train_camembert_span_classification.ipynb

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   "source": [
    "# Fine-tune a CamemBERT model for span classification on the GeoEDdA dataset\n",
    "\n",
    "\n",
    "This notebook shows how to fine-tune a CamemBERT model for span classification on French texts.\n",
    "\n",
    "The model is available on HuggingFace: \n",
    "https://huggingface.co/GEODE/camembert-base-edda-span-classification\n",
    "\n",
    "We use the GeoEDdA dataset available also on HuggingFace:\n",
    "https://huggingface.co/datasets/GEODE/GeoEDdA\n",
    "\n",
    "For more details about the dataset and tagset used, see: https://github.com/GEODE-project/ner-spancat-edda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datasets import load_dataset, Dataset\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "from sklearn_crfsuite.utils import flatten\n",
    "from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer\n",
    "import os\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import filter_tags, format, get_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "device(type='mps')"
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "device = torch.device(\"mps\" if torch.backends.mps.is_available() else (\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n",
    "device"
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   "outputs": [],
   "source": [
    "dataset = load_dataset(\"GEODE/GeoEDdA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "tgQ36aZc9tT5"
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    "dfs = []\n",
    "for key in dataset.keys():\n",
    "    dfs.append(pd.DataFrame({'dataset':key, 'text':dataset[key]['text'], 'meta':dataset[key]['meta'], 'tokens':dataset[key]['tokens'], 'spans':dataset[key]['spans']}))\n",
    "df = pd.concat(dfs, ignore_index=True)"
   ]
  },
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    "id": "WCVorAdh9tT5",
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       "  dataset                                               text  \\\n",
       "0   train  ILLESCAS, (Géog.) petite ville d'Espagne, dans...   \n",
       "1   train  MULHAUSEN, (Géog.) ville impériale d'Allemagne...   \n",
       "2   train  * ADDA, riviere de Suisse & d'Italie, qui a sa...   \n",
       "3   train  SINTRA ou CINTRA, (Géog. mod.) montagne de Por...   \n",
       "4   train  * ACHSTEDE, ou AKSTEDE, s. petite Ville d'Alle...   \n",
       "\n",
       "                                                meta  \\\n",
       "0  {'volume': 8, 'head': 'ILLESCAS', 'author': 'u...   \n",
       "1  {'volume': 10, 'head': 'MULHAUSEN', 'author': ...   \n",
       "2  {'volume': 1, 'head': 'ADDA', 'author': 'Dider...   \n",
       "3  {'volume': 15, 'head': 'SINTRA ou CINTRA', 'au...   \n",
       "4  {'volume': 1, 'head': 'ACHSTEDE, ou AKSTEDE', ...   \n",
       "\n",
       "                                              tokens  \\\n",
       "0  [{'text': 'ILLESCAS', 'start': 0, 'end': 8, 'i...   \n",
       "1  [{'text': 'MULHAUSEN', 'start': 0, 'end': 9, '...   \n",
       "2  [{'text': '*', 'start': 0, 'end': 1, 'id': 0, ...   \n",
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       "4  [{'text': '*', 'start': 0, 'end': 1, 'id': 0, ...   \n",
       "\n",
       "                                               spans  \n",
       "0  [{'text': 'ILLESCAS', 'start': 0, 'end': 8, 't...  \n",
       "1  [{'text': 'MULHAUSEN', 'start': 0, 'end': 9, '...  \n",
       "2  [{'text': 'ADDA', 'start': 2, 'end': 6, 'token...  \n",
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   "source": [
    "df.head()"
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   "execution_count": 6,
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    "tagset_filter = ['Head', 'Domain-mark', 'NC-Spatial', 'NC-Person', 'NP-Spatial', 'NP-Person', 'NP-Misc', 'Relation', 'Latlong']\n",
    "\n",
    "df['tags'] = df.apply(lambda x:[' '.join(label) for label in filter_tags(x, tagset_filter, remove_nested=True)], axis=1)\n",
    "\n",
    "tagset = set([tag for tags in df['tags'] for tag in tags])\n",
    "\n",
    "label2id, id2label = {tag: i for i, tag in enumerate(tagset)}, {i: tag for i, tag in enumerate(tagset)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ids'] = df.apply(lambda x: format(list(x['tags']), label2id), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "  dataset                                               text  \\\n",
       "0   train  ILLESCAS, (Géog.) petite ville d'Espagne, dans...   \n",
       "1   train  MULHAUSEN, (Géog.) ville impériale d'Allemagne...   \n",
       "2   train  * ADDA, riviere de Suisse & d'Italie, qui a sa...   \n",
       "3   train  SINTRA ou CINTRA, (Géog. mod.) montagne de Por...   \n",
       "4   train  * ACHSTEDE, ou AKSTEDE, s. petite Ville d'Alle...   \n",
       "\n",
       "                                                meta  \\\n",
       "0  {'volume': 8, 'head': 'ILLESCAS', 'author': 'u...   \n",
       "1  {'volume': 10, 'head': 'MULHAUSEN', 'author': ...   \n",
       "2  {'volume': 1, 'head': 'ADDA', 'author': 'Dider...   \n",
       "3  {'volume': 15, 'head': 'SINTRA ou CINTRA', 'au...   \n",
       "4  {'volume': 1, 'head': 'ACHSTEDE, ou AKSTEDE', ...   \n",
       "\n",
       "                                              tokens  \\\n",
       "0  [{'text': 'ILLESCAS', 'start': 0, 'end': 8, 'i...   \n",
       "1  [{'text': 'MULHAUSEN', 'start': 0, 'end': 9, '...   \n",
       "2  [{'text': '*', 'start': 0, 'end': 1, 'id': 0, ...   \n",
       "3  [{'text': 'SINTRA', 'start': 0, 'end': 6, 'id'...   \n",
       "4  [{'text': '*', 'start': 0, 'end': 1, 'id': 0, ...   \n",
       "\n",
       "                                               spans  \\\n",
       "0  [{'text': 'ILLESCAS', 'start': 0, 'end': 8, 't...   \n",
       "1  [{'text': 'MULHAUSEN', 'start': 0, 'end': 9, '...   \n",
       "2  [{'text': 'ADDA', 'start': 2, 'end': 6, 'token...   \n",
       "3  [{'text': 'SINTRA ou CINTRA', 'start': 0, 'end...   \n",
       "4  [{'text': 'ACHSTEDE, ou AKSTEDE', 'start': 2, ...   \n",
       "\n",
       "                                                tags  \\\n",
       "0  [Head, O, O, Domain-mark, Domain-mark, O, NC-S...   \n",
       "1  [Head, O, O, Domain-mark, Domain-mark, O, NC-S...   \n",
       "2  [O, Head, O, NC-Spatial, O, NP-Spatial, O, O, ...   \n",
       "3  [Head, Head, Head, O, O, Domain-mark, Domain-m...   \n",
       "4  [O, Head, Head, Head, Head, O, O, NC-Spatial, ...   \n",
       "\n",
       "                                                 ids  \n",
       "0  [0, 4, 4, 2, 2, 4, 1, 1, 4, 9, 4, 3, 9, 9, 9, ...  \n",
       "1  [0, 4, 4, 2, 2, 4, 1, 1, 4, 9, 4, 3, 9, 9, 4, ...  \n",
       "2  [4, 0, 4, 1, 4, 9, 4, 4, 9, 4, 4, 3, 3, 3, 3, ...  \n",
       "3  [0, 0, 0, 4, 4, 2, 2, 2, 2, 4, 1, 4, 9, 3, 9, ...  \n",
       "4  [4, 0, 0, 0, 0, 4, 4, 1, 1, 4, 9, 3, 1, 1, 4, ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = Dataset.from_list(get_data((df[df['dataset'] == 'train'].to_dict('records'))))\n",
    "val_dataset = Dataset.from_list(get_data((df[df['dataset'] == 'test'].to_dict('records'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "1be5750ea41344549cfd71a34f7e346e",
      "7723ec81a9c94c1eac509f2ed3cf8a82",
      "c2cb49d448ad44ed9fc4f850926c591c",
      "643853367b61468da37e5cf1c1faedd7",
      "d11b5bd2bea84b6ea84d4257388318c3",
      "6fd7134172e64b838cbb72717af44088",
      "3ccb3b7b3a874496b468b27e7b420297",
      "52f81e6a5c064f7795a7aa273835426a",
      "5928e6c9651e4c12889f4bed4e5ac4f2",
      "532d202c521d48759134d38ceae943af",
      "f0f40f63b5b24dfcafd269761dee9fca",
      "b901c22ac93f4668b38e9c28e85447ec",
      "0c726eaa084d4b22bd273ed920df6ae8",
      "4572d1aa279743e2be57f9d239dda622",
      "e3f4f324f92c458b817d4ff47538bc78",
      "8066ef3df95044cba633d9612f849c56",
      "6de75142a98e42cca839ecc78b014189",
      "53654cd7ddf447b9979a56e2456424b5",
      "a42ca2f4c6b04ac18af35dae682e7312",
      "9970851e4ebb423482554814bc17b02b",
      "012244bda70d443b9ed1a0e7a8ff67d4",
      "dc8ecf5c64f74ec4a5c9d59d9d5b489b",
      "db55738fe5d64ca781db972d6119af1d",
      "59fb01ddb7e742a0bad2f422cfbbfbfe",
      "90856bfb1083458ca3798e5709f892c0",
      "4a288f079d5543fabfd8d7340a2a6453",
      "3d3c7ecbcf60451681e8009ec4ddc749",
      "c979b0b4364d48d19505beba8b291574",
      "55d24e498c9b435bb34cc01312541e4b",
      "ce23f84e87f74694b76b93c3729a9898",
      "6314e50964774ff19d485a572695e3a2",
      "c8f841ec185e4787bf2c8021ec8d793d",
      "9df9204cdcaa46bdbe9abbcbed42f0e1"
     ]
    },
    "id": "rnSqMJ-k9tT_",
    "outputId": "8b38cfff-3673-46b4-f55c-c29de3bcb2f0"
   },
   "outputs": [],
   "source": [
    "# model_base = \"camembert-base\"\n",
    "model_base = \"almanach/camembertv2-base\"\n",
    "# model_base = \"almanach/camembertav2-base\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_base, clean_up_tokenization_spaces=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_and_align_labels(examples):\n",
    "    tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True, padding=\"max_length\")\n",
    "    labels = []\n",
    "    word_idss = []\n",
    "    for i, label in enumerate(examples[f\"tags_id\"]):\n",
    "        word_ids = tokenized_inputs.word_ids(batch_index=i) \n",
    "        label_ids = []\n",
    "        for j, word_idx in enumerate(word_ids):  \n",
    "            if word_idx is None:\n",
    "                label_ids.append(label2id['O'])\n",
    "            else:\n",
    "                label_ids.append(label[word_idx])\n",
    "        labels.append(label_ids)\n",
    "        word_idss.append(word_ids)\n",
    "    tokenized_inputs[\"labels\"] = labels\n",
    "    tokenized_inputs['word_ids'] = word_idss\n",
    "    return tokenized_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "9e5f001035d943389c470ec4d8ef5dd2",
      "60e84c2d7b0149f2a8d1610054da5122",
      "96f24627743c43bf87b920268d7402aa",
      "aba108450fad44b991535c76eadca98d",
      "0b33b012231e4f81ad728b3219021a73",
      "8f3156039828433f95e82b40cae94104",
      "34a802706eba4f0e8b63c0d9e718965c",
      "8182bbe26e214091b8547b726eea6bf4",
      "7da43ba1e8494cce9831eac204c402a1",
      "06a4ce5350be4986b5c304362de763e3",
      "8ab4892f3d2346409aaa5c0003f9177c",
      "65e076c759b3491e896bd52e101b825f",
      "4d42be523f0b432cb0606229fd01fa15",
      "0792efba4ed94444ab93a06b5164ad1e",
      "16056fbd5a68411a8beb88491523b59f",
      "c0a9633426a9465cb5aa239b3855d0a7",
      "20569a0860654fae9980f663a0d68f9d",
      "fb9cb89fb81045deb683c578bc965e08",
      "6a36c009d74d44e1bdd47c9daaaeaef5",
      "3d2086b3f72a400082cd1fb9917a03fb",
      "7ff62d0d426848ae9a6913f2967c2a93",
      "8436b0a7b4bf4d5d974053cf939d4245",
      "9259e4570d07492eac12bfa2d9d10844",
      "3afecf7f33874eaf9fa52f2f7d297b59",
      "edb2ed99bf914cd6ad97075beecb4557",
      "4330652a4de24c649281d5fbee19d7e6",
      "7f78a4f9dda845778cf88b6b06e61d03",
      "78a792f6813f44c9bf9aaa5b1b400f2a",
      "25cf978c08b146eebfb2b7213368f793",
      "e3bc286e80ff40229a08610a56fe3db9",
      "25fd566485c4427c84bba14a9854b7a5",
      "80ad040ada734dfd889610bcafe85686",
      "28ae48612ef144a6ab3c6f36d133f08f"
     ]
    },
    "id": "IDcOpU6P9tT_",
    "outputId": "0597a0e2-a406-4f85-b1f4-0221f64c6826"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "050d31c66d7b454eb2474b58be3cf2e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1800 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11fedae07ec3484a986099d3cece2414",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_train_dataset = train_dataset.map(tokenize_and_align_labels, batched=True)\n",
    "tokenized_val_dataset = val_dataset.map(tokenize_and_align_labels, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4sRKxsbq9tUA"
   },
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C6CLSttW9tUA",
    "outputId": "3f58db30-0f1d-4eb2-b324-8a49aaead76c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token\t\t Label\n",
      " [CLS] \t\t O\n",
      " IL \t\t Head\n",
      " ##LES \t\t Head\n",
      " ##CA \t\t Head\n",
      " ##S \t\t Head\n",
      " , \t\t O\n",
      " ( \t\t O\n",
      " Gé \t\t Domain-mark\n",
      " ##og \t\t Domain-mark\n",
      " . \t\t Domain-mark\n",
      " ) \t\t O\n",
      " petite \t\t NC-Spatial\n",
      " ville \t\t NC-Spatial\n",
      " d' \t\t O\n",
      " Espagne \t\t NP-Spatial\n",
      " , \t\t O\n",
      " dans \t\t Relation\n",
      " la \t\t NP-Spatial\n",
      " nouvelle \t\t NP-Spatial\n",
      " Cast \t\t NP-Spatial\n",
      " ##ille \t\t NP-Spatial\n",
      " , \t\t Relation\n",
      " à \t\t Relation\n",
      " six \t\t Relation\n",
      " lieu \t\t Relation\n",
      " ##es \t\t Relation\n",
      " au \t\t Relation\n",
      " sud \t\t Relation\n",
      " de \t\t Relation\n",
      " Madrid \t\t NP-Spatial\n",
      " . \t\t O\n",
      " [SEP] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
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      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n",
      " [PAD] \t\t O\n"
     ]
    }
   ],
   "source": [
    "sample = tokenized_train_dataset[0]\n",
    "print(\"Token\\t\\t Label\")\n",
    "for token_id, token_labels in zip(sample[\"input_ids\"], sample[\"labels\"]):\n",
    "    token_text = tokenizer.decode(token_id)\n",
    "    labels = id2label[token_labels]\n",
    "    print(f\" {token_text} \\t\\t {labels}\")\n",
    "    if token_text == \"</s>\":\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def divide(a: int, b: int):\n",
    "    return a / b if b > 0 else 0\n",
    "\n",
    "\n",
    "def compute_metrics(p):\n",
    "    predictions, true_labels = p\n",
    "    metrics = {}\n",
    "    cm = multilabel_confusion_matrix(flatten(true_labels), [np.argmax(token) for sentence in predictions for token in sentence], labels=list(id2label.keys()))\n",
    "    for label_idx, matrix in enumerate(cm):\n",
    "        if id2label[label_idx] == 'O':\n",
    "            continue\n",
    "        tp, fp, fn = matrix[1, 1], matrix[0, 1], matrix[1, 0]\n",
    "        precision = divide(tp, tp + fp)\n",
    "        recall = divide(tp, tp + fn)\n",
    "        f1 = divide(2 * precision * recall, precision + recall)\n",
    "        metrics[f\"f1_{id2label[label_idx]}\"] = f1\n",
    "    macro_f1 = sum(list(metrics.values())) / (len(list(id2label.keys())) - 1)\n",
    "    metrics[\"macro_f1\"] = macro_f1\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86,
     "referenced_widgets": [
      "efa7dbd2fa084ad0a90618759bca8db5",
      "cdbd9e1325bb4741b1d090802f7da136",
      "0a3a57c263a64e5cb752be11f4af55e2",
      "e05561e14bdd4fa38bcc98103c54deee",
      "c4baa128367d4d1c811eebfaffee33bf",
      "497ea51806664e48ba38bccbdaf3fe35",
      "4802e58cbab349a4ad627a0fa6093496",
      "e82139d907f94e8ca66be417f180d5f1",
      "20ee8376de434e75ad3165d70fbb9897",
      "6a1c07dd889241848251915e4114d7a4",
      "f0500603b2b446fc963d9b019593fa4e"
     ]
    },
    "id": "5-pBrzNd9tUB",
    "outputId": "eb60086f-dd70-4e63-e70f-0dfe6711690d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at almanach/camembertv2-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"
     ]
    }
   ],
   "source": [
    "model = AutoModelForTokenClassification.from_pretrained(model_base, num_labels = len(tagset), id2label=id2label, label2id=label2id).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "JOVllaTJ9tUB"
   },
   "outputs": [],
   "source": [
    "#output_model_name = 'camembert-base-edda-span-classification'\n",
    "output_model_name = 'camembertv2-base-edda-span-classification'\n",
    "#output_model_name = 'camembertav2-base-edda-span-classification'\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir = os.path.join('models', output_model_name),\n",
    "    eval_strategy = 'epoch',\n",
    "    save_strategy = 'epoch',\n",
    "    num_train_epochs = 4,\n",
    "    learning_rate = 1e-4,\n",
    "    per_device_train_batch_size = 8,\n",
    "    per_device_eval_batch_size = 8,\n",
    "    metric_for_best_model = 'macro_f1',\n",
    "    load_best_model_at_end = True,\n",
    "    save_total_limit = 1,\n",
    "    logging_steps = 225,\n",
    "    seed = 42\n",
    "    )\n",
    "\n",
    "trainer = Trainer(\n",
    "    model = model,\n",
    "    args = training_args,\n",
    "    train_dataset = tokenized_train_dataset,\n",
    "    eval_dataset = tokenized_val_dataset,\n",
    "    compute_metrics = compute_metrics\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 746
    },
    "id": "4VLlAB9_9tUC",
    "outputId": "00cbee1b-e874-4edd-eaf5-561a1c1b7af8"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8586d7b293f489b8d681e576c6e5e99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/900 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.1017, 'grad_norm': 0.190268412232399, 'learning_rate': 7.500000000000001e-05, 'epoch': 1.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a8ea60dcb874dc0bdbaddfc3973d4c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.0281609445810318, 'eval_f1_Head': 0.9617918313570488, 'eval_f1_NC-Spatial': 0.9151817533856024, 'eval_f1_Domain-mark': 0.9756572541382668, 'eval_f1_Relation': 0.9126712328767124, 'eval_f1_NP-Person': 0.8727770177838577, 'eval_f1_NP-Misc': 0.7381818181818183, 'eval_f1_NC-Person': 0.7031249999999999, 'eval_f1_Latlong': 0.9669947886508395, 'eval_f1_NP-Spatial': 0.9474528680304853, 'eval_macro_f1': 0.8882037293782923, 'eval_runtime': 10.4379, 'eval_samples_per_second': 19.161, 'eval_steps_per_second': 2.395, 'epoch': 1.0}\n",
      "{'loss': 0.0234, 'grad_norm': 0.13051867485046387, 'learning_rate': 5e-05, 'epoch': 2.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c80d2ddc122b426da89a986dc702a5b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.02150683104991913, 'eval_f1_Head': 0.9811827956989247, 'eval_f1_NC-Spatial': 0.9218523878437049, 'eval_f1_Domain-mark': 0.991404011461318, 'eval_f1_Relation': 0.9157446808510639, 'eval_f1_NP-Person': 0.8761904761904762, 'eval_f1_NP-Misc': 0.5963636363636364, 'eval_f1_NC-Person': 0.7445255474452555, 'eval_f1_Latlong': 0.9691322073383809, 'eval_f1_NP-Spatial': 0.9277389277389277, 'eval_macro_f1': 0.8804594078812987, 'eval_runtime': 11.2634, 'eval_samples_per_second': 17.757, 'eval_steps_per_second': 2.22, 'epoch': 2.0}\n",
      "{'loss': 0.0146, 'grad_norm': 0.13007865846157074, 'learning_rate': 2.5e-05, 'epoch': 3.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb4e58d2511e439c8f34b29308e0bb4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.015162939205765724, 'eval_f1_Head': 0.9864864864864865, 'eval_f1_NC-Spatial': 0.9361702127659575, 'eval_f1_Domain-mark': 0.9924098671726755, 'eval_f1_Relation': 0.9308836395450569, 'eval_f1_NP-Person': 0.8639365918097754, 'eval_f1_NP-Misc': 0.766721044045677, 'eval_f1_NC-Person': 0.7515337423312883, 'eval_f1_Latlong': 0.9707943925233644, 'eval_f1_NP-Spatial': 0.9350441058540497, 'eval_macro_f1': 0.9037755647260369, 'eval_runtime': 10.5376, 'eval_samples_per_second': 18.98, 'eval_steps_per_second': 2.372, 'epoch': 3.0}\n",
      "{'loss': 0.0109, 'grad_norm': 0.04714209958910942, 'learning_rate': 0.0, 'epoch': 4.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "332fafd35ee84936bebabb8e40e70420",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.014626906253397465, 'eval_f1_Head': 0.9870483980913428, 'eval_f1_NC-Spatial': 0.9301983835415136, 'eval_f1_Domain-mark': 0.9933269780743564, 'eval_f1_Relation': 0.9308836395450569, 'eval_f1_NP-Person': 0.8801089918256131, 'eval_f1_NP-Misc': 0.7740916271721958, 'eval_f1_NC-Person': 0.7731629392971245, 'eval_f1_Latlong': 0.9690962099125364, 'eval_f1_NP-Spatial': 0.939034045922407, 'eval_macro_f1': 0.9085501348202387, 'eval_runtime': 9.9892, 'eval_samples_per_second': 20.022, 'eval_steps_per_second': 2.503, 'epoch': 4.0}\n",
      "{'train_runtime': 11797.8546, 'train_samples_per_second': 0.61, 'train_steps_per_second': 0.076, 'train_loss': 0.037661625544230144, 'epoch': 4.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=900, training_loss=0.037661625544230144, metrics={'train_runtime': 11797.8546, 'train_samples_per_second': 0.61, 'train_steps_per_second': 0.076, 'total_flos': 3762945441792000.0, 'train_loss': 0.037661625544230144, 'epoch': 4.0})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "L6xchH2uEC9W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'models/camembertv2-base-edda-span-classification/checkpoint-900'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_checkpoint = trainer.state.best_model_checkpoint\n",
    "best_model_checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "VsIURyKQBwca"
   },
   "outputs": [],
   "source": [
    "train_logs = trainer.state.log_history\n",
    "\n",
    "with open(os.path.join('training_logs',output_model_name+'.json'), \"w\") as json_file:\n",
    "    json.dump(train_logs, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.save_model(output_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join('training_logs',output_model_name+'.json')) as json_file:\n",
    "    train_logs = json.load(json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_loss = []\n",
    "loss = []\n",
    "metrics = {}\n",
    "for label in [\"eval_f1_\"+i for i in [j for j in tagset if j != 'O']]:\n",
    "    metrics[label] = []\n",
    "\n",
    "for log in train_logs:\n",
    "    if(\"loss\" in log.keys()):\n",
    "        loss.append(log['loss'])\n",
    "    if(\"eval_loss\" in log.keys()):\n",
    "        eval_loss.append(log['eval_loss'])\n",
    "    common = list(set(list(metrics.keys())).intersection(list(log.keys())))\n",
    "    for element in common:\n",
    "        metrics[element].append(log[element])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "l7Trth937-jd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss, label=\"Training loss\")\n",
    "plt.plot(eval_loss, label=\"Validation loss\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "for key,value in metrics.items():\n",
    "    plt.plot(value, label=key[8:])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('F1 score')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "ner-bert-py311",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "0066147abfa44f6a9c0e728a6b8a86fb": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
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