PhoBert.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/quocthang0507/VietnameseNaturalLanguageProcessing/blob/main/PhoBert.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "source": [
        "PhoBERT Github repo: https://github.com/VinAIResearch/PhoBERT\n",
        "\n",
        "Citation:\n",
        "```\n",
        "@inproceedings{phobert,\n",
        " title     = {{PhoBERT: Pre-trained  language models for Vietnamese}},\n",
        " author    = {Dat Quoc Nguyen and Anh Tuan Nguyen},\n",
        " booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},\n",
        " year      = {2020},\n",
        " pages     = {1037--1042}\n",
        "}\n",
        "```"
      ]
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        "outputId": "d9d535a9-a736-410a-ee19-7d85b35d6b7e"
      },
      "source": [
        "!pip install transformers\n",
        "import torch\n",
        "from transformers import AutoModel, AutoTokenizer"
      ],
      "execution_count": 1,
      "outputs": [
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            "Collecting transformers\n",
            "  Downloading transformers-4.9.2-py3-none-any.whl (2.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6 MB 7.6 MB/s \n",
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            "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
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            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 46.5 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 53.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
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            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.45-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 52.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "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",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.0.12 pyyaml-5.4.1 sacremoses-0.0.45 tokenizers-0.10.3 transformers-4.9.2\n"
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            "Some weights of the model checkpoint at vinai/phobert-base were not used when initializing RobertaModel: ['lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
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        "tokenizer = AutoTokenizer.from_pretrained('vinai/phobert-base')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f17bd2a5d1dc42f6b62fc7914f182ea5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=895321.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "015620d4a2da47f9972f39f8cf52678e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1135173.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHcoxdNJ27X2"
      },
      "source": [
        "text = \"Xin chào\""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blqhBvdx3MYP"
      },
      "source": [
        "input_ids = torch.tensor([tokenizer.encode(text)])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izz3G1lV3TC0"
      },
      "source": [
        "align_layer = 8\n",
        "threshold = 1e-3\n",
        "with torch.no_grad():\n",
        "    features = phobert(input_ids)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3qPAcNX3YRw",
        "outputId": "cc24a5a4-cd8f-4872-e8a2-7ea873d7ece7"
      },
      "source": [
        "print(tokenizer(text))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'input_ids': [0, 3280, 3683, 2], 'token_type_ids': [0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1]}\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}