{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "yViS84WpSENo" }, "source": [ "### Sentiment analysis on twitter dataset\n", "\n", "Возьмем модель `sentiment-roberta-large-english` и дообучим ее на датасете твитов `carblacac/twitter-sentiment-analysis`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "X-X7InJOGwT_" }, "outputs": [], "source": [ "#!streamlit run /home/theodore/anaconda3/envs/ds/lib/python3.10/site-packages/ipykernel_launcher.py" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RKHGyhG9G6WF", "outputId": "bbaeac00-caa0-4eec-b77e-e44dbdf78f94" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.28.1)\n", "Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.11.0)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4)\n", "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in 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(from aiohttp->datasets) (6.0.4)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n" ] } ], "source": [ "!pip install transformers datasets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "ohaBadE4GwUA" }, "outputs": [], "source": [ "# import json\n", "\n", "# with open(\"arxivData.json\", 'r') as f:\n", "# arxiv_data = json.load(f)\n", "\n", "# arxiv_data[0]['tag']" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "HAP_PN0iGwUB" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "import transformers\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 86, "referenced_widgets": [ "06de297475874730a793d3255cfe9a0d", "1a192819128a429b96c571f48c7de552", "4e50c449ee2047148e35c76fddf38e08", "12483d6de04546feb5ee3b99ede894dc", "6bc412e1b7c04160b58f83db5841d255", "8e44d4f0218f4c43bb28a22a1b0472f7", "9b9e621263e24472be473d87b2d54db5", "f4947eda1c18457a80aa7c68df79d32a", "11585997b0494e3db0f0121538e4f9f4", "dc511eb900064df8a10177ba5ea1e7ff", "2973a9eaa7f34f70bb02d4ffaea67462" ] }, "id": "ctTETRGcGwUC", "outputId": "49c41fe7-acc0-4678-d257-aac1502edc72" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Found cached dataset twitter-sentiment-analysis (/home/theodore/.cache/huggingface/datasets/carblacac___twitter-sentiment-analysis/default/1.0.0/cd65e23e456de6a4f7264e305380b0ffe804d6f5bfd361c0ec0f68d8d1fab95b)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ebd17ca95bce4cc3b9d0f9597a5d9a91", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3 [00:00, tokenizer=tokenizer, block_size=128)\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"siebert/sentiment-roberta-large-english\")\n", "model = AutoModelForSequenceClassification.from_pretrained(\"siebert/sentiment-roberta-large-english\", num_labels=2)\n", "\n", "# print(\"Dataset ready!\")\n", "dataset = data.map(lambda xs: tokenizer(xs[\"text\"], truncation=True, padding='max_length'))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "J2Q492BsOedY" }, "outputs": [], "source": [ "# model.to('cpu');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "xRYsJ1KTGwUF" }, "outputs": [], "source": [ "from datasets import ClassLabel\n", "\n", "dataset = dataset.rename_column(\"feeling\", \"labels\")\n", "dataset = dataset.cast_column(\"labels\", ClassLabel(names=['0', '1']))\n", "dataset = dataset.align_labels_with_mapping({'0': 1, '1': 0}, \"labels\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 339 }, "id": "BsGX2H5tGwUG", "outputId": "dbcb10ea-a7e8-431c-b96e-9fad8b448eb0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=2500, training_loss=0.7088180541992187, metrics={'train_runtime': 3241.3296, 'train_samples_per_second': 3.085, 'train_steps_per_second': 0.771, 'total_flos': 9319313633280000.0, 'train_loss': 0.7088180541992187, 'epoch': 1.0})" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer = Trainer(\n", " model=model, train_dataset=dataset[\"train\"].shuffle().select(range(10000)),\n", " eval_dataset = dataset['test'].select(range(5000)),\n", " args=TrainingArguments(\n", " output_dir=\"./my_saved_model\", overwrite_output_dir=True,\n", " num_train_epochs=1, per_device_train_batch_size=4,\n", " save_steps=10_000, save_total_limit=2),\n", ")\n", "\n", "trainer.train()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "DZLeB7tSWoKO" }, "outputs": [], "source": [ "import torch\n", "torch.save(model.state_dict(), \"./model_cached.pth\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PRFXZltKGwUH" }, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.read_json(\"./arxivData.json\")\n", "df.loc[0]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tBObhO5PfOGZ", "outputId": "3771e041-dac7-43ac-ff4e-fc202155a004" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/gdrive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/gdrive')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "6O7BNuwXGwUI" }, "outputs": [], "source": [ "model_save_name = 'model_cached.pth'\n", "path = F\"/content/gdrive/My Drive/cached_model.pth\" \n", "torch.save(model.state_dict(), path)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import torch\n", "from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModel, Trainer, TrainingArguments, LineByLineTextDataset\n", "import evaluate\n", "\n", "\n", "model_enhanced = AutoModelForSequenceClassification.from_pretrained(\"siebert/sentiment-roberta-large-english\", num_labels=2)\n", "model_enhanced.load_state_dict(torch.load('model_cached_2.pth', map_location=torch.device('cpu')))\n", "\n", "metric = evaluate.load(\"accuracy\")\n", "\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " predictions = np.argmax(logits, axis=-1)\n", " return metric.compute(predictions=predictions, references=labels)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "task_evaluator = evaluate.evaluator(\"text-classification\")\n", "data_to_evaluate = dataset[\"test\"].select(range(500))\n", "data_to_evaluate = data_to_evaluate.rename_column(\"feeling\", \"label\")\n", "\n", "results = task_evaluator.compute(\n", " model_or_pipeline=model_enhanced,\n", " tokenizer=tokenizer,\n", " data=data_to_evaluate,\n", " label_mapping={\"POSITIVE\": 1.0, \"NEGATIVE\": 0.0},\n", " metric=\"accuracy\",\n", " strategy=\"simple\"\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'accuracy': 0.758,\n", " 'total_time_in_seconds': 123.58850028699817,\n", " 'samples_per_second': 4.045683852776724,\n", " 'latency_in_seconds': 0.2471770005739963}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "ds", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", 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