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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# SpanMarker\n",
        "SpanMarker is a model and library for performing span classification, originally used for Named Entity Recognition (NER). However, it could also be applied to detecting Part of Speech tokens (POS tagging). POS Tagging refers to assigning one label per word, where each label corresponds with a part of speech, such as VERB, NOUN or ADJ. Depending on the dataset, labels may be more finegrained, e.g.\n",
        "```\n",
        "'\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'\n",
        "```\n",
        "\n",
        "We can use SpanMarker for this task by converting a POS Dataset into a 🤗 Dataset with `tokens` and `ner_tags` columns."
      ],
      "metadata": {
        "id": "1Wj6hjlcdXp-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Installing\n",
        "First of all, make sure that \"GPU\" is selected as the Hardware Accelerator under `Runtime > Change runtime type`. Once set, we can install the dependencies."
      ],
      "metadata": {
        "id": "uuG8luo0fdBH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install span_marker -qqq"
      ],
      "metadata": {
        "id": "C2h3OObMff7O"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data loading and preprocessing\n",
        "\n",
        "Afterwards, we can load any Hugging Face Hub dataset with POS data, e.g. [batterydata/pos_tagging](https://huggingface.co/datasets/batterydata/pos_tagging). We can convert this into the format that is required for SpanMarker.\n",
        "\n"
      ],
      "metadata": {
        "id": "6-GMw_vgfjWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset_dict = load_dataset(\"batterydata/pos_tagging\")\n",
        "\n",
        "def labels_to_ner_tags(sample):\n",
        "    sample[\"ner_tags\"] = [1 if label.startswith(\"V\") else 0 for label in sample.pop(\"labels\")]\n",
        "    sample[\"tokens\"] = sample.pop(\"words\")\n",
        "    return sample\n",
        "\n",
        "dataset_dict = dataset_dict.map(labels_to_ner_tags)\n",
        "train_dataset = dataset_dict[\"train\"]\n",
        "eval_dataset = dataset_dict[\"test\"]\n",
        "labels = [\"O\", \"B-VERB\", \"I-VERB\"]\n",
        "\n",
        "train_dataset"
      ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n"
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              "Map:   0%|          | 0/13054 [00:00<?, ? examples/s]"
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            "text/plain": [
              "Dataset({\n",
              "    features: ['ner_tags', 'tokens'],\n",
              "    num_rows: 13054\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model loading\n",
        "We can initialize a SpanMarker model using a BERT-style encoder (e.g. the multilingual xlm-RoBERTa-large)."
      ],
      "metadata": {
        "id": "er5ZT-f3gdId"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Yh-4uBuhdVK_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b339110f-95b3-494d-835d-14c186c55bab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at xlm-roberta-large were not used when initializing XLMRobertaModel: ['lm_head.dense.bias', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']\n",
            "- This IS expected if you are initializing XLMRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing XLMRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from span_marker import SpanMarkerModel, Trainer, SpanMarkerModel\n",
        "\n",
        "# Initialize a SpanMarker model using a pretrained BERT-style encoder\n",
        "model_name = \"xlm-roberta-large\"\n",
        "model = SpanMarkerModel.from_pretrained(\n",
        "    model_name,\n",
        "    labels=labels,\n",
        "    # SpanMarker hyperparameters:\n",
        "    model_max_length=128,\n",
        "    marker_max_length=64,\n",
        "    entity_max_length=1,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "# Prepare the 🤗 transformers training arguments\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"models/span_marker_xlm_roberta_large_verbs\",\n",
        "    # Training Hyperparameters:\n",
        "    learning_rate=1e-5,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    warmup_ratio=0.1,\n",
        "    fp16=True,  # Replace `bf16` with `fp16` if your hardware can't use bf16.\n",
        "    # Other Training parameters\n",
        "    logging_first_step=True,\n",
        "    logging_steps=50,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    save_strategy=\"steps\",\n",
        "    eval_steps=1000,\n",
        "    save_total_limit=2,\n",
        "    dataloader_num_workers=2,\n",
        ")"
      ],
      "metadata": {
        "id": "6VKr6GuNg18i"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the trainer using our model, training args & dataset, and train\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YmGMd3Vg3DK",
        "outputId": "835a0308-5d01-4045-e079-d9c887f1f7b8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:span_marker.label_normalizer:Detected the IOB or IOB2 labeling scheme.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610,
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        },
        "id": "aX1iyB5Eg31F",
        "outputId": "63354904-b8b5-4093-ed3d-5be1d0a11143"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Label normalizing the train dataset:   0%|          | 0/13054 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b593d02393ae4a50a2903065fa63d3f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing the train dataset:   0%|          | 0/13054 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f60271e6439e48a8b62ce45d0267a326"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:span_marker.tokenizer:This SpanMarker model will ignore 0.023546% of all annotated entities in the train dataset. This is caused by the SpanMarkerModel maximum model input length of 128 tokens.\n",
            "A total of 10 (0.023546%) entities were missed due to the maximum input length.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Spreading data between multiple samples:   0%|          | 0/13054 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81132f1402d248558c8f7c5b7f6f41b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:span_marker.trainer:Spread 13054 sentences across 13083 samples, a 0.222154% increase. You can increase `model_max_length` or `marker_max_length` to decrease the number of samples, but recognize that longer samples are slower.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='4905' max='4905' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [4905/4905 1:00:42, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Overall Precision</th>\n",
              "      <th>Overall Recall</th>\n",
              "      <th>Overall F1</th>\n",
              "      <th>Overall Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.036000</td>\n",
              "      <td>0.015133</td>\n",
              "      <td>0.991139</td>\n",
              "      <td>0.973260</td>\n",
              "      <td>0.982118</td>\n",
              "      <td>0.995600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.012600</td>\n",
              "      <td>0.013101</td>\n",
              "      <td>0.985581</td>\n",
              "      <td>0.986418</td>\n",
              "      <td>0.985999</td>\n",
              "      <td>0.996522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.017500</td>\n",
              "      <td>0.015389</td>\n",
              "      <td>0.973481</td>\n",
              "      <td>0.989389</td>\n",
              "      <td>0.981370</td>\n",
              "      <td>0.995337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.017178</td>\n",
              "      <td>0.982052</td>\n",
              "      <td>0.987054</td>\n",
              "      <td>0.984547</td>\n",
              "      <td>0.996154</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Label normalizing the evaluation dataset:   0%|          | 0/1451 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1f33f79ca0c4b48bedda7084f6c8f8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing the evaluation dataset:   0%|          | 0/1451 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2277a67a4914abe80535e032d008590"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Spreading data between multiple samples:   0%|          | 0/1451 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8041bcf3a2a34cc6916e66e9ccfb01c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:span_marker.trainer:Spread 1451 sentences across 1455 samples, a 0.275672% increase. You can increase `model_max_length` or `marker_max_length` to decrease the number of samples, but recognize that longer samples are slower.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2533ba585ede4ee6bc09ec72ce92a793"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-7294c3a6873200e3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-3f23d6d42575286f.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-736674293271a954.arrow\n",
            "INFO:span_marker.trainer:Spread 1451 sentences across 1455 samples, a 0.275672% increase. You can increase `model_max_length` or `marker_max_length` to decrease the number of samples, but recognize that longer samples are slower.\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-7294c3a6873200e3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-3f23d6d42575286f.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-736674293271a954.arrow\n",
            "INFO:span_marker.trainer:Spread 1451 sentences across 1455 samples, a 0.275672% increase. You can increase `model_max_length` or `marker_max_length` to decrease the number of samples, but recognize that longer samples are slower.\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-7294c3a6873200e3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-3f23d6d42575286f.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-736674293271a954.arrow\n",
            "INFO:span_marker.trainer:Spread 1451 sentences across 1455 samples, a 0.275672% increase. You can increase `model_max_length` or `marker_max_length` to decrease the number of samples, but recognize that longer samples are slower.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=4905, training_loss=0.03492981094323957, metrics={'train_runtime': 3646.2474, 'train_samples_per_second': 10.764, 'train_steps_per_second': 1.345, 'total_flos': 1.8283684849207296e+16, 'train_loss': 0.03492981094323957, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"models/span_marker_xlm_roberta_large_verbs/checkpoint-final\")"
      ],
      "metadata": {
        "id": "rnmqDRcbg5WF"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute & save the metrics on the test set\n",
        "metrics = trainer.evaluate()\n",
        "trainer.save_metrics(\"eval\", metrics)\n",
        "metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "SlQNwjGhg6xJ",
        "outputId": "efc4b760-c572-4b0d-93d2-d9134c29f281"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-7294c3a6873200e3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-3f23d6d42575286f.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/batterydata___json/batterydata--pos_tagging-606f444cf86cfa2f/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-736674293271a954.arrow\n",
            "INFO:span_marker.trainer:Spread 1451 sentences across 1455 samples, a 0.275672% increase. You can increase `model_max_length` or `marker_max_length` to decrease the number of samples, but recognize that longer samples are slower.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='728' max='364' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [364/364 01:28]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.015249394811689854,\n",
              " 'eval_overall_precision': 0.984514212982605,\n",
              " 'eval_overall_recall': 0.9849320882852292,\n",
              " 'eval_overall_f1': 0.9847231063017186,\n",
              " 'eval_overall_accuracy': 0.99620633331577,\n",
              " 'eval_runtime': 26.9862,\n",
              " 'eval_samples_per_second': 53.916,\n",
              " 'eval_steps_per_second': 13.488,\n",
              " 'epoch': 3.0}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.model.predict(\"I'd love to try and figure out how well this works.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tHdTu1Qg7h7",
        "outputId": "6adce5b8-7e83-490e-f5eb-6fa67fb1482d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'span': 'love',\n",
              "  'label': 'VERB',\n",
              "  'score': 0.9993988275527954,\n",
              "  'char_start_index': 4,\n",
              "  'char_end_index': 8},\n",
              " {'span': 'try',\n",
              "  'label': 'VERB',\n",
              "  'score': 0.9999654293060303,\n",
              "  'char_start_index': 12,\n",
              "  'char_end_index': 15},\n",
              " {'span': 'figure',\n",
              "  'label': 'VERB',\n",
              "  'score': 0.9992504715919495,\n",
              "  'char_start_index': 20,\n",
              "  'char_end_index': 26}]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacy spacy-transformers -qqq\n",
        "!spacy download en_core_web_trf"
      ],
      "metadata": {
        "id": "XHwF7kHswoA3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy_transformers\n",
        "import spacy\n",
        "from spacy.tokens import Doc\n",
        "from tqdm.autonotebook import tqdm\n",
        "import seqeval\n",
        "\n",
        "nlp = spacy.load(\"en_core_web_trf\")\n",
        "\n",
        "preds = []\n",
        "golds = []\n",
        "for tokens, gold_ner_tags in tqdm(zip(eval_dataset[\"tokens\"], eval_dataset[\"ner_tags\"]), total=len(eval_dataset)):\n",
        "    doc = nlp(Doc(nlp.vocab, tokens))\n",
        "    pred_ner_tags = [\"B-VERB\" if token.pos_ in (\"VERB\", \"AUX\") else \"O\" for token in doc]\n",
        "    gold_ner_tags = [\"B-VERB\" if tag else \"O\" for tag in gold_ner_tags]\n",
        "    preds.append(pred_ner_tags)\n",
        "    golds.append(gold_ner_tags)\n",
        "\n",
        "print(seqeval.metrics.classification_report(golds, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192,
          "referenced_widgets": [
            "55ddf1d69e7b4eed8a7eba4b26d6154e",
            "a1a4b72caee44232b1025527b61905e3",
            "efc200c3a80044f68a3adc5853668709",
            "7db9208f041b433c8c10233a951cbd5a",
            "ffd4844ae0604ff5ab4952746b9c0423",
            "c922ed4949dd47539c7924ecce8bf259",
            "52bb35f369b54d0f82b14a01e86e958b",
            "d223c4194869496eae7446c53baf728c",
            "8ad70c47ef2c4b64989d10f02839ff60",
            "726da7ecc1d149fab60b834e3e917ec8",
            "ffd7f7bcc7a348e285968211fd3254bd"
          ]
        },
        "id": "3J7GwcOVwt_c",
        "outputId": "b24f5341-35a1-4189-c305-bd5f423894c7"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1451 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55ddf1d69e7b4eed8a7eba4b26d6154e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        VERB       0.91      0.97      0.94      4712\n",
            "\n",
            "   micro avg       0.91      0.97      0.94      4712\n",
            "   macro avg       0.91      0.97      0.94      4712\n",
            "weighted avg       0.91      0.97      0.94      4712\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that these results are not truly indicative of spaCy's power - it was trained on a slightly different label set, probably using a slightly different tokenizer.\n",
        "\n",
        "However, it's clear that the SpanMarker model is very adept at classifying verbs, as it reached a 98.472 F1. You might be able to get slightly better performance if you use the English-only `roberta-large` instead of the multilingual `xlm-roberta-large`.\n",
        "Beyond that, a `large` model might be overkill here."
      ],
      "metadata": {
        "id": "arjr6tzP4jd3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.create_model_card()\n",
        "!cat models/span_marker_xlm_roberta_large_verbs/README.md"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6GkwEZ88haR",
        "outputId": "62cada24-c87e-47f2-eedb-cc5cbaf5be92"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---\n",
            "tags:\n",
            "- generated_from_trainer\n",
            "model-index:\n",
            "- name: span_marker_xlm_roberta_large_verbs\n",
            "  results: []\n",
            "---\n",
            "\n",
            "<!-- This model card has been generated automatically according to the information the Trainer had access to. You\n",
            "should probably proofread and complete it, then remove this comment. -->\n",
            "\n",
            "# span_marker_xlm_roberta_large_verbs\n",
            "\n",
            "This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.\n",
            "It achieves the following results on the evaluation set:\n",
            "- Loss: 0.0152\n",
            "- Overall Precision: 0.9845\n",
            "- Overall Recall: 0.9849\n",
            "- Overall F1: 0.9847\n",
            "- Overall Accuracy: 0.9962\n",
            "\n",
            "## Model description\n",
            "\n",
            "More information needed\n",
            "\n",
            "## Intended uses & limitations\n",
            "\n",
            "More information needed\n",
            "\n",
            "## Training and evaluation data\n",
            "\n",
            "More information needed\n",
            "\n",
            "## Training procedure\n",
            "\n",
            "### Training hyperparameters\n",
            "\n",
            "The following hyperparameters were used during training:\n",
            "- learning_rate: 1e-05\n",
            "- train_batch_size: 4\n",
            "- eval_batch_size: 4\n",
            "- seed: 42\n",
            "- gradient_accumulation_steps: 2\n",
            "- total_train_batch_size: 8\n",
            "- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n",
            "- lr_scheduler_type: linear\n",
            "- lr_scheduler_warmup_ratio: 0.1\n",
            "- num_epochs: 3\n",
            "\n",
            "### Training results\n",
            "\n",
            "| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |\n",
            "|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|\n",
            "| 0.036         | 0.61  | 1000 | 0.0151          | 0.9911            | 0.9733         | 0.9821     | 0.9956           |\n",
            "| 0.0126        | 1.22  | 2000 | 0.0131          | 0.9856            | 0.9864         | 0.9860     | 0.9965           |\n",
            "| 0.0175        | 1.83  | 3000 | 0.0154          | 0.9735            | 0.9894         | 0.9814     | 0.9953           |\n",
            "| 0.0115        | 2.45  | 4000 | 0.0172          | 0.9821            | 0.9871         | 0.9845     | 0.9962           |\n",
            "\n",
            "\n",
            "### Framework versions\n",
            "\n",
            "- Transformers 4.30.2\n",
            "- Pytorch 2.0.1+cu118\n",
            "- Datasets 2.13.1\n",
            "- Tokenizers 0.13.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"span-marker-xlm-roberta-large-verbs\")"
      ],
      "metadata": {
        "id": "FqkHttjY0qrv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://huggingface.co/tomaarsen/span-marker-xlm-roberta-large-verbs"
      ],
      "metadata": {
        "id": "4YUU-AYz8sXR"
      }
    }
  ]
}