Bert_base_Vietnamese_uncased.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/quocthang0507/VietnameseNaturalLanguageProcessing/blob/main/Bert_base_Vietnamese_uncased.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3pLZwafgkOd0"
      },
      "source": [
        "https://huggingface.co/trituenhantaoio/bert-base-vietnamese-uncased/tree/main\n",
        "\n",
        "bert-base-vietnamese-uncased citation:\n",
        "```\n",
        "@article{ttnt2020bert,\n",
        "  title={Vietnamese BERT: Pretrained on News and Wiki},\n",
        "  author={trituenhantao.io},\n",
        "  year = {2020},\n",
        "  publisher = {GitHub},\n",
        "  journal = {GitHub repository},\n",
        "  howpublished = {\\url{https://github.com/trituenhantaoio/vn-bert-base-uncased}},\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "l01QjLC29e_e",
        "outputId": "5fc67a56-c9e8-4bac-a6f9-867d01fda993"
      },
      "source": [
        "!pip install transformers\n",
        "from transformers import BertForSequenceClassification\n",
        "from transformers import BertTokenizer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.9.1-py3-none-any.whl (2.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6 MB 8.4 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
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            "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
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            "\u001b[?25hCollecting sacremoses\n",
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            "Installing collected packages: tokenizers, sacremoses, pyyaml, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
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            "Successfully installed huggingface-hub-0.0.12 pyyaml-5.4.1 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.9.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNYaC9bd-Q7l"
      },
      "source": [
        "import torch\n",
        "import itertools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
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            "6717f77ca8524234bd9c643b0817133d",
            "2385adb7433d4d63b5aa7fb8ecbb3f75",
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            "a2906956cbe544cf9fdde3663296fa30",
            "4718581ee603496f8ba0fce5eb29f058",
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            "5fc005fd317541af9d667446efe39520",
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            "71a76950f2074581922d092c21b549c7",
            "cab23110bb654962a347aa5fea4c8f7d",
            "99fc123e44854405837389d9d5509540",
            "4acb2ad5902a4063856778f8528cbb54",
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            "f1022e0ee56947b083fc7fd7ff5efa8f",
            "5a66c0a03a094958bfc9e495e6cb9b30",
            "6389bc6573094680994ce938835d2261"
          ]
        },
        "id": "hzi7dsXQ9mZ6",
        "outputId": "bfa3b7b8-e469-4a45-ae5b-1b40e352b646"
      },
      "source": [
        "model = BertForSequenceClassification.from_pretrained(\"trituenhantaoio/bert-base-vietnamese-uncased\")\n",
        "tokenizer = BertTokenizer.from_pretrained(\"trituenhantaoio/bert-base-vietnamese-uncased\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66326d61fdaa493c8afa828ba0e230f2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=846.0, 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": "2385adb7433d4d63b5aa7fb8ecbb3f75",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442550979.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at trituenhantaoio/bert-base-vietnamese-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": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=207410.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3a4e60a722647f3a42399c622a35c48",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cab23110bb654962a347aa5fea4c8f7d",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=238.0, style=ProgressStyle(description_…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iXNY4uj9sa3"
      },
      "source": [
        "src = 'Xin chào tân sinh viên trường Đại học Đà Lạt'\n",
        "tgt = 'Welcome Da Lat University\\'s freshman'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1SGJ3PWK98Xn",
        "outputId": "58fdea1e-18bd-4be7-e66e-99ee746f52a7"
      },
      "source": [
        "sent_src, sent_tgt = src.strip().split(), tgt.strip().split()\n",
        "token_src, token_tgt = [tokenizer.tokenize(word) for word in sent_src], [tokenizer.tokenize(word) for word in sent_tgt]\n",
        "wid_src, wid_tgt = [tokenizer.convert_tokens_to_ids(x) for x in token_src], [tokenizer.convert_tokens_to_ids(x) for x in token_tgt]\n",
        "ids_src, ids_tgt = tokenizer.prepare_for_model(list(itertools.chain(*wid_src)), return_tensors='pt', model_max_length=tokenizer.model_max_length, truncation=True)['input_ids'], tokenizer.prepare_for_model(list(itertools.chain(*wid_tgt)), return_tensors='pt', truncation=True, model_max_length=tokenizer.model_max_length)['input_ids']\n",
        "sub2word_map_src = []\n",
        "for i, word_list in enumerate(token_src):\n",
        "  sub2word_map_src += [i for x in word_list]\n",
        "sub2word_map_tgt = []\n",
        "for i, word_list in enumerate(token_tgt):\n",
        "  sub2word_map_tgt += [i for x in word_list]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "Z1ERBaas-YV2",
        "outputId": "910a6d30-c073-4169-d83f-748c37c0d2f7"
      },
      "source": [
        "align_layer = 8\n",
        "threshold = 1e-3\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "  out_src = model(ids_src.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]\n",
        "  out_tgt = model(ids_tgt.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]\n",
        "\n",
        "  dot_prod = torch.matmul(out_src, out_tgt.transpose(-1, -2))\n",
        "\n",
        "  softmax_srctgt = torch.nn.Softmax(dim=-1)(dot_prod)\n",
        "  softmax_tgtsrc = torch.nn.Softmax(dim=-2)(dot_prod)\n",
        "\n",
        "  softmax_inter = (softmax_srctgt > threshold)*(softmax_tgtsrc > threshold)\n",
        "\n",
        "align_subwords = torch.nonzero(softmax_inter, as_tuple=False)\n",
        "align_words = set()\n",
        "for i, j in align_subwords:\n",
        "  align_words.add( (sub2word_map_src[i], sub2word_map_tgt[j]) ) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-ab4120d43bdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mout_src\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids_src\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0malign_layer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0mout_tgt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids_tgt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0malign_layer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m   1887\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0minner_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1889\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1891\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
          ]
        }
      ]
    }
  ]
}