11: Train_Custom_Tokenizer_and_Mdoel(Roberta)_using_HuggingFace.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_BERT/blob/main/11%3A%20Train_Custom_Tokenizer_and_Mdoel(Roberta)_using_HuggingFace.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oK7PPVm2XBgr"
      },
      "source": [
        "## 1. Find a dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Up82H83l5Bv9"
      },
      "outputs": [],
      "source": [
        "!!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YMHttZKX5Nqh"
      },
      "outputs": [],
      "source": [
        "import datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXJDJow25Q-K",
        "outputId": "eb70eb5d-bd4b-41ee-aaf5-68cd103d093b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8083\n"
          ]
        }
      ],
      "source": [
        "all_ds = datasets.list_datasets()\n",
        "print(len(all_ds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW0BbRP95UF_",
        "outputId": "1188c642-e6a6-4027-8995-987e7fec9bb7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['acronym_identification',\n",
              " 'ade_corpus_v2',\n",
              " 'adversarial_qa',\n",
              " 'aeslc',\n",
              " 'afrikaans_ner_corpus',\n",
              " 'ag_news',\n",
              " 'ai2_arc',\n",
              " 'air_dialogue',\n",
              " 'ajgt_twitter_ar',\n",
              " 'allegro_reviews',\n",
              " 'allocine',\n",
              " 'alt',\n",
              " 'amazon_polarity',\n",
              " 'amazon_reviews_multi',\n",
              " 'amazon_us_reviews',\n",
              " 'ambig_qa',\n",
              " 'americas_nli',\n",
              " 'ami',\n",
              " 'amttl',\n",
              " 'anli']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_ds[:20]"
      ]
    },
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        {
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            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.58k [00:00<?, ?B/s]"
            ]
          },
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          "output_type": "display_data"
        },
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            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/359k [00:00<?, ?B/s]"
            ]
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset oscar/unshuffled_deduplicated_la (download: 3.26 MiB, generated: 8.46 MiB, post-processed: Unknown size, total: 11.72 MiB) to /root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_la/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2...\n"
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              "Downloading data:   0%|          | 0.00/81.0 [00:00<?, ?B/s]"
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          },
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              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "Downloading data:   0%|          | 0.00/3.42M [00:00<?, ?B/s]"
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            },
            "text/plain": [
              "Generating train split:   0%|          | 0/18808 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset oscar downloaded and prepared to /root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_la/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2. Subsequent calls will reuse this data.\n"
          ]
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              "version_minor": 0
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "dataset = datasets.load_dataset('oscar', 'unshuffled_deduplicated_la')\n",
        "##https://huggingface.co/datasets/oscar/viewer/unshuffled_deduplicated_la/train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_Vgtl6N6Nlu",
        "outputId": "42a36ef5-0efa-4839-a6c6-c1fe59d59b97"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'text'],\n",
              "        num_rows: 18808\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxRqt84H6SMs",
        "outputId": "20d1f1b1-6d4a-4463-c9fe-590e101f5a2c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'id': 1,\n",
              " 'text': 'Sicut enim maius est illuminare quam lucere solum, ita maius est contemplata aliis tradere quam solum contemplari.'}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train'][1]"
      ]
    },
    {
      "cell_type": "code",
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        "id": "kqCzM05D6cZ6",
        "outputId": "c36c5400-2ac3-4adf-fdf3-19308d759af6"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9288d8ebcbd34c1082949c7fda591bec",
              "version_major": 2,
              "version_minor": 0
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              "  0%|          | 0/18808 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
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      ],
      "source": [
        "from tqdm.auto import tqdm  # for our loading bar\n",
        "\n",
        "text_data = []\n",
        "file_count = 0\n",
        "\n",
        "for sample in tqdm(dataset['train']):\n",
        "    # remove newline characters from each sample as we need to use exclusively as seperators\n",
        "    sample = sample['text'].replace('\\n', '')\n",
        "    text_data.append(sample)\n",
        "    if len(text_data) == 5_000:\n",
        "        # once we hit the 5K mark, save to file\n",
        "        with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
        "            fp.write('\\n'.join(text_data))\n",
        "        text_data = []\n",
        "        file_count += 1\n",
        "# after saving in 5K chunks, we will have ~3808 leftover samples, we save those now too\n",
        "with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
        "    fp.write('\\n'.join(text_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnWARsPY6sND",
        "outputId": "964ef1b7-0a21-420f-85d2-0990a0612292"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/data/text_3.txt',\n",
              " '/content/data/text_1.txt',\n",
              " '/content/data/text_0.txt',\n",
              " '/content/data/text_2.txt']"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "paths = [str(x) for x in Path('/content/data/').glob('**/*.txt')]\n",
        "paths"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-kkz81OY6xH"
      },
      "source": [
        "## 2. Train a tokenizer\n",
        "\n",
        "We choose to train a byte-level Byte-pair encoding tokenizer (the same as GPT-2), with the same special tokens as RoBERTa. Let’s arbitrarily pick its size to be 52\n",
        ",000.\n",
        "\n",
        "We recommend training a byte-level BPE (rather than let’s say, a WordPiece tokenizer like BERT) because it will start building its vocabulary from an alphabet of single bytes, so all words will be decomposable into tokens (no more `<unk>` tokens!).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xaS5N5CiC4q4"
      },
      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mmw6htwFK4a",
        "outputId": "814d30d1-8704-4695-8b30-cd26703166f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "paths = [str(x) for x in Path(\".\").glob(\"**/*.txt\")]\n",
        "print(len(paths))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IMnymRDLe0hi"
      },
      "outputs": [],
      "source": [
        "from tokenizers import ByteLevelBPETokenizer\n",
        "# initialize\n",
        "tokenizer = ByteLevelBPETokenizer()\n",
        "# and train\n",
        "tokenizer.train(files=paths, vocab_size=30000, min_frequency=2,\n",
        "                special_tokens=['<s>', '<pad>', '</s>', '<unk>', '<mask>'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Ei7bqpRf1LH"
      },
      "source": [
        "Now let's save files to disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIS-irI0f32P",
        "outputId": "c19bbbc4-0899-49b5-b3ff-4370c25ee88e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['LaRoBERTo/vocab.json', 'LaRoBERTo/merges.txt']"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title\n",
        "!mkdir LaRoBERTo\n",
        "tokenizer.save_model(\"LaRoBERTo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tKVWB8WShT-z"
      },
      "outputs": [],
      "source": [
        "from transformers import RobertaTokenizerFast\n",
        "\n",
        "tokenizer = RobertaTokenizerFast.from_pretrained('LaRoBERTo')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3Ye27nchfzq",
        "outputId": "f00e7840-70af-475b-a6a3-7c2d9b2a573a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': [0, 10046, 411, 2118, 297, 21702, 350, 24799, 980, 16, 585, 2118, 297, 27869, 1360, 10251, 350, 980, 23314, 18, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer('Sicut enim maius est illuminare quam lucere solum, ita maius est contemplata aliis tradere quam solum contemplari.', max_length=512, padding='max_length', truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VRS9aojc8LQP"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQpUC_CDhnWW"
      },
      "source": [
        "## 3. Train a language model from scratch\n",
        "\n",
        "**Update:** This section follows along the [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py) script, using our new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) directly. Feel free to pick the approach you like best.\n",
        "\n",
        "> We’ll train a RoBERTa-like model, which is a BERT-like with a couple of changes (check the [documentation](https://huggingface.co/transformers/model_doc/roberta.html) for more details).\n",
        "\n",
        "As the model is BERT-like, we’ll train it on a task of *Masked language modeling*, i.e. the predict how to fill arbitrary tokens that we randomly mask in the dataset. This is taken care of by the example script.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kD140sFjh0LQ",
        "outputId": "869b3603-fa80-4919-877e-bf6797bc7c52"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Aug  4 18:36:11 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   36C    P8     8W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# Check that we have a GPU\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNZZs-r6iKAV",
        "outputId": "c4cb37da-abd2-4660-c2eb-4ba2242f0710"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check that PyTorch sees it\n",
        "import torch\n",
        "torch.cuda.is_available()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0qQzgrBi1OX"
      },
      "source": [
        "### We'll define the following config for the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LTXXutqeDzPi"
      },
      "outputs": [],
      "source": [
        "from transformers import RobertaConfig\n",
        "\n",
        "config = RobertaConfig(\n",
        "    vocab_size=52_000,\n",
        "    max_position_embeddings=514,\n",
        "    num_attention_heads=12,\n",
        "    num_hidden_layers=6,\n",
        "    type_vocab_size=1,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAwQ82JiE5pi"
      },
      "source": [
        "Now let's re-create our tokenizer in transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "4keFBUjQFOD1",
        "outputId": "2a110324-73c1-4811-f591-97bb6f04686e"
      },
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mget_file_from_repo\u001b[0;34m(path_or_repo, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only)\u001b[0m\n\u001b[1;32m    710\u001b[0m             \u001b[0mlocal_files_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_files_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 711\u001b[0;31m             \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\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    712\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)\u001b[0m\n\u001b[1;32m    291\u001b[0m             \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 292\u001b[0;31m             \u001b[0mlocal_files_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_files_only\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    293\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0metag_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             \u001b[0m_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\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    503\u001b[0m             \u001b[0metag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"X-Linked-Etag\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ETag\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m_raise_for_status\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m    417\u001b[0m         raise RepositoryNotFoundError(\n\u001b[0;32m--> 418\u001b[0;31m             \u001b[0;34mf\"401 Client Error: Repository not found for url: {response.url}. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m             \u001b[0;34m\"If the repo is private, make sure you are authenticated.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 401 Client Error: Repository not found for url: https://huggingface.co/EsperBERTo/resolve/main/tokenizer_config.json. If the repo is private, make sure you are authenticated.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-440f43255e58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRobertaTokenizerFast\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRobertaTokenizerFast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./EsperBERTo\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)\u001b[0m\n\u001b[1;32m   1704\u001b[0m                     \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1705\u001b[0m                     \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1706\u001b[0;31m                     \u001b[0mlocal_files_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_files_only\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   1707\u001b[0m                 )\n\u001b[1;32m   1708\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mresolved_config_file\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mget_file_from_repo\u001b[0;34m(path_or_repo, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mRepositoryNotFoundError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m         raise EnvironmentError(\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0;34mf\"{path_or_repo} is not a local folder and is not a valid model identifier \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;34m\"listed on 'https://huggingface.co/models'\\nIf this is a private repository, make sure to \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;34m\"pass a token having permission to this repo with `use_auth_token` or log in with \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: ./EsperBERTo is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`."
          ]
        }
      ],
      "source": [
        "from transformers import RobertaTokenizerFast\n",
        "\n",
        "tokenizer = RobertaTokenizerFast.from_pretrained(\"./EsperBERTo\", max_len=512)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yNCw-3hFv9h"
      },
      "source": [
        "Finally let's initialize our model.\n",
        "\n",
        "**Important:**\n",
        "\n",
        "As we are training from scratch, we only initialize from a config, not from an existing pretrained model or checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BzMqR-dzF4Ro"
      },
      "outputs": [],
      "source": [
        "from transformers import RobertaForMaskedLM\n",
        "\n",
        "model = RobertaForMaskedLM(config=config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jU6JhBSTKiaM"
      },
      "outputs": [],
      "source": [
        "model.num_parameters()\n",
        "# => 84 million parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBtUHRMliOLM"
      },
      "source": [
        "### Now let's build our training Dataset\n",
        "\n",
        "We'll build our dataset by applying our tokenizer to our text file.\n",
        "\n",
        "Here, as we only have one text file, we don't even need to customize our `Dataset`. We'll just use the `LineByLineDataset` out-of-the-box."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "GlvP_A-THEEl",
        "outputId": "d0320b04-0a13-4cca-b26d-9d9197510a1c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/data/datasets/language_modeling.py:125: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 1.69 s, sys: 36.5 ms, total: 1.72 s\n",
            "Wall time: 975 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from transformers import LineByLineTextDataset\n",
        "\n",
        "dataset = LineByLineTextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "    file_path=\"./oscar.eo.txt\",\n",
        "    block_size=128,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDLs73HcIHk5"
      },
      "source": [
        "Like in the [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script, we need to define a data_collator.\n",
        "\n",
        "This is just a small helper that will help us batch different samples of the dataset together into an object that PyTorch knows how to perform backprop on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zTgWPa9Dipk2"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri2BIQKqjfHm"
      },
      "source": [
        "### Finally, we are all set to initialize our Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YpvnFFmZJD-N"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./EsperBERTo\",\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=1,\n",
        "    per_gpu_train_batch_size=64,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        "    prediction_loss_only=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=dataset,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6sASa36Nf-N"
      },
      "source": [
        "### Start training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "VmaHZXzmkNtJ",
        "outputId": "31721ef0-308d-4de1-d0d9-e9dc237d464d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: 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",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 5002\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 64\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 64\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 79\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='79' max='79' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [79/79 01:28, Epoch 1/1]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 1min 29s, sys: 478 ms, total: 1min 30s\n",
            "Wall time: 1min 32s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=79, training_loss=9.429111915298655, metrics={'train_runtime': 92.1723, 'train_samples_per_second': 54.268, 'train_steps_per_second': 0.857, 'total_flos': 165850234208256.0, 'train_loss': 9.429111915298655, 'epoch': 1.0})"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZkooHz1-_2h"
      },
      "source": [
        "#### 🎉 Save final model (+ tokenizer + config) to disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "QDNgPls7_l13",
        "outputId": "54551201-4d04-4bef-fe6f-e6ef7ce68101"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to ./EsperBERTo\n",
            "Configuration saved in ./EsperBERTo/config.json\n",
            "Model weights saved in ./EsperBERTo/pytorch_model.bin\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model(\"./EsperBERTo\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0caceCy_p1-"
      },
      "source": [
        "## 4. Check that the LM actually trained"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iIQJ8ND_AEhl"
      },
      "source": [
        "Aside from looking at the training and eval losses going down, the easiest way to check whether our language model is learning anything interesting is via the `FillMaskPipeline`.\n",
        "\n",
        "Pipelines are simple wrappers around tokenizers and models, and the 'fill-mask' one will let you input a sequence containing a masked token (here, `<mask>`) and return a list of the most probable filled sequences, with their probabilities.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ltXgXyCbAJLY",
        "outputId": "e974a26b-c47e-461a-b955-01afa9f54bea"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file ./EsperBERTo/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"./EsperBERTo\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.21.1\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 52000\n",
            "}\n",
            "\n",
            "loading configuration file ./EsperBERTo/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"./EsperBERTo\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.21.1\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 52000\n",
            "}\n",
            "\n",
            "loading weights file ./EsperBERTo/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing RobertaForMaskedLM.\n",
            "\n",
            "All the weights of RobertaForMaskedLM were initialized from the model checkpoint at ./EsperBERTo.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForMaskedLM for predictions without further training.\n",
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "loading configuration file ./EsperBERTo/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"./EsperBERTo\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.21.1\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 52000\n",
            "}\n",
            "\n",
            "Didn't find file ./EsperBERTo/vocab.json. We won't load it.\n",
            "Didn't find file ./EsperBERTo/merges.txt. We won't load it.\n",
            "Didn't find file ./EsperBERTo/tokenizer.json. We won't load it.\n",
            "Didn't find file ./EsperBERTo/added_tokens.json. We won't load it.\n",
            "Didn't find file ./EsperBERTo/special_tokens_map.json. We won't load it.\n",
            "Didn't find file ./EsperBERTo/tokenizer_config.json. We won't load it.\n"
          ]
        },
        {
          "ename": "OSError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-9c8fc4a65749>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"fill-mask\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./EsperBERTo\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./EsperBERTo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, framework, revision, use_fast, use_auth_token, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    699\u001b[0m             tokenizer = AutoTokenizer.from_pretrained(\n\u001b[0;32m--> 700\u001b[0;31m                 \u001b[0mtokenizer_identifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_fast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_fast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_from_pipeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtokenizer_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    701\u001b[0m             )\n\u001b[1;32m    702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/auto/tokenization_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0mtokenizer_class_py\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer_class_fast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTOKENIZER_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\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    608\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtokenizer_class_fast\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0muse_fast\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtokenizer_class_py\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\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--> 609\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mtokenizer_class_fast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    610\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[1;32m    611\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtokenizer_class_py\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)\u001b[0m\n\u001b[1;32m   1788\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_file_name\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfull_file_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresolved_vocab_files\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m             raise EnvironmentError(\n\u001b[0;32m-> 1790\u001b[0;31m                 \u001b[0;34mf\"Can't load tokenizer for '{pretrained_model_name_or_path}'. If you were trying to load it from \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1791\u001b[0m                 \u001b[0;34m\"'https://huggingface.co/models', make sure you don't have a local directory with the same name. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1792\u001b[0m                 \u001b[0;34mf\"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Can't load tokenizer for './EsperBERTo'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure './EsperBERTo' is the correct path to a directory containing all relevant files for a RobertaTokenizerFast tokenizer."
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "fill_mask = pipeline(\n",
        "    \"fill-mask\",\n",
        "    model=\"./EsperBERTo\",\n",
        "    tokenizer=\"./EsperBERTo\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UIvgZ3S6AO0z"
      },
      "outputs": [],
      "source": [
        "# The sun <mask>.\n",
        "# =>\n",
        "\n",
        "fill_mask(\"La suno <mask>.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0qCyyhNAWZi"
      },
      "source": [
        "Ok, simple syntax/grammar works. Let’s try a slightly more interesting prompt:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YZ9HSQxAAbme"
      },
      "outputs": [],
      "source": [
        "fill_mask(\"Jen la komenco de bela <mask>.\")\n",
        "\n",
        "# This is the beginning of a beautiful <mask>.\n",
        "# =>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RsGaD1qAfLP"
      },
      "source": [
        "## 5. Share your model 🎉"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5oESe8djApQw"
      },
      "source": [
        "Finally, when you have a nice model, please think about sharing it with the community:\n",
        "\n",
        "- upload your model using the CLI: `transformers-cli upload`\n",
        "- write a README.md model card and add it to the repository under `model_cards/`. Your model card should ideally include:\n",
        "    - a model description,\n",
        "    - training params (dataset, preprocessing, hyperparameters), \n",
        "    - evaluation results,\n",
        "    - intended uses & limitations\n",
        "    - whatever else is helpful! 🤓\n",
        "\n",
        "### **TADA!**\n",
        "\n",
        "➡️ Your model has a page on http://huggingface.co/models and everyone can load it using `AutoModel.from_pretrained(\"username/model_name\")`.\n",
        "\n",
        "[![tb](https://huggingface.co/blog/assets/01_how-to-train/model_page.png)](https://huggingface.co/julien-c/EsperBERTo-small)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aw9ifsgqBI2o"
      },
      "source": [
        "If you want to take a look at models in different languages, check https://huggingface.co/models\n",
        "\n",
        "[![all models](https://huggingface.co/front/thumbnails/models.png)](https://huggingface.co/models)\n"
      ]
    }
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