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      "source": [
        "<a href=\"https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ot4A8Ag2zOSb"
      },
      "source": [
        "# Reproducing experimental results of LUKE on TACRED Using Hugging Face Transformers\n",
        "\n",
        "This notebook shows how to reproduce the state-of-the-art results on the [TACRED relation classification dataset](https://nlp.stanford.edu/projects/tacred/) reported in [this paper](https://arxiv.org/abs/2010.01057) using the Trasnsformers library and the [fine-tuned model checkpoint](https://huggingface.co/studio-ousia/luke-large-finetuned-tacred) available on the Model Hub.\n",
        "The source code used in the experiments is also available [here](https://github.com/studio-ousia/luke/tree/master/examples/relation_classification).\n",
        "\n",
        "There are two other related notebooks:\n",
        "\n",
        "* [Reproducing experimental results of LUKE on Open Entity Using Hugging Face Transformers](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb)\n",
        "* [Reproducing experimental results of LUKE on CoNLL-2003 Using Hugging Face Transformers](https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb)"
      ]
    },
    {
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      "metadata": {
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        },
        "id": "18lGoU-SmTK8",
        "outputId": "67f54b1c-4c19-4119-87bc-df34acd1253b"
      },
      "source": [
        "# Currently, LUKE is only available on the master branch\n",
        "!pip install git+https://github.com/huggingface/transformers.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git\n",
            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-l7qi_wn0\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBERtWDCnqXO"
      },
      "source": [
        "import json\n",
        "import torch\n",
        "from tqdm import trange\n",
        "from transformers import LukeTokenizer, LukeForEntityPairClassification"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRywXM4azmCP"
      },
      "source": [
        "## Loading the dataset\n",
        "\n",
        "**The TACRED dataset is not publicly available.** In the cell below, we copy the test set (test.json) uploaded to our Google Drive to the working directory. **Please obtain the dataset by following instructuions on the [TACRED web site](https://nlp.stanford.edu/projects/tacred/) and replace the cell below to place the test.json file in the current directory.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLzX8LIS127b",
        "outputId": "634b3b33-7322-4862-d57b-03ab3944f115"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp /content/drive/MyDrive/projects/luke/data/tacred/test.json test.json"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-v01ix-i2FS"
      },
      "source": [
        "def load_examples(dataset_file):\n",
        "    with open(dataset_file, \"r\") as f:\n",
        "        data = json.load(f)\n",
        "\n",
        "    examples = []\n",
        "    for i, item in enumerate(data):\n",
        "        tokens = item[\"token\"]\n",
        "        token_spans = dict(\n",
        "            subj=(item[\"subj_start\"], item[\"subj_end\"] + 1),\n",
        "            obj=(item[\"obj_start\"], item[\"obj_end\"] + 1)\n",
        "        )\n",
        "\n",
        "        if token_spans[\"subj\"][0] < token_spans[\"obj\"][0]:\n",
        "            entity_order = (\"subj\", \"obj\")\n",
        "        else:\n",
        "            entity_order = (\"obj\", \"subj\")\n",
        "\n",
        "        text = \"\"\n",
        "        cur = 0\n",
        "        char_spans = {}\n",
        "        for target_entity in entity_order:\n",
        "            token_span = token_spans[target_entity]\n",
        "            text += \" \".join(tokens[cur : token_span[0]])\n",
        "            if text:\n",
        "                text += \" \"\n",
        "            char_start = len(text)\n",
        "            text += \" \".join(tokens[token_span[0] : token_span[1]])\n",
        "            char_end = len(text)\n",
        "            char_spans[target_entity] = (char_start, char_end)\n",
        "            text += \" \"\n",
        "            cur = token_span[1]\n",
        "        text += \" \".join(tokens[cur:])\n",
        "        text = text.rstrip()\n",
        "\n",
        "        examples.append(dict(\n",
        "            text=text,\n",
        "            entity_spans=[tuple(char_spans[\"subj\"]), tuple(char_spans[\"obj\"])],\n",
        "            label=item[\"relation\"]\n",
        "        ))\n",
        "\n",
        "    return examples"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_H6us8Unl231"
      },
      "source": [
        "test_examples = load_examples(\"test.json\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHw1YtriA3S9"
      },
      "source": [
        "## Loading the fine-tuned model and tokenizer\n",
        "\n",
        "We construct the model and tokenizer using the [fine-tuned model checkpoint](https://huggingface.co/studio-ousia/luke-large-finetuned-tacred)."
      ]
    },
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        "id": "q9bXAEPZp0ZT",
        "outputId": "9a249a50-be0c-4bac-d61a-8ab293c5a61b"
      },
      "source": [
        "# Load the model checkpoint\n",
        "model = LukeForEntityPairClassification.from_pretrained(\"studio-ousia/luke-large-finetuned-tacred\")\n",
        "model.eval()\n",
        "model.to(\"cuda\")\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = LukeTokenizer.from_pretrained(\"studio-ousia/luke-large-finetuned-tacred\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9787cbcd2e974d6482d2981211f7396c",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3299.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0eae2309eb9d42378942cb5c7ad334e3",
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at studio-ousia/luke-large-finetuned-tacred were not used when initializing LukeForEntityPairClassification: ['luke.embeddings.position_ids']\n",
            "- This IS expected if you are initializing LukeForEntityPairClassification 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 LukeForEntityPairClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ],
          "name": "stderr"
        },
        {
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          ],
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            "\n"
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            "\n"
          ],
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuhdLhSlA93M"
      },
      "source": [
        "## Measuring performance\n",
        "\n",
        "We classify relations between entity pairs in the test set and measure the performance of the model.\n",
        "The performance reported in the [original paper](https://arxiv.org/abs/2010.01057) is successfully reproduced."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGnyU7rGnNg2",
        "outputId": "75ccce11-3b56-4ab4-ada3-7d5089ddfb86"
      },
      "source": [
        "batch_size = 128\n",
        "\n",
        "num_predicted = 0\n",
        "num_gold = 0\n",
        "num_correct = 0\n",
        "\n",
        "for batch_start_idx in trange(0, len(test_examples), batch_size):\n",
        "    batch_examples = test_examples[batch_start_idx:batch_start_idx + batch_size]\n",
        "    texts = [example[\"text\"] for example in batch_examples]\n",
        "    entity_spans = [example[\"entity_spans\"] for example in batch_examples]\n",
        "    gold_labels = [example[\"label\"] for example in batch_examples]\n",
        "\n",
        "    inputs = tokenizer(texts, entity_spans=entity_spans, return_tensors=\"pt\", padding=True)\n",
        "    inputs = inputs.to(\"cuda\")\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "    predicted_indices = outputs.logits.argmax(-1)\n",
        "    predicted_labels = [model.config.id2label[index.item()] for index in predicted_indices]\n",
        "    for predicted_label, gold_label in zip(predicted_labels, gold_labels):\n",
        "        if predicted_label != \"no_relation\":\n",
        "            num_predicted += 1\n",
        "        if gold_label != \"no_relation\":\n",
        "            num_gold += 1\n",
        "            if predicted_label == gold_label:\n",
        "                num_correct += 1\n",
        "\n",
        "precision = num_correct / num_predicted\n",
        "recall = num_correct / num_gold\n",
        "f1 = 2 * precision * recall / (precision + recall)\n",
        "\n",
        "print(f\"\\n\\nprecision: {precision} recall: {recall} f1: {f1}\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 122/122 [07:24<00:00,  3.64s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "precision: 0.7034638130104196 recall: 0.7512781954887218 f1: 0.7265852239674229\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BX-xzMbY2rWZ"
      },
      "source": [
        "## Detecting a relation between a pair of entities\n",
        "\n",
        "Finally, we detect a relation between a pair of entities in a text using the [fine-tuned model](https://huggingface.co/studio-ousia/luke-large-finetuned-tacred)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAFyp8Um26i2",
        "outputId": "8037362c-80be-4329-d2cd-7b71d6c4e176"
      },
      "source": [
        "text = \"Beyoncé lives in Los Angeles.\"\n",
        "entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to \"Beyoncé\" and \"Los Angeles\"\n",
        "\n",
        "inputs = tokenizer(text, entity_spans=entity_spans, return_tensors=\"pt\")\n",
        "inputs = inputs.to(\"cuda\")\n",
        "outputs = model(**inputs)\n",
        "\n",
        "predicted_class_idx = outputs.logits.argmax(-1).item()\n",
        "print(\"Predicted class:\", model.config.id2label[predicted_class_idx])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted class: per:cities_of_residence\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}