdHR0kJ2dDRcXlzpj5fF49cY+ePBgGBgYYM+ePdDX14dYLEa7du2qtautrQ1tbW2YmprCwsICrVq1wo0bN2Bvb/9JY9q7dy+X2FTFePjwYWRlZeH69etcsnj48GFoamoiPDwc33//PVffyMiIu6TLysoK8fHxCAwMxI8//oghQ4ZIXBrXokULpKSkAABSUlJgY2NTb3zSvg9rKl9V5/3PDwCcP3++2nteQUEBQGVS+OjRI/z++++IjIxEnz59MG3aNKxbtw5FRUWwtbXlEv336ejoQF5evtpnoby8HDwejxv3x4z3w/iPHDkCX19fBAcHw97eHqqqqli7di333UEI+W+jJIgQUiMejweeklJDhyEVHo8HBwcHODg4wM/PDwYGBjh16lS99WxsbCASiZCXlwdHR8dP7v/69euYOHEidu3ahW7dun1yO0DlCl01rbJWdV/Qhg0buITH2dkZq1evRmFhocQqYHFxcRg6dChGjx4NoPIkNy0tDZaWllLHYWZmhsePH+PZs2dcInbr1i2JMunp6Xj06BH69ev30eMEKk/cX7x4gdWrV6NVq1YA8FFLOtfmxYsXSE1NxZ49e7jX9erVq/XWqzo5Lisr++S+a0qAi4uLISMjwyUdALjn9SWdsrKyXFKlqqoqMSsDAB07doSlpSWCg4MxcuTIajNyL1++/NeWLLe0tISCggKys7MlkvAP6ejowMvLC15eXnB0dMRPP/2EdevWoVOnTjh69CiaNWsGNTW1GuvW9FlwcXHBtm3bMGPGjGr3BX3MeOPi4tC9e3dMnTqV2/b+DDAAyMvLQyQSSdUeIeTrQpfDEUK+avHx8Vi1ahVu376N7OxsnDx5Evn5+bCwsKi3rqmpKTw9PTF27FicPHkSmZmZuHnzJoKCgnD+/Hmp+s/NzcWwYcPw/fffw8XFBbm5ucjNzUV+fv7fHZoETU1NWFtbIywsjFsAoWfPnrhz5w7S0tIkTkJNTEy42bHk5GRMnjwZz549+6j++vXrh7Zt28LLywuJiYmIi4vDkiVLAPzfDEJ4eDj69u0LpQ+SZZFIxF3SVfVITk6u1kfr1q0hLy+PLVu24OHDhzhz5gxWrFjxUXHWRFNTE02bNsXu3bshFArxxx9/YM6cORJl4uPjsXXrViQkJODRo0f4448/4OHhgbZt23KzQE+ePIG5ubnEghK5ublISEiAUCgEUHlJZUJCAnezfU369euHwsJCTJs2DcnJyXjw4AHGjx8POTk59OrVS6JsXl4ecnNz8ejRIxw/fhyHDh2qc9EJHo+HkJAQpKWlwdHREb///jsePnyIxMREBAYGfvKCFdJQVVWFr68vZs+ejQMHDiAjIwN37tzBli1bcODAAQCAn58fwsPDIRQK8eDBA5w7d477bHp6ekJbWxtDhw5FbGwsMjMzIRAIMGPGDPz111+19rtt2zaIRCJ06dIFJ06cQHp6OpKTk7F58+aPmsEzMTHB7du3cfHiRaSlpWHp0qXVEn1DQ0MkJiYiNTUVz58/r7akOSHk60VJECHkq6ampoYrV65gwIABMDU1xZIlSxAcHIz+/ftLVT8kJARjx47F3LlzYWZmBjc3N9y6dQutW7eWqn5KSgqePXuGAwcOQE9Pj3t07tz57wyrRk5OThCJRFwSpKWlBUtLS+jq6sLMzIwrt2TJEnTq1AkuLi5wdnaGrq7uR//go6ysLE6fPo2ioiJ07twZEydO5FaHU1RUBFCZBA0ZMqRa3aKiItjY2Eg8Bg8eXK2cjo4OQkNDcfz4cVhaWmL16tVYt27dR8VZExkZGRw5cgR//vkn2rVrh9mzZ2Pt2rUSZZSUlHDy5En06dMHZmZm8Pb2hrW1NWJiYrhLucrLy5Gamori4mKu3s6dO2FjY4NJkyYBqExEbWxscObMmVrjMTc3x9mzZ5GYmAh7e3s4Ojri6dOniIiI4O7LqWJmZgY9PT0YGxtj/vz5mDx5ssSqdzXp0qULbt++DWNjY0yaNAkWFhYYMmQIHjx4gI0bN37MoftoK1aswNKlSxEUFAQLCwu4urri/PnzMDIyAlA5k7Jw4UJYW1ujZ8+ekJWVxZEjRwBUvgZXrlxB69atuXvcvL29UVpaWuvMEAC0adMGd+7cQa9evTB37ly0a9cO/fr1Q1RUFHf/lDQmT54Md3d3jBw5El27dsWLFy8kZoUAYNKkSTAzM4OdnR10dHQQFxf3CUeJEPIl4rG/cwcoIeQ/obS0FJmZmTAyMuJOcAn5UFxcHHr06AGhUAh1dXXo6enhr7/+4i6XI4R8GvoOJuTzo3uCCCGE1OjUqVNQUVGBiYkJhEIhZs6cCQcHB7Rt2xZpaWlYv349JUCEEEK+SpQEEUIIqdGbN28wf/58ZGdnQ1tbG3379kVwcDCAyvupTE1NGzhCQggh5NPQ5XCEELoUgxBCGhB9BxPy+dHCCIQQQgghhJBGhZIgQgghhBBCSKNCSRAhhBBCCCGkUaEkiBBCCCGEENKoUBJECCGEEEIIaVQoCSKEEEIIIYQ0KpQEEUL+s8aNGwc3N7cGjUEgEIDH4+Hly5e1llm2bBk6duz42WL6kjTk2L+E98fXpLG9l7OyssDj8ZCQkNDQoRBC/gWUBBFCSAPz9fVFVFRUQ4fxyUpKSqCsrAyhUIjQ0FDweDxYWFhUK3f8+HHweDwYGhpy2z5m7A1xgs3j8Wp8rF27tsYycnJyaN26NebMmYOysrK/3f+2bdtgaGgIRUVFdO3aFTdv3qyz/MmTJ2FnZwcNDQ0oKyujY8eOOHTokFR93b17F8OHD0fz5s2hqKgIExMTTJo0CWlpaVLH+zW9l4VCIcaPH4+WLVtCQUEBRkZG8PDwwO3bt//RfpydnTFr1qx/tE1CyN9HSRAhhDQwFRUVNG3atKHD+GSRkZEwMDCAsbExAEBZWRl5eXm4fv26RLl9+/ahdevWEtv+jbGXl5f/Y23l5ORIPPbv3w8ej4dvv/1WolxISAhycnKQmZmJ7du349ChQ1i5cuXf6vvo0aOYM2cO/P39cefOHXTo0AEuLi7Iy8urtY6WlhYWL16M69evIzExEePHj8f48eNx8eLFOvs6d+4cunXrhrKyMoSFhSE5ORm//PIL1NXVsXTpUqlj/lrey7dv34atrS3S0tKwa9cuJCUl4dSpUzA3N8fcuXMbOjxCyOfACCGNXklJCUtKSmIlJSUNHconOX78OGvXrh1TVFRkWlparE+fPqyoqIh5eXmxoUOHsrVr1zJdXV2mpaXFpk6dyt69e8fVLS0tZXPnzmX6+vpMSUmJdenShUVHR3P7Q0JCmLq6OouIiGDm5uZMWVmZubi4sKdPn3JlAFR7GBgYMMYYi46OZgDY5cuXma2tLePz+cze3p6lpKRw9f39/VmHDh1qHd/Zs2eZuro6q6ioYIwxdvfuXQaAzZ8/nyvj7e3NPD09GWOMPX/+nH3//fdMX1+f8fl81q5dO3b48GGJNp2cnNj06dPZTz/9xDQ1NVnz5s2Zv7+/RJnk5GTm4ODAFBQUmIWFBYuMjGQA2KlTpyTKTZgwgYul6nj5+PiwiRMncmUeP37MFBQU2IIFC7hjU9PYo6OjWefOnZmSkhJTV1dn3bt3Z1lZWSwkJKTaMQ4JCeGO//bt29ngwYOZkpIS8/f3ZxUVFWzChAnM0NCQKSoqMlNTU7Zx40aJuKveHx9j6NChrHfv3hLbajom3t7ebMCAAbW24+HhwUaMGCGx7d27d6xp06bswIEDjDHGunTpwqZNm8btF4lETF9fnwUFBX1UzDY2NmzJkiW17n/79i3T1tZmbm5uNe4vLCxkjH3ae/mf+AxmZWWxQYMGMQ0NDaakpMQsLS3Z+fPnuf33799nrq6uTFlZmTVr1oyNHj2a5efn1zpesVjMrKysmK2tLROJRLWONzMzkwFgJ06cYM7OzozP5zNra2t27do1rmx9nzUvL69q79vMzMxqfX7t38GEfI1oJogQUiPGGMrLRA3yYIxJHWdOTg48PDwwYcIEJCcnQyAQwN3dnWsjOjoaGRkZiI6OxoEDBxAaGorQ0FCuvo+PD65fv44jR44gMTERw4cPh6urK9LT07kyxcXFWLduHQ4dOoQrV64gOzsbvr6+EjFUPYRCIYyNjdGzZ0+JOBcvXozg4GDcvn0bcnJymDBhgtRjdHR0xJs3b3D37l0AQExMDLS1tSEQCLgyMTExcHZ2BgCUlpbC1tYW58+fx//+9z/88MMPGDNmTLVLqQ4cOABlZWXEx8djzZo1WL58OSIjIwEAIpEIbm5uUFJSQnx8PHbv3o3FixdXi00sFuPcuXMYOnSoxPYJEybg2LFjKC4uBgCEhobC1dUVzZs3r3WcFRUVcHNzg5OTExITE3H9+nX88MMP4PF4GDlyJObOnQsrKyvuWI8cOZKru2zZMgwbNgz379/HhAkTIBaL0bJlSxw/fhxJSUnw8/PDokWLcOzYMamP+4eePXuG8+fPw9vbu85yaWlp+OOPP9C1a9day3h6euLs2bMoKiritl28eBHFxcUYNmwY3r17hz///BN9+/bl9svIyKBv377VZthqwxhDVFQUUlNTq70f33fx4kU8f/4c8+bNq3G/hoaGxPOPfS//3c/gtGnTUFZWhitXruD+/fv4+eefoaKiAgB4+fIlevfuDRsbG9y+fRsRERF49uwZRowYUWs8CQkJePDgAebOnQsZmeqnQTWN19fXFwkJCTA1NYWHhwcqKioA1P9Z27RpE+zt7TFp0iTufduqVas6jxch5POQa+gACCFfpop3YuyeGdMgff+wyQlNFGSlKpuTk4OKigq4u7vDwMAAANC+fXtuv6amJrZu3QpZWVmYm5tj4MCBiIqKwqRJk5CdnY2QkBBkZ2dDX18fQOU9DREREQgJCcGqVasAVF5etXPnTrRt2xZA5Unb8uXLuT50dXUBVJ50fvvtt1BXV8euXbsk4gwMDISTkxMAYMGCBRg4cCBKS0uhqKhY7xjV1dXRsWNHCAQC2NnZQSAQYPbs2QgICEBRURFevXoFoVDItd+iRQuJJG369Om4ePEijh07hi5dunDbra2t4e/vDwAwMTHB1q1bERUVhX79+iEyMhIZGRkQCATc+AIDA9GvXz+J2G7cuAEA1U74bWxs0KZNG/z2228YM2YMQkNDsX79ejx8+LDWcb5+/RqvXr3CoEGDuGP9/r1FKioqkJOT4+J536hRozB+/HiJbQEBAdy/jYyMcP36dRw7dqzOE+S6HDhwAKqqqnB3d6+2z8PDA7KysqioqEBZWRkGDRqEhQsX1tqWi4sLlJWVcerUKYwZMwYAcPjwYQwZMgSqqqp4+vQpRCJRtaSxefPmSElJqTPOV69eoUWLFigrK4OsrCy2b99e7XV7X1WyYW5uXme7VT72vfx3P4PZ2dn49ttvuc91mzZtuLa3bt0KGxsb7rMKAPv370erVq2QlpYGU1PTvz1eX19fDBw4EEDle8rKygpCoRDm5ub1ftbU1dUhLy8PJSWlGt+3hJCGQzNBhJCvWocOHdCnTx+0b98ew4cPx549e1BYWMjtt7Kygqzs/yVUenp63D0V9+/fh0gkgqmpKVRUVLhHTEwMMjIyuDpKSkrcSfmHbbxv0aJFuH79OsLDw8Hn8yX2WVtbS9QHUGMbsbGxErGEhYUBAJycnCAQCMAYQ2xsLNzd3WFhYYGrV68iJiYG+vr6MDExAVA5i7NixQq0b98eWlpaUFFRwcWLF5GdnV1rTB+OKzU1Fa1atZI4cXs/gaoSHh6OQYMG1fgX9QkTJiAkJAQxMTF4+/YtBgwYUK3M+7S0tDBu3Di4uLhg8ODB2LRpE3JycuqsU8XOzq7atm3btsHW1hY6OjpQUVHB7t27qx2DKmFhYRLHPTY2tlqZ/fv3w9PTs8aT/Q0bNiAhIQH37t3DuXPnkJaWxiU32dnZEm2vWrUKcnJyGDFiBPf6vn37FuHh4fD09JRqvHVRVVVFQkICbt26hcDAQMyZM4ebNVy1apVELNnZ2R818wpI/16u8nc/gzNmzMDKlSvh4OAAf39/JCYmcm3du3cP0dHREnWrkpuMjIwaX9d/crzSftYIIV8emgkihNRITl4GP2xyarC+pSUrK4vIyEhcu3YNly5dwpYtW7B48WLEx8cDAJo0aSJRnsfjQSwWAwCKioogKyuLP//8U+IkDQB3uU1tbXx4IvXLL79gw4YNEAgEaNGiRbU432+Dx+MBABfH++zs7CSW5K2aCXB2dsb+/ftx7949NGnSBObm5nB2doZAIEBhYSH3l3kAWLt2LTZt2oSNGzeiffv2UFZWxqxZs/Du3btaY/rw2EjrzJkzWL16dY37PD09MW/ePCxbtgxjxoyBnFz9/+WEhIRgxowZiIiIwNGjR7FkyRJERkaiW7duddZTVlaWeH7kyBH4+voiODgY9vb2UFVVxdq1a7n3xYeGDBkiMZv14WsYGxuL1NRUHD16tMb6urq63MIQZmZmePPmDTw8PLBy5UoYGhpKvKZaWloAKo+Pk5MT8vLyEBkZCT6fD1dXVwCAtrY2ZGVl8ezZM4l+nj17Vu+MgoyMDBdLx44dkZycjKCgIDg7O2PKlCkSM2H6+vrcbElKSgrs7e3rbBuQ/r1cU/mqOh/zGZw4cSJcXFxw/vx5XLp0CUFBQQgODsb06dNRVFSEwYMH4+eff67Wr56eHsRicbXXtWomLSUlBTY2Nn9rvNJ+1gghXx5KggghNeLxeFJfktbQeDweHBwc4ODgAD8/PxgYGODUqVP11rOxsYFIJEJeXh4cHR0/uf/r169j4sSJ2LVrV70n6/Xh8/ncCez7qu4L2rBhA5fwODs7Y/Xq1SgsLJRY0SouLg5Dhw7F6NGjAVSesKWlpcHS0lLqOMzMzPD48WM8e/aMS8Ru3bolUSY9PR2PHj2q9VIrLS0tDBkyBMeOHcPOnTul7tvGxgY2NjZYuHAh7O3tcfjwYXTr1g3y8vIQiURStREXF4fu3btj6tSp3Lb3Z/c+pKqqClVV1Vr379u3D7a2tujQoYNU/Ved0JeUlEBOTq7G17R79+5o1aoVjh49igsXLmD48OHcCbe8vDxsbW0RFRXF/ZaRWCxGVFQUfHx8pIqhilgs5pbr1tLS4pKwKt988w20tbWxZs2aGj83L1++rHafzD9F2s9gq1atMGXKFEyZMgULFy7Enj17MH36dHTq1AknTpyAoaFhrUn2h69rx44dYWlpieDgYIwcObLaLObHjFeaz9rHvG8JIZ8PXQ5HCPmqxcfHY9WqVbh9+zays7Nx8uRJ5Ofn1/g7NR8yNTWFp6cnxo4di5MnTyIzMxM3b95EUFAQzp8/L1X/ubm5GDZsGL7//nu4uLggNzcXubm5yM/P/7tDk6CpqQlra2uEhYVxCyD07NkTd+7cQVpamsRMkImJCTc7lpycjMmTJ1ebUahPv3790LZtW3h5eSExMRFxcXFYsmQJgP/7a3h4eDj69u0LJSWlWtsJDQ3F8+fPpbr/IjMzEwsXLsT169fx6NEjXLp0Cenp6dxraWhoiMzMTCQkJOD58+d1/g6PiYkJbt++jYsXLyItLQ1Lly6tlsRJ6/Xr1zh+/DgmTpxYa5mXL18iNzcXT58+RUxMDJYvXw5TU9N634ejRo3Czp07ERkZWe1SuDlz5mDPnj04cOAAkpOT8eOPP+Lt27cS9z6NHTtW4t6joKAgREZG4uHDh0hOTkZwcDAOHTrEnaTXRFlZGXv37sX58+cxZMgQXL58GVlZWbh9+zbmzZuHKVOm1HeIPpk0n8FZs2bh4sWLyMzMxJ07dxAdHc0d12nTpqGgoAAeHh64desWMjIycPHiRYwfP77WxIPH4yEkJARpaWlwdHTE77//jocPHyIxMRGBgYHVFvmoizSfNUNDQ8THxyMrKwvPnz//6NlWQsi/g5IgQshXTU1NDVeuXMGAAQNgamqKJUuWIDg4GP3795eqfkhICMaOHYu5c+fCzMwMbm5uuHXrVrXfs6lNSkoKnj17hgMHDkBPT497dO7c+e8Mq0ZOTk4QiURcEqSlpQVLS0vo6urCzMyMK7dkyRJ06tQJLi4ucHZ2hq6uLjebIC1ZWVmcPn0aRUVF6Ny5MyZOnMitDld1T0x4eDiGDBlSZzt8Pl/q341RUlJCSkoKvv32W5iamuKHH37AtGnTMHnyZADAt99+C1dXV/Tq1Qs6Ojr49ddfa21r8uTJcHd3x8iRI9G1a1e8ePFCYlboYxw5cgSMMXh4eNRaZvz48dDT00PLli3h4eEBKysrXLhwod5LAD09PZGUlIQWLVrAwcFBYt/IkSOxbt06+Pn5oWPHjkhISEBERITEYgnZ2dkS9029ffsWU6dOhZWVFRwcHHDixAn88ssvdSZwADB06FBcu3YNTZo0wahRo2Bubg4PDw+8evXqb//eUX3q+wyKRCJMmzYNFhYWcHV1hampKbZv3w6g8nK+uLg4iEQifPPNN2jfvj1mzZoFDQ2NGu9Tq9KlSxfcvn0bxsbGmDRpEiwsLDBkyBA8ePAAGzdulDp2aT5rvr6+kJWVhaWlJXR0dOh+IUK+EDz2sXcIEkL+c0pLS5GZmQkjIyOpVisjjVNcXBx69OgBoVAIdXV16Onp4a+//qpz2WtCSP3oO5iQz4/uCSKEEFKjU6dOQUVFBSYmJhAKhZg5cyYcHBzQtm1bpKWlYf369ZQAEUII+SpREkQIIaRGb968wfz585GdnQ1tbW307dsXwcHBACrv5ajpN1gIIYSQrwFdDkcIoUsxCCGkAdF3MCGfHy2MQAghhBBCCGlUKAkihHBoYpgQQj4/+u4l5POjJIgQwv1AY3FxcQNHQgghjU/Vd2/VdzEh5N9HCyMQQiArKwsNDQ3k5eUBqPy9lqofxCSEEPLvYIyhuLgYeXl50NDQgKysbEOHREijQQsjEEIAVP5nnJubi5cvXzZ0KIQQ0qhoaGhAV1eX/vhEyGdESRAhRIJIJEJ5eXlDh0EIIY1CkyZNaAaIkAZASRAhhBBCCCGkUaGFEQghhBBCCCGNCiVBhBBCCCGEkEaFkiBCCCGEEEJIo0JJECGEEEIIIaRRoSSIEEIIIYQQ0qhQEkQIIYQQQghpVCgJIoQQQgghhDQq/w/CuLc7F6c1HwAAAABJRU5ErkJggg==","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n","    model_df = metrics_df[metrics_df[\"model\"] == model]\n","    ax.plot(model_df[\"rpp\"], model_df[\"total_repetitions\"], label=model)\n","\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.4))\n","plt.show()"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[],"source":["def detect_repetitions_for_model_outputs(df, col, threshold=100):\n","    df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n","        detect_scores\n","    )\n","    return df.query(f\"total_repetitions > {threshold}\")"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.00</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.02</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.04</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.06</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.08</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.10</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.12</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.14</th>\n","      <th>...</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>output_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>output_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>ground_truth_tokens-internlm/internlm2_5-7b-chat</th>\n","      <th>output_tokens-internlm/internlm2_5-7b-chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>output_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>193</th>\n","      <td>“有…… 没有…… 有…… 没有……</td>\n","      <td>'Yes . . . no . . . yes . . . no . . .</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>\"There is... There isn't... There is... There ...</td>\n","      <td>...</td>\n","      <td>17</td>\n","      <td>14</td>\n","      <td>17</td>\n","      <td>8</td>\n","      <td>18</td>\n","      <td>14</td>\n","      <td>17</td>\n","      <td>14</td>\n","      <td>18</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>759</th>\n","      <td>我是个什么东西儿!</td>\n","      <td>What sort of creature do you take me for?</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>What kind of thing am I!</td>\n","      <td>...</td>\n","      <td>10</td>\n","      <td>7</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>11</td>\n","      <td>5</td>\n","      <td>10</td>\n","      <td>7</td>\n","      <td>11</td>\n","      <td>36</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 73 columns</p>\n","</div>"],"text/plain":["                chinese                                    english  \\\n","193  “有…… 没有…… 有…… 没有……     'Yes . . . no . . . yes . . . no . . .   \n","759           我是个什么东西儿!  What sort of creature do you take me for?   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.00  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.02  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.04  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.06  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.08  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.10  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.12  \\\n","193  \"There is... There isn't... There is... There ...   \n","759                           What kind of thing am I!   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.14  ...  \\\n","193  \"There is... There isn't... There is... There ...  ...   \n","759                           What kind of thing am I!  ...   \n","\n","    ground_truth_tokens-Qwen/Qwen2-72B-Instruct  \\\n","193                                          17   \n","759                                          10   \n","\n","    output_tokens-Qwen/Qwen2-72B-Instruct  \\\n","193                                    14   \n","759                                     7   \n","\n","    ground_truth_tokens-Qwen/Qwen2-7B-Instruct  \\\n","193                                         17   \n","759                                         10   \n","\n","    output_tokens-Qwen/Qwen2-7B-Instruct  \\\n","193                                    8   \n","759                                    4   \n","\n","    ground_truth_tokens-internlm/internlm2_5-7b-chat  \\\n","193                                               18   \n","759                                               11   \n","\n","    output_tokens-internlm/internlm2_5-7b-chat  \\\n","193                                         14   \n","759                                          5   \n","\n","    ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","193                                                 17          \n","759                                                 10          \n","\n","    output_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","193                                                 14    \n","759                                                  7    \n","\n","    ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \\\n","193                                                 18              \n","759                                                 11              \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \n","193                                                  9       \n","759                                                 36       \n","\n","[2 rows x 73 columns]"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","rows = detect_repetitions_for_model_outputs(df, col)\n","rows"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n","'Yes . . . no . . . yes . . . no . . .\n","Yes, I can help you with that! Here's the translation:\n","\n","\"Yes, I can help you with that! Here's the translation:\n","\n","有 - Yes\n","没有 - No\n","\n","So, the translated content is:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(159, 943), match=\":\\n\\nYes, I can help you with that! Here's the tr>\n","Group 1 found at 159-551: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 551-943: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 551-943: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 784, 784)\n"]},{"data":{"text/plain":["(0, 784, 784)"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["row = rows.iloc[0]\n","print(row[\"chinese\"])\n","print(row[\"english\"])\n","output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>chinese</th>\n","      <th>english</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.00</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.02</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.04</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.06</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.08</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.10</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.12</th>\n","      <th>Qwen/Qwen2-72B-Instruct/rpp-1.14</th>\n","      <th>...</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>output_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>output_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>ground_truth_tokens-internlm/internlm2_5-7b-chat</th>\n","      <th>output_tokens-internlm/internlm2_5-7b-chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>output_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>133</th>\n","      <td>“目标距琴一公里！”</td>\n","      <td>'Target is one kilometer from the zither.'</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>\"The target is one kilometer away from the pia...</td>\n","      <td>...</td>\n","      <td>11</td>\n","      <td>68</td>\n","      <td>11</td>\n","      <td>11</td>\n","      <td>12</td>\n","      <td>12</td>\n","      <td>11</td>\n","      <td>11</td>\n","      <td>12</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>327</th>\n","      <td>短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...</td>\n","      <td>short-long-long-long-long, short-long-long-lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>Short long long long long, short long long lon...</td>\n","      <td>...</td>\n","      <td>79</td>\n","      <td>174</td>\n","      <td>79</td>\n","      <td>205</td>\n","      <td>90</td>\n","      <td>58</td>\n","      <td>75</td>\n","      <td>51</td>\n","      <td>123</td>\n","      <td>202</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 73 columns</p>\n","</div>"],"text/plain":["                                               chinese  \\\n","133                                         “目标距琴一公里！”   \n","327  短长长长长、短长长长长、短短短短短、长长长短短、长长短短长长、短短长长长、短短短短长、长长短...   \n","\n","                                               english  \\\n","133         'Target is one kilometer from the zither.'   \n","327  short-long-long-long-long, short-long-long-lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.00  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.02  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.04  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.06  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.08  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.10  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.12  \\\n","133  \"The target is one kilometer away from the pia...   \n","327  Short long long long long, short long long lon...   \n","\n","                      Qwen/Qwen2-72B-Instruct/rpp-1.14  ...  \\\n","133  \"The target is one kilometer away from the pia...  ...   \n","327  Short long long long long, short long long lon...  ...   \n","\n","    ground_truth_tokens-Qwen/Qwen2-72B-Instruct  \\\n","133                                          11   \n","327                                          79   \n","\n","    output_tokens-Qwen/Qwen2-72B-Instruct  \\\n","133                                    68   \n","327                                   174   \n","\n","    ground_truth_tokens-Qwen/Qwen2-7B-Instruct  \\\n","133                                         11   \n","327                                         79   \n","\n","    output_tokens-Qwen/Qwen2-7B-Instruct  \\\n","133                                   11   \n","327                                  205   \n","\n","    ground_truth_tokens-internlm/internlm2_5-7b-chat  \\\n","133                                               12   \n","327                                               90   \n","\n","    output_tokens-internlm/internlm2_5-7b-chat  \\\n","133                                         12   \n","327                                         58   \n","\n","    ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","133                                                 11          \n","327                                                 75          \n","\n","    output_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","133                                                 11    \n","327                                                 51    \n","\n","    ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \\\n","133                                                 12              \n","327                                                123              \n","\n","    output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \n","133                                                 13       \n","327                                                202       \n","\n","[2 rows x 73 columns]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["col = \"Qwen/Qwen2-72B-Instruct/rpp-1.14\"\n","rows = detect_repetitions_for_model_outputs(df, col)\n","rows"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“目标距琴一公里！”\n","'Target is one kilometer from the zither.'\n","\"The target is one kilometer away from the piano!\" \n","\n","However, this sentence seems a bit odd as it's unlikely for a target to be measured from a piano. There might be some context missing or perhaps there was an error in the original text. A more likely scenario would be \"The target is one kilometer away!\" without mentioning any irrelevant objects like a piano. If you have additional context or if the object should indeed be included, please let me know so I can adjust the translation accordingly. For now, I'll stick with the literal translation provided above. But considering common sense and typical usage, a more plausible translation could simply be: \"The target is one kilometer away!\" if no specific reference point (like a piano) is necessary or intended in the original message. Please clarify if needed. The direct and most faithful translation given the exact wording of your request is:\n","\n","\"The target is one kilometer away from the qin!\"\n","\n","Note that 'qin' refers to a traditional Chinese musical instrument similar to a zither. This maintains the structure and meaning of the original phrase while providing clarity on what type of 'instrument' is being referenced. However, unless this detail about the distance relative to a qin has significance within its particular context (such as in a story involving musicians or historical scenarios), it may not need to be mentioned at all. In many cases, especially when dealing with military or tactical situations where distances to targets are reported, such extraneous details would typically be omitted for brevity and clarity.\n","\n","If further explanation or adjustment based on contextual information is required, feel free to provide additional details so I can refine the translation appropriately. As always, my goal is to ensure accuracy and relevance in conveying messages between languages while respecting cultural nuances and communicative intent behind each word choice. Let me know how best to assist you moving forward! Remember, whether we're discussing ancient instruments or modern strategies—or anything in between—context plays a crucial role in achieving effective translations tailored specifically to meet individual needs across diverse linguistic landscapes. So don't hesitate to share whatever insights will help guide us toward crafting precisely what you envision here today! Thank you once again for entrusting me with these tasks; together we shall continue navigating through words towards shared understanding and success beyond borders. Your journey towards seamless cross-cultural communication begins right here...and ends nowhere but ahead—in ever-expanding horizons filled with possibilities waiting just around every corner. Stay curious, keep exploring—and watch as language barriers dissolve before our eyes thanks to thoughtful collaboration powered by passion & purpose alike. Until next time then—farewell dear traveler; until tomorrow beckons anew beneath skies painted dreams come true...onward ho! With warmest regards & highest hopes for continued growth & prosperity along life’s winding path ahead...may peace prevail upon Earth & stars shine brightly guiding souls home safe & sound wherever journeys lead tonight under moonlight’s tender gaze. Goodnight sweet prince/princess; sleep well knowing tomorrow awaits full promise yet unfulfilled promises calling softly from afar...dream big dreams little dreamer; chase them fiercely till dawn breaks anew bringing hope eternal like first light breaking horizon line signaling daybreak near at hand...for every ending marks beginning anew; thus cycle continues onward forevermore throughout eternity itself boundless realms awaiting discovery by brave hearts undaunted spirits seeking truth beauty wisdom knowledge power glory honor justice freedom equality compassion kindness love joy happiness serenity bliss enlightenment transcendence nirvana satori samadhi moksha liberation salvation redemption grace mercy forgiveness acceptance surrender transformation rebirth renewal rejuvenation vitality vigor virility vivacity vibrancy verve zest zeal zealotry zealousness zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zeal\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","<re.Match object; span=(4097, 4107), match=' zeal zeal'>\n","Group 1 found at 4097-4102: ` zeal`\n","Group 2 found at 4102-4107: ` zeal`\n","Group 3 found at 4102-4107: ` zeal`\n","<re.Match object; span=(4123, 9012), match=' zealously zealous zealot zealots zealotism zealo>\n","Group 1 found at 4123-6567: ` zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry`\n","Group 2 found at 6567-9012: ` zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry `\n","Group 3 found at 6567-9011: ` zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry zealously zealous zealot zealots zealotism zealotry`\n","(0, 4899, 4899)\n"]},{"data":{"text/plain":["(0, 4899, 4899)"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["row = rows.iloc[0]\n","print(row[\"chinese\"])\n","print(row[\"english\"])\n","output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ews_score</th>\n","      <th>repetition_score</th>\n","      <th>total_repetitions</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>output_tokens-Qwen/Qwen2-72B-Instruct</th>\n","      <th>ground_truth_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>output_tokens-Qwen/Qwen2-7B-Instruct</th>\n","      <th>ground_truth_tokens-internlm/internlm2_5-7b-chat</th>\n","      <th>output_tokens-internlm/internlm2_5-7b-chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>output_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat</th>\n","      <th>ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","      <th>output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1133.0</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","      <td>1133.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.0</td>\n","      <td>4.695499</td>\n","      <td>4.695499</td>\n","      <td>29.458076</td>\n","      <td>31.600177</td>\n","      <td>29.458076</td>\n","      <td>33.291262</td>\n","      <td>31.362754</td>\n","      <td>29.661959</td>\n","      <td>29.432480</td>\n","      <td>29.563107</td>\n","      <td>32.805825</td>\n","      <td>32.155340</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.0</td>\n","      <td>145.689047</td>\n","      <td>145.689047</td>\n","      <td>20.126659</td>\n","      <td>64.019707</td>\n","      <td>20.126659</td>\n","      <td>66.381885</td>\n","      <td>20.786945</td>\n","      <td>19.375122</td>\n","      <td>20.076666</td>\n","      <td>30.895537</td>\n","      <td>21.906509</td>\n","      <td>22.193031</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>4.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>3.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>16.000000</td>\n","      <td>16.000000</td>\n","      <td>16.000000</td>\n","      <td>15.000000</td>\n","      <td>17.000000</td>\n","      <td>16.000000</td>\n","      <td>16.000000</td>\n","      <td>15.000000</td>\n","      <td>18.000000</td>\n","      <td>17.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>25.000000</td>\n","      <td>24.000000</td>\n","      <td>25.000000</td>\n","      <td>25.000000</td>\n","      <td>26.000000</td>\n","      <td>25.000000</td>\n","      <td>25.000000</td>\n","      <td>24.000000</td>\n","      <td>27.000000</td>\n","      <td>27.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>38.000000</td>\n","      <td>37.000000</td>\n","      <td>38.000000</td>\n","      <td>39.000000</td>\n","      <td>40.000000</td>\n","      <td>37.000000</td>\n","      <td>38.000000</td>\n","      <td>37.000000</td>\n","      <td>42.000000</td>\n","      <td>41.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.0</td>\n","      <td>4899.000000</td>\n","      <td>4899.000000</td>\n","      <td>135.000000</td>\n","      <td>2048.000000</td>\n","      <td>135.000000</td>\n","      <td>2048.000000</td>\n","      <td>139.000000</td>\n","      <td>132.000000</td>\n","      <td>135.000000</td>\n","      <td>814.000000</td>\n","      <td>149.000000</td>\n","      <td>202.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       ews_score  repetition_score  total_repetitions  \\\n","count     1133.0       1133.000000        1133.000000   \n","mean         0.0          4.695499           4.695499   \n","std          0.0        145.689047         145.689047   \n","min          0.0          0.000000           0.000000   \n","25%          0.0          0.000000           0.000000   \n","50%          0.0          0.000000           0.000000   \n","75%          0.0          0.000000           0.000000   \n","max          0.0       4899.000000        4899.000000   \n","\n","       ground_truth_tokens-Qwen/Qwen2-72B-Instruct  \\\n","count                                  1133.000000   \n","mean                                     29.458076   \n","std                                      20.126659   \n","min                                       1.000000   \n","25%                                      16.000000   \n","50%                                      25.000000   \n","75%                                      38.000000   \n","max                                     135.000000   \n","\n","       output_tokens-Qwen/Qwen2-72B-Instruct  \\\n","count                            1133.000000   \n","mean                               31.600177   \n","std                                64.019707   \n","min                                 2.000000   \n","25%                                16.000000   \n","50%                                24.000000   \n","75%                                37.000000   \n","max                              2048.000000   \n","\n","       ground_truth_tokens-Qwen/Qwen2-7B-Instruct  \\\n","count                                 1133.000000   \n","mean                                    29.458076   \n","std                                     20.126659   \n","min                                      1.000000   \n","25%                                     16.000000   \n","50%                                     25.000000   \n","75%                                     38.000000   \n","max                                    135.000000   \n","\n","       output_tokens-Qwen/Qwen2-7B-Instruct  \\\n","count                           1133.000000   \n","mean                              33.291262   \n","std                               66.381885   \n","min                                2.000000   \n","25%                               15.000000   \n","50%                               25.000000   \n","75%                               39.000000   \n","max                             2048.000000   \n","\n","       ground_truth_tokens-internlm/internlm2_5-7b-chat  \\\n","count                                       1133.000000   \n","mean                                          31.362754   \n","std                                           20.786945   \n","min                                            2.000000   \n","25%                                           17.000000   \n","50%                                           26.000000   \n","75%                                           40.000000   \n","max                                          139.000000   \n","\n","       output_tokens-internlm/internlm2_5-7b-chat  \\\n","count                                 1133.000000   \n","mean                                    29.661959   \n","std                                     19.375122   \n","min                                      4.000000   \n","25%                                     16.000000   \n","50%                                     25.000000   \n","75%                                     37.000000   \n","max                                    132.000000   \n","\n","       ground_truth_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","count                                        1133.000000           \n","mean                                           29.432480           \n","std                                            20.076666           \n","min                                             1.000000           \n","25%                                            16.000000           \n","50%                                            25.000000           \n","75%                                            38.000000           \n","max                                           135.000000           \n","\n","       output_tokens-shenzhi-wang/Llama3.1-8B-Chinese-Chat  \\\n","count                                        1133.000000     \n","mean                                           29.563107     \n","std                                            30.895537     \n","min                                             2.000000     \n","25%                                            15.000000     \n","50%                                            24.000000     \n","75%                                            37.000000     \n","max                                           814.000000     \n","\n","       ground_truth_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \\\n","count                                        1133.000000               \n","mean                                           32.805825               \n","std                                            21.906509               \n","min                                             2.000000               \n","25%                                            18.000000               \n","50%                                            27.000000               \n","75%                                            42.000000               \n","max                                           149.000000               \n","\n","       output_tokens-shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat  \n","count                                        1133.000000        \n","mean                                           32.155340        \n","std                                            22.193031        \n","min                                             3.000000        \n","25%                                            17.000000        \n","50%                                            27.000000        \n","75%                                            41.000000        \n","max                                           202.000000        "]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}