ROBERTA-xlm.ipynb

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    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lucia1299/BERTolde/blob/main/ROBERTA-xlm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wkVbkFwj66F7"
      },
      "outputs": [],
      "source": [
        "!pip install transformers datasets tokenizers seqeval -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "import numpy as np\n",
        "from transformers import XLMRobertaTokenizerFast, DataCollatorForTokenClassification, XLMRobertaForTokenClassification"
      ],
      "metadata": {
        "id": "fz26Mt5M7Kdt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, ClassLabel, Features, Sequence, Value, DatasetDict\n",
        "\n",
        "def creationdictionary(data, posdict, nerdict, pos_tags_list, ner_tags_list):\n",
        "    container_list = []\n",
        "\n",
        "    while data:\n",
        "        container_dict = {}\n",
        "        pos_list = []\n",
        "        ner_list = []\n",
        "        words_list = []\n",
        "\n",
        "        index_of_empty_list = data.index([]) if [] in data else len(data)\n",
        "        tokenized_data1 = data[0: index_of_empty_list]\n",
        "        for n in tokenized_data1:\n",
        "            if 'id' not in container_dict:\n",
        "                container_dict['id'] = 0\n",
        "            else:\n",
        "                container_dict['id'] += 1\n",
        "            if n[1] in posdict:\n",
        "                pos_list.append(posdict[n[1]])\n",
        "            if n[3] in nerdict:\n",
        "                ner_list.append(nerdict[n[3]])\n",
        "            words_list.append(n[0])\n",
        "\n",
        "        container_dict['pos'] = pos_list\n",
        "        container_dict['ner_tags'] = ner_list\n",
        "        container_dict['tokens'] = words_list\n",
        "        container_dict['issueid'] = tokenized_data1[0][2]\n",
        "        container_dict['id'] = str(container_dict['id'])\n",
        "\n",
        "        container_list.append(container_dict)\n",
        "\n",
        "        data = data[index_of_empty_list + 1:]\n",
        "\n",
        "    container_dict = {key: [d[key] for d in container_list] for key in container_list[0]}\n",
        "\n",
        "    ds = Dataset.from_dict(container_dict, features=Features({\n",
        "        'pos': Sequence(ClassLabel(names=pos_tags_list)),\n",
        "        'ner_tags': Sequence(ClassLabel(names=ner_tags_list)),\n",
        "        'tokens': Sequence(Value(dtype='string')),\n",
        "        'issueid': Value(dtype='string'),\n",
        "        'id': Value(dtype='string')\n",
        "        }))\n",
        "\n",
        "    return ds\n",
        "\n",
        "def posconversion(data):\n",
        "    posdict = {}\n",
        "    set1 = set()\n",
        "    for i in data:\n",
        "        if len(i) >= 1:\n",
        "            pos_tag = i[1]\n",
        "            set1.add(pos_tag)\n",
        "    pos_tags_list = list(set1)\n",
        "    v = 0\n",
        "    for i in set1:\n",
        "        posdict[i] = v\n",
        "        v +=1\n",
        "    return posdict, pos_tags_list\n",
        "\n",
        "def nerconversion(data):\n",
        "    nerdict = {}\n",
        "    set1 = set()\n",
        "    for i in data:\n",
        "        if len(i) >= 4:\n",
        "            ner_tag = i[3]\n",
        "            set1.add(ner_tag)\n",
        "    ner_tags_list = list(set1)\n",
        "    v = 0\n",
        "    for i in set1:\n",
        "        nerdict[i] = v\n",
        "        v +=1\n",
        "    return nerdict, ner_tags_list\n",
        "\n",
        "file_path_training = '/content/I-CAB-evalita09-NER-training.iob2'\n",
        "file_path_test = '/content/I-CAB-evalita09-NER-test.iob2'\n",
        "\n",
        "def read_iob_data(file_path):\n",
        "     with open(file_path, 'r', encoding='ISO-8859-1') as file:\n",
        "         iob_data = file.readlines()\n",
        "\n",
        "     data = [line.strip().split() for line in iob_data]\n",
        "     return data\n",
        "\n",
        "data_training = read_iob_data(file_path_training)\n",
        "posdict, pos_tags_list = posconversion(data_training)\n",
        "nerdict, ner_tags_list = nerconversion(data_training)\n",
        "result_training = creationdictionary(data_training, posdict, nerdict, pos_tags_list, ner_tags_list)\n",
        "\n",
        "data_test = read_iob_data(file_path_test)\n",
        "posdict, pos_tags_list = posconversion(data_test)\n",
        "nerdict, ner_tags_list = nerconversion(data_test)\n",
        "result_test = creationdictionary(data_test, posdict, nerdict, pos_tags_list, ner_tags_list)\n",
        "\n",
        "main_dataset = DatasetDict({\"train\": result_training, \"test\": result_test})\n",
        "print(main_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkAtprgH7dJ6",
        "outputId": "3f5a922f-1396-412d-9a33-1ed4694f5f2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['id', 'pos', 'ner_tags', 'tokens', 'issueid'],\n",
            "        num_rows: 11227\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['id', 'pos', 'ner_tags', 'tokens', 'issueid'],\n",
            "        num_rows: 4136\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_gzLGfR79Sb",
        "outputId": "846ee80a-81f3-4da5-e620-8b3f8b4cad44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': (11227, 5), 'test': (4136, 5)}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_dataset['train'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltsXMzzE8DAN",
        "outputId": "b8808cd1-1283-4b54-f5fc-96df5f6d531a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '6',\n",
              " 'pos': [19, 27, 19, 39, 22, 30, 54],\n",
              " 'ner_tags': [0, 0, 0, 0, 0, 0, 2],\n",
              " 'tokens': ['Latte', 'al', 'seno', ',', 'sos', 'di', 'Pedrotti'],\n",
              " 'issueid': 'adige20041007_id413942'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_dataset['train'].features['ner_tags']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWHpzENC8L81",
        "outputId": "0e9a2035-3497-4d49-8bcf-6a4d3ac67a7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(names=['O', 'I-LOC', 'B-PER', 'B-ORG', 'I-PER', 'I-GPE', 'I-ORG', 'B-LOC', 'B-GPE'], id=None), length=-1, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = XLMRobertaTokenizerFast.from_pretrained(\"xlm-roberta-base\", add_prefix_space=True)\n",
        "\n",
        "#add_prefix_space needs to be used because this is how the instatiation of RobertaTokenizerFast works"
      ],
      "metadata": {
        "id": "ACQAL-EI8TZi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example_text = main_dataset['train'][1]\n",
        "\n",
        "tokenized_input = tokenizer(example_text['tokens'], is_split_into_words=True)\n",
        "\n",
        "tokens = tokenizer.convert_ids_to_tokens(tokenized_input['input_ids'])\n",
        "\n",
        "word_ids = tokenized_input.word_ids()\n",
        "\n",
        "#ValueError: word_ids() is not available when using non-fast tokenizers (e.g. instance of a `XxxTokenizerFast` class).\n",
        "#this is why we need to use TokenizersFast also for Roberta\n",
        "\n",
        "word_ids\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhXTKUH28pgm",
        "outputId": "71224629-442a-4e38-b5b8-2a7912b72c5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, 0, 0, 1, 2, 2, 3, 3, 4, 5, 6, 6, None]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(tokenized_input['input_ids'])\n",
        "tokens\n",
        "\n",
        "#<s> and </s> are mapped to NONE because the they are \"special tokens\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwgNOOGZ_tfM",
        "outputId": "1810c157-7ae4-48b0-d6f6-d5984ad0a811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<s>',\n",
              " '▁La',\n",
              " 'tte',\n",
              " '▁al',\n",
              " '▁se',\n",
              " 'no',\n",
              " '▁',\n",
              " ',',\n",
              " '▁sos',\n",
              " '▁di',\n",
              " '▁Pedro',\n",
              " 'tti',\n",
              " '</s>']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(example_text['ner_tags']), len(tokenized_input['input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpE2Vuj79SWb",
        "outputId": "08385965-b246-4dd9-f09f-28b646a3bd5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_align_labels(example, label_all_tokens =True):\n",
        "    tokenized_input = tokenizer(example['tokens'], truncation=True, is_split_into_words=True)\n",
        "\n",
        "    labels = []\n",
        "\n",
        "    for i, label in enumerate(example['ner_tags']):\n",
        "        word_ids = tokenized_input.word_ids(batch_index=i)\n",
        "        #it returns a list indicating the word corresponding to each token\n",
        "        previous_word_idx = None\n",
        "\n",
        "        label_ids =[]\n",
        "\n",
        "        for word_idx in word_ids:\n",
        "            if word_idx is None:\n",
        "                #special tokens\n",
        "                label_ids.append(-100)\n",
        "            elif word_idx != previous_word_idx:\n",
        "                #regular case\n",
        "                label_ids.append(label[word_idx])\n",
        "            else:\n",
        "                #subword case\n",
        "                label_ids.append(label[word_idx] if label_all_tokens else -100)\n",
        "            previous_word_idx = word_idx\n",
        "        labels.append(label_ids)\n",
        "    tokenized_input['labels'] = labels\n",
        "    return tokenized_input"
      ],
      "metadata": {
        "id": "wHIlfAFg-jSe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q = tokenize_and_align_labels(main_dataset['train'][4:5])\n",
        "print(q)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WP5L8ha9Bee-",
        "outputId": "99b75896-9488-4b16-ac92-be9324211862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[0, 78792, 747, 137060, 144, 13443, 59932, 159, 5, 54286, 11, 78, 44918, 1298, 45, 986, 18, 76091, 23, 4804, 78244, 6, 4, 45, 291, 3019, 158, 48432, 169124, 6, 4, 45, 108, 2477, 118, 10, 3109, 351, 2338, 565, 15548, 188, 211, 55152, 986, 48557, 6, 176694, 6, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for token, label in zip(tokenizer.convert_ids_to_tokens(q['input_ids'][0]), (q['labels'][0])):\n",
        "    print(f\"{token:_<40} {label}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POVz5UwuB1sH",
        "outputId": "a2f488bb-f1e4-47d2-ae7b-14599cb4891f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>_____________________________________ -100\n",
            "▁Oggi___________________________________ 0\n",
            "▁alle___________________________________ 0\n",
            "▁17.30__________________________________ 0\n",
            "▁al_____________________________________ 0\n",
            "▁Centro_________________________________ 0\n",
            "▁culturale______________________________ 0\n",
            "▁S______________________________________ 0\n",
            "._______________________________________ 0\n",
            "▁Chiar__________________________________ 0\n",
            "a_______________________________________ 0\n",
            "▁si_____________________________________ 0\n",
            "▁parler_________________________________ 0\n",
            "à_______________________________________ 0\n",
            "▁di_____________________________________ 0\n",
            "▁alla___________________________________ 0\n",
            "t_______________________________________ 0\n",
            "tamento_________________________________ 0\n",
            "▁in_____________________________________ 0\n",
            "▁Tre____________________________________ 8\n",
            "ntino___________________________________ 8\n",
            "▁_______________________________________ 0\n",
            ",_______________________________________ 0\n",
            "▁di_____________________________________ 0\n",
            "▁ma_____________________________________ 0\n",
            "mme_____________________________________ 0\n",
            "▁con____________________________________ 0\n",
            "▁tanti__________________________________ 0\n",
            "▁dubbi__________________________________ 0\n",
            "▁_______________________________________ 0\n",
            ",_______________________________________ 0\n",
            "▁di_____________________________________ 0\n",
            "▁ne_____________________________________ 0\n",
            "ona_____________________________________ 0\n",
            "ti______________________________________ 0\n",
            "▁a______________________________________ 0\n",
            "▁cui____________________________________ 0\n",
            "▁non____________________________________ 0\n",
            "▁sempre_________________________________ 0\n",
            "▁è______________________________________ 0\n",
            "▁garanti________________________________ 0\n",
            "to______________________________________ 0\n",
            "▁il_____________________________________ 0\n",
            "▁diritto________________________________ 0\n",
            "▁alla___________________________________ 0\n",
            "▁migliore_______________________________ 0\n",
            "▁_______________________________________ 0\n",
            "alimentazione___________________________ 0\n",
            "▁_______________________________________ 0\n",
            "._______________________________________ 0\n",
            "</s>____________________________________ -100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = main_dataset.map(tokenize_and_align_labels, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "c6bcee86d1664c83aa0a726fdc1f715c",
            "e2fae860014b4956a198a4302f2a7cdd",
            "2284118278534d79aabc437ec0bbf5be",
            "3a5eccaf1f10411dbadbac1b39336a2f",
            "12761fab5adb49d3b2bf057f40149665",
            "3a6a8031374c4f34bc67c947aaaad95c",
            "3e75e40d2cdd4a9abcae1052a79c2c2b",
            "05b2066530de465e93ca3917acba21ab",
            "d675425617ab43b18f5421511cde1fc7",
            "bdfccad9598446c8a02e7fa41555129a",
            "2f2811e53c2140a4adde9af014f1914a",
            "a20c5ba932314258bfbc43d4a399ab5c",
            "7eedc289f78341ea9b6f0415f2577474",
            "d11753b8019547a489356936647e99fc",
            "d6d0136720494d8fb91674d8df4a5d5f",
            "f2bb66c3254646bb996a99c6082f44ac",
            "4ecc7834e99f402791a34683a780514a",
            "82f25febc9b44341a97dc729430f1da3",
            "9be9e98e82c74a858f1b8db960f1b8e5",
            "cf916fcf893c4390bea6094527bef96d",
            "7c0dc9549be6428280fb4cf9196b4cb7",
            "8ac115ac758a4e81b7ecd186cac6e19e"
          ]
        },
        "id": "sCTG0BXkCosA",
        "outputId": "447757c4-9976-4b5a-919f-bd1a06dd2cef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11227 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6bcee86d1664c83aa0a726fdc1f715c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4136 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a20c5ba932314258bfbc43d4a399ab5c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_labels = main_dataset['train'].features['ner_tags'].feature.num_classes\n",
        "num_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhrHaitQDJ7u",
        "outputId": "842ea065-093a-4bb3-cd8b-cc6c693dbbb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = XLMRobertaForTokenClassification.from_pretrained(\"xlm-roberta-base\", num_labels = num_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dedA-YxfC7GA",
        "outputId": "bc37b506-9bff-4313-c357-632943a1c03f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of XLMRobertaForTokenClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ym83mLclFQp",
        "outputId": "4608b686-1945-4b65-daa6-2a67d90d9939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.25.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu118)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install transformers[torch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCWT7BUKlTfq",
        "outputId": "43b17388-0452-4525-bf88-cd87b728d9ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.1)\n",
            "Requirement already satisfied: torch!=1.12.0,>=1.10 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.1.0+cu118)\n",
            "Requirement already satisfied: accelerate>=0.20.3 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.25.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->transformers[torch]) (5.9.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[torch]) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[torch]) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.10->transformers[torch]) (2.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2023.11.17)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[torch]) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.10->transformers[torch]) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, AdamW, get_linear_schedule_with_warmup\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, 12000, 250000)\n",
        "#scheduler (optimizer, num_steps, num_warmup_steps, num_training_steps)"
      ],
      "metadata": {
        "id": "Dnh5Vc-DLJJW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd4bfdb6-66a9-452f-a020-a3cb6224e93f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Number of Training Steps: A training step is one gradient update, where in one step, a batch of examples is processed. The number of training steps indicates how many times the weights of the network are updated. It is often used in the context of specifying the duration of training, and the model is trained for a certain number of steps.\n",
        "*   Number of Epochs: An epoch is a complete pass through the entire training dataset. It consists of one or more batches. The number of epochs defines how many times the learning algorithm will work through the entire training dataset. It is a hyperparameter that is traditionally set to a large number, allowing the learning algorithm to run until the error from the model has been sufficiently minimized."
      ],
      "metadata": {
        "id": "vAysDnIy_ixq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#define training arguments parameters\n",
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"test-ner\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        ")"
      ],
      "metadata": {
        "id": "gZGaI19ADSCu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
        "#objects that will form a batch by using a list of dataset elements as input - they are the same type of the elements of the trian dataset"
      ],
      "metadata": {
        "id": "kJ_-UpQTEwCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metric = datasets.load_metric('seqeval')\n",
        "\n",
        "example = main_dataset['train'][0]"
      ],
      "metadata": {
        "id": "r4THUkMiFL98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "065b7fab10aa43ee81c4656eebc0b024",
            "cfe52779527d47a19605b5224004bb8a",
            "7cac2a7d3bab4d56bf5ada0f07bd1850",
            "ba39e973ad364f1ca35873413e4ca0fe",
            "9f60955bcbc84c0caa008657016b6eee",
            "2f5603f9515741c79649ca7d65303619",
            "87cbb1b5c0a0477aa898dce660eff4f1",
            "fee7b61082614411a5054c2b351cb80d",
            "97a90484479e4b709d8350c8aa1ec4be",
            "5d28b63645884ad3a9d950adc734d638",
            "9aaf61b1eff54a8a945e127072f6f344"
          ]
        },
        "outputId": "17842a6d-9c17-4228-8384-483c0f1ba219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-296c61dff470>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = datasets.load_metric('seqeval')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "065b7fab10aa43ee81c4656eebc0b024"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_list = main_dataset['train'].features['ner_tags'].feature.names\n",
        "label_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbqYW3FKFTkF",
        "outputId": "7b215735-e2e5-481d-82f9-52b445c3fb73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'I-LOC', 'B-PER', 'B-ORG', 'I-PER', 'I-GPE', 'I-ORG', 'B-LOC', 'B-GPE']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = main_dataset['train'][1]"
      ],
      "metadata": {
        "id": "1xrheOc6GvMC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculate the metric on a single example (the first of the train dataset)\n",
        "labels = [label_list[i] for i in example['ner_tags']]\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGkApBvuGMli",
        "outputId": "6583bc67-7549-4c58-db7a-58b558143ca9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'O', 'O', 'O', 'O', 'O', 'B-PER']"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check metric.compute works\n",
        "metric.compute(predictions=[labels], references=[labels])\n",
        "#the score of this is very high because I pass the same list to both my predictions and references"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-ZEzI6OGz11",
        "outputId": "e39192b7-f621-4c50-e030-79970342d7e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
              " 'overall_precision': 1.0,\n",
              " 'overall_recall': 1.0,\n",
              " 'overall_f1': 1.0,\n",
              " 'overall_accuracy': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    pred_logits, labels = eval_preds\n",
        "\n",
        "    # pred_logits are obtained by passing numpy.argmax\n",
        "    pred_logits = np.argmax(pred_logits, axis=2)\n",
        "    # the logits and the probabilities are in the same order,\n",
        "    # so we don’t need to apply the softmax\n",
        "\n",
        "    # We remove all the values where the label is -100\n",
        "    # define the variable prediction here\n",
        "    predictions = [\n",
        "        [label_list[eval_preds] for (eval_preds, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(pred_logits, labels)\n",
        "    ]\n",
        "    # define the variable true_labels here\n",
        "    true_labels = [\n",
        "      [label_list[l] for (eval_preds, l) in zip(prediction, label) if l != -100]\n",
        "       for prediction, label in zip(pred_logits, labels)\n",
        "    ]\n",
        "    results = metric.compute(predictions=predictions, references=true_labels)\n",
        "\n",
        "    global stored_predictions, stored_labels\n",
        "    stored_predictions.extend(predictions)\n",
        "    stored_labels.extend(true_labels)\n",
        "\n",
        "    return {\n",
        "   \"precision\": results[\"overall_precision\"],\n",
        "   \"recall\": results[\"overall_recall\"],\n",
        "   \"f1\": results[\"overall_f1\"],\n",
        "    \"accuracy\": results[\"overall_accuracy\"],\n",
        "  }"
      ],
      "metadata": {
        "id": "JAn7-31bHMEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stored_predictions = []\n",
        "stored_labels = []"
      ],
      "metadata": {
        "id": "Bev5zJI0nqM7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer (\n",
        "    model,\n",
        "    args,\n",
        "    optimizers=(optimizer, scheduler),\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "SvQZcRLpHMJ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "y0z5_wItJftt",
        "outputId": "968e7798-7aa6-428c-f53d-b5f10bf44073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='8421' max='8421' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [8421/8421 27:17, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.153300</td>\n",
              "      <td>0.139716</td>\n",
              "      <td>0.746940</td>\n",
              "      <td>0.785898</td>\n",
              "      <td>0.765924</td>\n",
              "      <td>0.970258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.100400</td>\n",
              "      <td>0.118315</td>\n",
              "      <td>0.823645</td>\n",
              "      <td>0.808972</td>\n",
              "      <td>0.816243</td>\n",
              "      <td>0.975457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.077700</td>\n",
              "      <td>0.135239</td>\n",
              "      <td>0.833687</td>\n",
              "      <td>0.844065</td>\n",
              "      <td>0.838844</td>\n",
              "      <td>0.977605</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=8421, training_loss=0.21688686126683618, metrics={'train_runtime': 1642.1861, 'train_samples_per_second': 20.51, 'train_steps_per_second': 5.128, 'total_flos': 884285715700386.0, 'train_loss': 0.21688686126683618, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from conlleval import evaluate\n",
        "\n",
        "true_flat = [item for sublist in stored_labels for item in sublist]\n",
        "pred_flat = [item for sublist in stored_predictions for item in sublist]\n",
        "\n",
        "# print out the table as above\n",
        "evaluate(true_flat, pred_flat, verbose=True)\n",
        "\n",
        "# calculate overall metrics\n",
        "prec, rec, f1 = evaluate(true_flat, pred_flat, verbose=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8v0BK3dnE2y",
        "outputId": "f533e6d9-ff2d-4492-8c40-6ff01671492a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processed 364632 tokens with 27954 phrases; found: 28390 phrases; correct: 22726.\n",
            "accuracy:  82.97%; (non-O)\n",
            "accuracy:  97.44%; precision:  80.05%; recall:  81.30%; FB1:  80.67\n",
            "              GPE: precision:  74.34%; recall:  81.08%; FB1:  77.56  6636\n",
            "              LOC: precision:  51.68%; recall:  22.84%; FB1:  31.68  358\n",
            "              ORG: precision:  67.74%; recall:  74.84%; FB1:  71.11  7898\n",
            "              PER: precision:  90.81%; recall:  88.12%; FB1:  89.45  13498\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "conf_matrix = confusion_matrix(np.concatenate(stored_labels), np.concatenate(stored_predictions))\n",
        "labels = sorted(list(set(np.concatenate(stored_labels))))\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bNLQx-klnT3q",
        "outputId": "0e7a42d1-17a8-4c19-a9c6-007ea890a6b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model to Google Drive\n",
        "model.save_pretrained('/content/drive/MyDrive/ner_model')\n",
        "\n",
        "# Save the tokenizer to Google Drive\n",
        "tokenizer.save_pretrained('/content/drive/MyDrive/tokenizer')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "ba2lB73weU14",
        "outputId": "0fbcdc4b-e2b0-48ba-8654-7e4b8f7b2823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device",
            "\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-33-937c7df8a262>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Save the model to Google Drive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/ner_model'\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Save the tokenizer to Google Drive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/tokenizer'\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/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36msave_pretrained\u001b[0;34m(self, save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, token, save_peft_format, **kwargs)\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_main_process\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2062\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_hf_peft_config_loaded\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2063\u001b[0;31m                 \u001b[0mmodel_to_save\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_directory\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   2064\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcan_generate\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   2065\u001b[0m                 \u001b[0mmodel_to_save\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_directory\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/python3.10/dist-packages/transformers/configuration_utils.py\u001b[0m in \u001b[0;36msave_pretrained\u001b[0;34m(self, save_directory, push_to_hub, **kwargs)\u001b[0m\n\u001b[1;32m    458\u001b[0m         \u001b[0moutput_config_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG_NAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_json_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_config_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_diff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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    461\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Configuration saved in {output_config_file}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py\u001b[0m in \u001b[0;36mto_json_file\u001b[0;34m(self, json_file_path, use_diff)\u001b[0m\n\u001b[1;32m    937\u001b[0m                 \u001b[0;32mis\u001b[0m \u001b[0mserialized\u001b[0m \u001b[0mto\u001b[0m \u001b[0mJSON\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m         \"\"\"\n\u001b[0;32m--> 939\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_file_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mwriter\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    940\u001b[0m             \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_json_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muse_diff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_diff\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    941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
          ]
        }
      ]
    }
  ]
}