ExploringLayoutLM.ipynb

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            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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        "outputId": "94ad066d-bf64-4743-a49b-d8d16f9de201"
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      "source": [
        "from transformers import LayoutLMTokenizer, LayoutLMForTokenClassification\n",
        "import torch\n",
        "tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')\n",
        "model = LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased')"
      ],
      "execution_count": 2,
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            "\n"
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          "name": "stdout"
        },
        {
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at microsoft/layoutlm-base-uncased were not used when initializing LayoutLMForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']\n",
            "- This IS expected if you are initializing LayoutLMForTokenClassification 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 LayoutLMForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of LayoutLMForTokenClassification were not initialized from the model checkpoint at microsoft/layoutlm-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvfNiSahoaag"
      },
      "source": [
        "words = [\"Hello\", \"world\"]\n",
        "normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]\n",
        "token_boxes = []\n",
        "for word, box in zip(words, normalized_word_boxes):\n",
        "    word_tokens = tokenizer.tokenize(word)\n",
        "    token_boxes.extend([box] * len(word_tokens))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JL1Si-Loo4Q",
        "outputId": "92220308-9ac6-46cd-f83d-383472c6943d"
      },
      "source": [
        "token_boxes"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0, 0, 0, 0],\n",
              " [637, 773, 693, 782],\n",
              " [698, 773, 733, 782],\n",
              " [1000, 1000, 1000, 1000]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNDPLRIdorye",
        "outputId": "fb1ecab3-ccf5-470a-e297-67765be450ef"
      },
      "source": [
        "word_tokens"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['world']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ae1dZWxTocaL"
      },
      "source": [
        "# add bounding boxes of cls + sep tokens\n",
        "token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UE1PFerOoguu"
      },
      "source": [
        "encoding = tokenizer(' '.join(words), return_tensors=\"pt\")\n",
        "input_ids = encoding[\"input_ids\"]\n",
        "attention_mask = encoding[\"attention_mask\"]\n",
        "token_type_ids = encoding[\"token_type_ids\"]\n",
        "bbox = torch.tensor([token_boxes])\n",
        "token_labels = torch.tensor([1,1,0,0]).unsqueeze(0) # batch size of 1"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCE3AoEZoj1w"
      },
      "source": [
        "outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
        "                labels=token_labels)\n",
        "loss = outputs.loss\n",
        "logits = outputs.logits"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjULxcYXokvL",
        "outputId": "ad98db94-b9cb-4c4a-d760-20083e67166c"
      },
      "source": [
        "logits"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[-0.0344,  0.2428],\n",
              "         [ 0.1178,  0.2391],\n",
              "         [ 0.1211,  0.1931],\n",
              "         [-0.0339,  0.2423]]], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNHQDVYGoxJF",
        "outputId": "a804067e-2ae6-46f5-d1e1-ee828f51ebe2"
      },
      "source": [
        "loss"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.6923, grad_fn=<NllLossBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H5B-JO0oyh7",
        "outputId": "d22a900f-d086-4572-a9c2-30f6c3b0ceb6"
      },
      "source": [
        "from transformers import LayoutLMTokenizer, LayoutLMForSequenceClassification\n",
        "import torch\n",
        "tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')\n",
        "model = LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased')\n",
        "words = [\"Hello\", \"world\"]\n",
        "normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]\n",
        "token_boxes = []\n",
        "for word, box in zip(words, normalized_word_boxes):\n",
        "    word_tokens = tokenizer.tokenize(word)\n",
        "    token_boxes.extend([box] * len(word_tokens))\n",
        "# add bounding boxes of cls + sep tokens\n",
        "token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]\n",
        "encoding = tokenizer(' '.join(words), return_tensors=\"pt\")\n",
        "input_ids = encoding[\"input_ids\"]\n",
        "attention_mask = encoding[\"attention_mask\"]\n",
        "token_type_ids = encoding[\"token_type_ids\"]\n",
        "bbox = torch.tensor([token_boxes])\n",
        "sequence_label = torch.tensor([1])\n",
        "outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
        "                labels=sequence_label)\n",
        "loss = outputs.loss\n",
        "logits = outputs.logits\n",
        "logits"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at microsoft/layoutlm-base-uncased were not used when initializing LayoutLMForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']\n",
            "- This IS expected if you are initializing LayoutLMForSequenceClassification 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 LayoutLMForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of LayoutLMForSequenceClassification were not initialized from the model checkpoint at microsoft/layoutlm-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.1121, -0.0101]], grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmMWNWwmpAzd"
      },
      "source": [
        "from transformers import LayoutLMTokenizer, LayoutLMForMaskedLM\n",
        "import torch\n",
        "tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')\n",
        "model = LayoutLMForMaskedLM.from_pretrained('microsoft/layoutlm-base-uncased')\n",
        "words = [\"Hello\", \"[MASK]\"]\n",
        "normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]\n",
        "token_boxes = []\n",
        "for word, box in zip(words, normalized_word_boxes):\n",
        "    word_tokens = tokenizer.tokenize(word)\n",
        "    token_boxes.extend([box] * len(word_tokens))\n",
        "# add bounding boxes of cls + sep tokens\n",
        "token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]\n",
        "encoding = tokenizer(' '.join(words), return_tensors=\"pt\")\n",
        "input_ids = encoding[\"input_ids\"]\n",
        "attention_mask = encoding[\"attention_mask\"]\n",
        "token_type_ids = encoding[\"token_type_ids\"]\n",
        "bbox = torch.tensor([token_boxes])\n",
        "labels = tokenizer(\"Hello world\", return_tensors=\"pt\")[\"input_ids\"]\n",
        "outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
        "                labels=labels)\n",
        "loss = outputs.loss"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z91Z9IWIpOrD",
        "outputId": "83306706-5d9a-4e5d-80db-6166274583a0"
      },
      "source": [
        "outputs"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MaskedLMOutput([('loss', tensor(11.0031, grad_fn=<NllLossBackward>)),\n",
              "                ('logits',\n",
              "                 tensor([[[ 0.5534,  0.1442,  0.0901,  ..., -0.0590,  0.8132,  0.7086],\n",
              "                          [-1.6607, -0.8606, -1.0854,  ...,  0.2116,  0.2717, -0.9807],\n",
              "                          [-1.4802, -0.6127, -1.2147,  ...,  0.5059,  0.1404, -1.1180],\n",
              "                          [ 0.5582,  0.1473,  0.0929,  ..., -0.0526,  0.8160,  0.7153]]],\n",
              "                        grad_fn=<AddBackward0>))])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJg7TuOgpQH7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}