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модель классификатора\n", "\n", "- Загрузить цельную модель на haggingface\n", "\n", "- Проверить работоспособность\n", "\n", "- IPYNB с комментариями тоже загрузить на HF\n", "\n", "Размеченный датасет в формате слово - сущность приложен.\n", "\n", "Рецепты разделены пустыми строками.\n", "\n", "В итоге после загрузки модели я должен подавать ей на вход рецепт в текстовом виде, а она - выделять из него именованные сущности\n", "\n", "Присылать ссылку на HuggingFace модель" ], "metadata": { "id": "j1yzXj_bB4D_" } }, { "cell_type": "markdown", "source": [ "# Блок 1: Установка и импорт библиотек" ], "metadata": { "id": "7ekgSfKFEX1h" } }, { "cell_type": "code", "source": [ "!pip install transformers datasets tokenizers seqeval accelerate -q" ], "metadata": { "id": "SwYq9VA-EWOO", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "46e4e465-c88a-4b01-ee76-f22e8800c35b" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "from transformers import (\n", " AutoTokenizer,\n", " AutoModelForTokenClassification,\n", " TrainingArguments,\n", " Trainer,\n", " DataCollatorForTokenClassification,\n", " pipeline,\n", " EarlyStoppingCallback\n", ")\n", "from datasets import Dataset, DatasetDict, ClassLabel, Sequence\n", "from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers\n", "import torch\n", "import json\n", "import bz2\n", "from collections import Counter\n", "import matplotlib.pyplot as plt\n", "from seqeval.metrics import classification_report, accuracy_score" ], "metadata": { "id": "qeXiOd98D8x7" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Блок 2: Загрузка и проверка данных" ], "metadata": { "id": "ApKd4knCEf8H" } }, { "cell_type": "code", "source": [ "import bz2\n", "\n", "file_path = \"/content/bio-recipe-datasettxt.bz2\"\n", "\n", "def load_conll_format_from_bz2(file_path):\n", " sentences = []\n", " labels = []\n", " words = []\n", " tags = []\n", "\n", " with bz2.open(file_path, 'rt', encoding='utf-8') as f:\n", " for line in f:\n", " line = line.strip()\n", " if line == '':\n", " if words:\n", " sentences.append(words)\n", " labels.append(tags)\n", " words = []\n", " tags = []\n", " else:\n", " parts = line.split()\n", " if len(parts) >= 2:\n", " word = parts[0]\n", " tag = parts[-1]\n", " words.append(word)\n", " tags.append(tag)\n", "\n", " if words:\n", " sentences.append(words)\n", " labels.append(tags)\n", "\n", " return sentences, labels\n", "\n", "# Загружаем данные\n", "sentences, labels = load_conll_format_from_bz2(file_path)\n", "\n", "print(f\"Загружено рецептов: {len(sentences)}\")\n", "print(f\"Загружено наборов меток: {len(labels)}\")\n", "\n", "# Проверка данных\n", "if len(sentences) > 0 and len(labels) > 0:\n", " print(f\"\\nПример первого рецепта (первые 10 токенов):\")\n", " print(\"Слова:\", sentences[0][:10])\n", " print(\"Метки:\", labels[0][:10])\n", "\n", " print(f\"\\nПримеры рецептов:\")\n", " for i in range(min(3, len(sentences))):\n", " print(f\"\\nРецепт {i+1}:\")\n", " # Показываем только сущности (не 'O')\n", " entities = [(w, t) for w, t in zip(sentences[i], labels[i]) if t != 'O']\n", " if entities:\n", " for word, tag in entities[:10]:\n", " print(f\" {word}: {tag}\")\n", " else:\n", " print(\" Сущностей нет\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6Rk9aVfhD-Wo", "outputId": "ea6f85fd-5730-41dc-9406-26e567161306" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Загружено рецептов: 49848\n", "Загружено наборов меток: 49848\n", "\n", "Пример первого рецепта (первые 10 токенов):\n", "Слова: ['1$1/4', 'cups', 'cooked', 'and', 'pureed', 'fresh', 'butternut', 'squash', ',', 'or']\n", "Метки: ['B-QTY', 'B-UNIT', 'B-COMMENT', 'I-COMMENT', 'I-COMMENT', 'I-COMMENT', 'B-NAME', 'I-NAME', 'OTHER', 'I-COMMENT']\n", "\n", "Примеры рецептов:\n", "\n", "Рецепт 1:\n", " 1$1/4: B-QTY\n", " cups: B-UNIT\n", " cooked: B-COMMENT\n", " and: I-COMMENT\n", " pureed: I-COMMENT\n", " fresh: I-COMMENT\n", " butternut: B-NAME\n", " squash: I-NAME\n", " ,: OTHER\n", " or: I-COMMENT\n", "\n", "Рецепт 2:\n", " 1: B-INDEX\n", " cup: B-UNIT\n", " peeled: I-COMMENT\n", " and: I-COMMENT\n", " cooked: I-COMMENT\n", " fresh: I-COMMENT\n", " chestnuts: B-NAME\n", " (: B-COMMENT\n", " about: I-COMMENT\n", " 20: I-COMMENT\n", "\n", "Рецепт 3:\n", " 1: B-QTY\n", " medium-size: B-COMMENT\n", " onion: B-NAME\n", " ,: B-COMMENT\n", " peeled: I-COMMENT\n", " and: I-COMMENT\n", " chopped: I-COMMENT\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Блок 3: Анализ данных и меток" ], "metadata": { "id": "7Lzy8KuwFu_p" } }, { "cell_type": "code", "source": [ "# Собираем уникальные метки\n", "unique_labels = set()\n", "for recipe_labels in labels:\n", " unique_labels.update(recipe_labels)\n", "\n", "label_list = ['O'] + sorted([l for l in unique_labels if l != 'O'])\n", "label2id = {label: i for i, label in enumerate(label_list)}\n", "id2label = {i: label for label, i in label2id.items()}\n", "\n", "print(f\"Найдено уникальных меток: {len(label_list)}\")\n", "print(\"Метки:\", label_list)\n", "\n", "# Статистика по меткам\n", "all_labels_flat = [label for recipe_labels in labels for label in recipe_labels]\n", "label_counts = Counter(all_labels_flat)\n", "\n", "print(\"\\nРаспределение меток:\")\n", "for label, count in label_counts.most_common(10):\n", " percentage = (count / len(all_labels_flat)) * 100\n", " print(f\" {label}: {count} ({percentage:.1f}%)\")\n", "\n", "# Статистика по длинам рецептов\n", "recipe_lengths = [len(s) for s in sentences]\n", "print(f\"\\n- Средняя длина рецепта: {np.mean(recipe_lengths):.1f} токенов\")\n", "print(f\"- Медианная длина: {np.median(recipe_lengths):.1f} токенов\")\n", "print(f\"- Мин: {min(recipe_lengths)}, Макс: {max(recipe_lengths)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OU8U7oXPEAJ_", "outputId": "9aaf3bf8-4d00-401a-8505-658295510789" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Найдено уникальных меток: 11\n", "Метки: ['O', 'B-COMMENT', 'B-INDEX', 'B-NAME', 'B-QTY', 'B-RANGE_END', 'B-UNIT', 'I-COMMENT', 'I-NAME', 'I-UNIT', 'OTHER']\n", "\n", "Распределение меток:\n", " I-COMMENT: 73588 (25.0%)\n", " B-NAME: 52117 (17.7%)\n", " B-QTY: 42568 (14.5%)\n", " B-COMMENT: 39599 (13.5%)\n", " B-UNIT: 36039 (12.3%)\n", " I-NAME: 28991 (9.9%)\n", " OTHER: 20317 (6.9%)\n", " B-RANGE_END: 755 (0.3%)\n", " B-INDEX: 2 (0.0%)\n", " I-UNIT: 1 (0.0%)\n", "\n", "- Средняя длина рецепта: 5.9 токенов\n", "- Медианная длина: 5.0 токенов\n", "- Мин: 1, Макс: 48\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Блок 4: Создание Dataset для Hugging Face" ], "metadata": { "id": "DJGQPSN7FzID" } }, { "cell_type": "code", "source": [ "# Подготовка данных\n", "dataset_dict = {\n", " 'tokens': sentences,\n", " 'ner_tags': [[label2id[label] for label in recipe_labels] for recipe_labels in labels]\n", "}\n", "\n", "dataset = Dataset.from_dict(dataset_dict)\n", "dataset = dataset.cast_column('ner_tags', Sequence(ClassLabel(names=label_list)))\n", "\n", "# Разделение на train/validation\n", "train_test_split = dataset.train_test_split(test_size=0.2, seed=42)\n", "datasets = DatasetDict({\n", " 'train': train_test_split['train'],\n", " 'validation': train_test_split['test']\n", "})\n", "\n", "print(f\"Train размер: {len(datasets['train'])} рецептов\")\n", "print(f\"Validation размер: {len(datasets['validation'])} рецептов\")\n", "print(f\"\\nПример из датасета:\")\n", "print(datasets['train'][0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 136, "referenced_widgets": [ "4bf7e28f70214a1fa48f3791237c5053", "3b3eae34274146e1835f45aee81a2cf1", "df0fcbae60c740dbae89594cf6127652", "6e12e6eda7034f62ad5c0f50a66a869d", "2aa7e586a0ab4937a778eb5a02cc0ed4", "417f9da6857449f48580e2770433b739", "5e1604641e3745aeaccc6f5238ea1280", "66d6b45a3d1946c8a697449d53b5b182", "f6a213ba943642ae9515a27c1ed3fccb", "c980655dcdb945a68d4380ddb60028e7", "967c5314cd68485b8c776280f45912b4" ] }, "id": "F5KcAqsQEDXy", "outputId": "cd5fb74e-4063-44d3-d518-706043e40386" }, "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Casting the dataset: 0%| | 0/49848 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [14958/24930 06:44 < 04:29, 36.97 it/s, Epoch 6/10]\n", "
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EpochTraining LossValidation LossPrecisionRecallF1Accuracy
10.3451680.3186900.8745900.8864670.8804880.898287
20.3004860.2966930.8811660.8930340.8870600.901014
30.2560920.2893320.8898040.9025340.8961240.908837
40.2467350.2945400.8973180.9049650.9011260.911308
50.2417890.2969450.9000080.9087100.9043380.913762
60.2137300.2905840.8975550.9109730.9042140.913899

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OTHER seems not to be NE tag.\n", " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OTHER seems not to be NE tag.\n", " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Результаты на валидации:\n", "Loss: 0.2893\n", "Accuracy: 0.9088\n", "F1-score: 0.8961\n", "Precision: 0.8898\n", "Recall: 0.9025\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Блок 8: Сохранение модели локально и тестирование" ], "metadata": { "id": "8LLBEfpLI4t3" } }, { "cell_type": "code", "source": [ "# Блок 8: Сохранение модели и тестирование\n", "print(\"\\nСохранение модели.\\n\")\n", "\n", "model_save_path = \"./recipe-ner-model-final\"\n", "\n", "# Сохраняем модель и токенизатор\n", "trainer.save_model(model_save_path)\n", "hf_tokenizer.save_pretrained(model_save_path)\n", "\n", "# Сохраняем метки\n", "with open(f\"{model_save_path}/label_mappings.json\", 'w') as f:\n", " json.dump({\"label2id\": label2id, \"id2label\": id2label}, f)\n", "\n", "print(f\"Модель сохранена в {model_save_path}\")\n", "\n", "# Функция для предсказаний\n", "def predict_ner(text, model, tokenizer, id2label):\n", " # Токенизация\n", " inputs = tokenizer(\n", " text,\n", " return_tensors=\"pt\",\n", " truncation=True,\n", " max_length=512,\n", " return_offsets_mapping=True\n", " )\n", "\n", " # Получаем offset mapping\n", " offset_mapping = inputs.pop('offset_mapping')\n", "\n", " # Перемещаем на устройство модели\n", " device = next(model.parameters()).device\n", " inputs = {k: v.to(device) for k, v in inputs.items()}\n", "\n", " # Предсказание\n", " model.eval()\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", " predictions = torch.argmax(outputs.logits, dim=2)[0].cpu().numpy()\n", " tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])\n", " offset_mapping = offset_mapping[0].cpu().numpy()\n", "\n", " # Собираем сущности\n", " entities = []\n", " current_entity = None\n", "\n", " for token, pred_id, offset in zip(tokens, predictions, offset_mapping):\n", " # Пропускаем специальные токены\n", " if token in [tokenizer.cls_token, tokenizer.sep_token, tokenizer.pad_token]:\n", " continue\n", "\n", " # Пропускаем подслова\n", " if token.startswith('##'):\n", " if current_entity:\n", " current_entity['word'] += token[2:]\n", " continue\n", "\n", " label = id2label.get(pred_id, 'O')\n", "\n", " if label != 'O':\n", " if current_entity:\n", " entities.append(current_entity)\n", " current_entity = {\n", " 'word': token,\n", " 'entity': label,\n", " 'start': int(offset[0]),\n", " 'end': int(offset[1])\n", " }\n", " else:\n", " if current_entity:\n", " entities.append(current_entity)\n", " current_entity = None\n", "\n", " if current_entity:\n", " entities.append(current_entity)\n", "\n", " return entities\n", "\n", "# Тестирование на английских рецептах\n", "print(\"\\nТестирование модели:\\n\")\n", "\n", "test_recipes = [\n", " \"Mix 200 grams of flour with 2 eggs and 100 ml of milk\",\n", " \"Add salt and pepper to taste\",\n", " \"Bake at 350 degrees for 30 minutes\",\n", " \"Preheat oven to 180 degrees Celsius\",\n", " \"Chop onions and garlic finely\",\n", " \"1/4 cups cooked and pureed fresh butternut squash\",\n", " \"Add vanilla extract and a pinch of salt\",\n", " \"Cook for 20-25 minutes until golden brown\"\n", "]\n", "\n", "print(\"Тестирование на английских рецептах:\\n\")\n", "for recipe in test_recipes:\n", " print(f\"{recipe}\")\n", " entities = predict_ner(recipe, model, hf_tokenizer, id2label)\n", " if entities:\n", " for entity in entities:\n", " print(f\" {entity['word']}: {entity['entity']}\")\n", " else:\n", " print(\" Сущности не найдены\")\n", " print()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "2d3cc46765e8403cbd90e920b869a9d4", "ec4b33e6d4764a1997b09bc88de4e4d8", "0686a544d9604472a02085c88528f7a9", "0173ea33daaf417ba31bf1f5c8f23791", "5121e921b2304cb78dc6fc519d9a49ee", "ef12bcd611744ef287a885f0f4009fdd", "a9e47d5f5f1040788db0ebd49e0e7d1e", "5f20209b344942be9f130b3353195ce5", "5020e3313f094a43984df1ca807863c9", "bb56dc1483704a29b5edbfe7094870e5", "21fb110614ac408ebb41adb4a5cf9328" ] }, "id": "87uASP4XEOBy", "outputId": "bf407e0a-96e2-49b7-95f2-fe5d7350b2c4" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Сохранение модели.\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00 {tokens}\")\n", "\n", "test_sentence = \"Mix 200 grams of flour with 2 eggs\"\n", "tokens = hf_tokenizer.tokenize(test_sentence)\n", "print(f\"\\nПредложение: {test_sentence}\")\n", "print(f\"Токены: {tokens}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pm6Y9N46rvWj", "outputId": "9b3ec865-1a23-4e97-8d1c-c8dce4209284" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Токенизатор bert-base-uncased загружен\n", "Размер словаря: 30522\n", "\n", "Проверка BERT токенизатора:\n", " Mix -> ['mix']\n", " Bake -> ['ba', '##ke']\n", " Add -> ['add']\n", " Preheat -> ['pre', '##hea', '##t']\n", " Chop -> ['chop']\n", " Cook -> ['cook']\n", " 200 -> ['200']\n", " grams -> ['grams']\n", " flour -> ['flour']\n", " butternut -> ['butter', '##nut']\n", " squash -> ['squash']\n", "\n", "Предложение: Mix 200 grams of flour with 2 eggs\n", "Токены: ['mix', '200', 'grams', 'of', 'flour', 'with', '2', 'eggs']\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Модель с BERT токенизатором" ], "metadata": { "id": "GwHsIWAKtM-e" } }, { "cell_type": "code", "source": [ "from transformers import AutoConfig, AutoModelForTokenClassification, EarlyStoppingCallback\n", "\n", "# Обновляем label_list - добавляем OTHER\n", "if 'OTHER' not in label_list:\n", " label_list.append('OTHER')\n", " label2id = {label: i for i, label in enumerate(label_list)}\n", " id2label = {i: label for label, i in label2id.items()}\n", "\n", "print(f\"Метки ({len(label_list)}): {label_list}\")\n", "\n", "# Токенизируем датасет\n", "def tokenize_and_align_labels(examples):\n", " tokenized_inputs = hf_tokenizer(\n", " examples[\"tokens\"],\n", " truncation=True,\n", " is_split_into_words=True,\n", " padding=False,\n", " max_length=512\n", " )\n", "\n", " labels = []\n", " for i, label in enumerate(examples[\"ner_tags\"]):\n", " word_ids = tokenized_inputs.word_ids(batch_index=i)\n", " previous_word_idx = None\n", " label_ids = []\n", " for word_idx in word_ids:\n", " if word_idx is None:\n", " label_ids.append(-100)\n", " elif word_idx != previous_word_idx:\n", " # Приводим метку к допустимому диапазону\n", " tag_id = label[word_idx]\n", " if tag_id >= len(label_list):\n", " tag_id = 0 # 'O' tag\n", " label_ids.append(tag_id)\n", " else:\n", " label_ids.append(-100)\n", " previous_word_idx = word_idx\n", " labels.append(label_ids)\n", "\n", " tokenized_inputs[\"labels\"] = labels\n", " return tokenized_inputs\n", "\n", "# Обновляем метки в датасете\n", "def update_labels(example):\n", " new_tags = []\n", " for tag in example['ner_tags']:\n", " if tag >= len(label_list):\n", " new_tags.append(0) # 'O'\n", " else:\n", " new_tags.append(tag)\n", " example['ner_tags'] = new_tags\n", " return example\n", "\n", "datasets_updated = datasets.map(update_labels)\n", "\n", "print(\"Метки обновлены, токенизируем датасет...\")\n", "tokenized_datasets = datasets_updated.map(\n", " tokenize_and_align_labels,\n", " batched=True,\n", " remove_columns=datasets_updated[\"train\"].column_names\n", ")\n", "\n", "print(\"Датасет токенизирован\")\n", "print(f\" Train: {len(tokenized_datasets['train'])} примеров\")\n", "print(f\" Validation: {len(tokenized_datasets['validation'])} примеров\")\n", "\n", "# Конфигурация модели\n", "config = AutoConfig.for_model(\n", " model_type=\"bert\",\n", " vocab_size=30522, # Размер словаря bert-base-uncased\n", " num_labels=len(label_list),\n", " id2label=id2label,\n", " label2id=label2id,\n", " hidden_size=256,\n", " num_hidden_layers=6,\n", " num_attention_heads=8,\n", " intermediate_size=1024,\n", " max_position_embeddings=512,\n", " hidden_dropout_prob=0.1,\n", " attention_probs_dropout_prob=0.1,\n", " type_vocab_size=1 # Убираем token_type_embeddings для упрощения\n", ")\n", "\n", "model = AutoModelForTokenClassification.from_config(config)\n", "\n", "print(f\"\\nМодель создана\")\n", "print(f\"Параметров: {model.num_parameters():,}\")\n", "\n", "# Data collator\n", "data_collator = DataCollatorForTokenClassification(\n", " tokenizer=hf_tokenizer,\n", " padding=True\n", ")\n", "\n", "# Настройки обучения\n", "training_args = TrainingArguments(\n", " output_dir=\"./recipe-ner-model-bert\",\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " learning_rate=2e-4,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " num_train_epochs=8,\n", " weight_decay=0.01,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"eval_loss\",\n", " push_to_hub=False,\n", " report_to=\"none\",\n", " logging_steps=200,\n", " save_total_limit=2\n", ")\n", "\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=tokenized_datasets[\"train\"],\n", " eval_dataset=tokenized_datasets[\"validation\"],\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", " callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]\n", ")\n", "\n", "print(\"\\nНАЧИНАЕМ ОБУЧЕНИЕ:\\n\")\n", "trainer.train()\n", "\n", "# Сохраняем\n", "model_save_path = \"./recipe-ner-model-bert-final\"\n", "trainer.save_model(model_save_path)\n", "hf_tokenizer.save_pretrained(model_save_path)\n", "\n", "with open(f\"{model_save_path}/label_mappings.json\", 'w') as f:\n", " json.dump({\"label2id\": label2id, \"id2label\": id2label}, f)\n", "\n", "print(f\"\\nМодель сохранена в {model_save_path}\")\n", "\n", "# Результаты\n", "eval_results = trainer.evaluate()\n", "print(f\"\\nРЕЗУЛЬТАТЫ:\")\n", "print(f\"Loss: {eval_results['eval_loss']:.4f}\")\n", "print(f\"F1: {eval_results['eval_f1']:.4f}\")\n", "print(f\"Accuracy: {eval_results['eval_accuracy']:.4f}\")\n", "print(f\"Precision:{eval_results['eval_precision']:.4f}\")\n", "print(f\"Recall: {eval_results['eval_recall']:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "e40c2edbd73c40b098deaefa7e73b12c", "9036e109c0bc4d3087e1e9dce2040b41", "64bd865678ee4685acfdcdd5168f45b1", "582346e1a00e4be98a7e3673459b2d2c", "199acde91cb54af7a771e7bce4dda643", "b3c2fc36b901478484e734ef5b53f20b", "a89fac260d374743bfd3e6ff237d65a2", "c12ef7eb858a4556988067f38707c0f5", "e380183690064cdda6945de0d00c88e5", "93d757baeb7049dd89af7ac8c24cf950", 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EpochTraining LossValidation LossPrecisionRecallF1Accuracy
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OTHER seems not to be NE tag.\n", " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [ "\n", "

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Пропускаем подслова (##)\n", " if token.startswith('##'):\n", " if current_entity:\n", " current_entity['word'] += token[2:]\n", " continue\n", "\n", " label = id2label.get(pred_id, 'O')\n", "\n", " # Сохраняем предыдущую сущность\n", " if current_entity:\n", " entities.append(current_entity)\n", " current_entity = None\n", "\n", " # Начинаем новую сущность\n", " if label not in ['O', 'OTHER']:\n", " current_entity = {'word': token, 'entity': label}\n", "\n", " if current_entity:\n", " entities.append(current_entity)\n", "\n", " return entities\n", "\n", "# Тестируем\n", "test_recipes = [\n", " \"Mix 200 grams of flour with 2 eggs and 100 ml of milk\",\n", " \"Add salt and pepper to taste\",\n", " \"Bake at 350 degrees for 30 minutes\",\n", " \"Preheat oven to 180 degrees Celsius\",\n", " \"Chop onions and garlic finely\",\n", " \"1/4 cups cooked and pureed fresh butternut squash\",\n", " \"Add vanilla extract and a pinch of salt\",\n", " \"Cook for 20-25 minutes until golden brown\"\n", "]\n", "\n", "print(\"Результаты:\\n\")\n", "for recipe in test_recipes:\n", " print(f\"- {recipe}\")\n", " entities = predict_ner(recipe, model, hf_tokenizer, id2label)\n", " if entities:\n", " for entity in entities:\n", " print(f\" {entity['word']}: {entity['entity']}\")\n", " else:\n", " print(\" Сущности не найдены\")\n", " print()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yLtuayYYvMP0", "outputId": "d62fa7f6-35bb-47b2-82db-abe3dbdf74db" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Результаты:\n", "\n", "- Mix 200 grams of flour with 2 eggs and 100 ml of milk\n", " mix: B-COMMENT\n", " 200: I-COMMENT\n", " grams: I-COMMENT\n", " of: I-COMMENT\n", " flour: B-NAME\n", " with: B-COMMENT\n", " 2: B-QTY\n", " eggs: I-COMMENT\n", " and: I-COMMENT\n", " 100: I-COMMENT\n", " ml: I-COMMENT\n", " of: I-COMMENT\n", " milk: I-COMMENT\n", "\n", "- Add salt and pepper to taste\n", " add: B-COMMENT\n", " salt: B-NAME\n", " and: I-NAME\n", " pepper: I-NAME\n", " to: B-COMMENT\n", " taste: I-COMMENT\n", "\n", "- Bake at 350 degrees for 30 minutes\n", " bake: B-NAME\n", " at: B-COMMENT\n", " 350: I-COMMENT\n", " degrees: I-COMMENT\n", " for: I-COMMENT\n", " 30: I-COMMENT\n", " minutes: I-COMMENT\n", "\n", "- Preheat oven to 180 degrees Celsius\n", " preheat: B-COMMENT\n", " oven: B-COMMENT\n", " to: B-NAME\n", " 180: I-COMMENT\n", " degrees: I-COMMENT\n", " celsius: B-NAME\n", "\n", "- Chop onions and garlic finely\n", " chop: B-COMMENT\n", " onions: B-NAME\n", " and: I-NAME\n", " garlic: B-NAME\n", " finely: B-COMMENT\n", "\n", "- 1/4 cups cooked and pureed fresh butternut squash\n", " 1: B-QTY\n", " /: B-UNIT\n", " 4: B-QTY\n", " cups: B-UNIT\n", " cooked: B-COMMENT\n", " and: I-COMMENT\n", " pureed: I-COMMENT\n", " fresh: I-COMMENT\n", " butternut: B-NAME\n", " squash: I-NAME\n", "\n", "- Add vanilla extract and a pinch of salt\n", " add: B-COMMENT\n", " vanilla: B-NAME\n", " extract: I-NAME\n", " and: I-NAME\n", " pinch: I-COMMENT\n", " salt: B-NAME\n", "\n", "- Cook for 20-25 minutes until golden brown\n", " cook: B-COMMENT\n", " for: B-COMMENT\n", " 20: I-COMMENT\n", " -: B-COMMENT\n", " 25: I-COMMENT\n", " minutes: I-COMMENT\n", " until: I-COMMENT\n", " golden: I-COMMENT\n", " brown: B-NAME\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Улучшенная функция предсказания:**" ], "metadata": { "id": "KebImO4pwKMv" } }, { "cell_type": "code", "source": [ "def predict_ner_improved(text, model, tokenizer, id2label):\n", " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=512)\n", "\n", " device = next(model.parameters()).device\n", " inputs = {k: v.to(device) for k, v in inputs.items()}\n", "\n", " model.eval()\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", " predictions = torch.argmax(outputs.logits, dim=2)[0].cpu().numpy()\n", " tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])\n", "\n", " # Собираем все токены с метками\n", " token_labels = []\n", " for token, pred_id in zip(tokens, predictions):\n", " if token in ['[CLS]', '[SEP]', '[PAD]']:\n", " continue\n", " if token.startswith('##'):\n", " if token_labels:\n", " token_labels[-1]['word'] += token[2:]\n", " continue\n", "\n", " label = id2label.get(pred_id, 'O')\n", " token_labels.append({'word': token, 'entity': label})\n", "\n", " # Группируем последовательные токены с одинаковой меткой\n", " entities = []\n", " i = 0\n", " while i < len(token_labels):\n", " token = token_labels[i]\n", " label = token['entity']\n", "\n", " if label not in ['O', 'OTHER', 'B-COMMENT', 'I-COMMENT']:\n", " # Нашли значимую сущность\n", " entity_word = token['word']\n", " entity_label = label.replace('B-', '').replace('I-', '')\n", "\n", " # Собираем продолжение (I- токены)\n", " j = i + 1\n", " while j < len(token_labels) and token_labels[j]['entity'].startswith('I-'):\n", " entity_word += ' ' + token_labels[j]['word']\n", " j += 1\n", "\n", " entities.append({'word': entity_word, 'entity': entity_label})\n", " i = j\n", " else:\n", " i += 1\n", "\n", " return entities\n", "\n", "# Тестируем улучшенную версию\n", "test_recipes = [\n", " \"Mix 200 grams of flour with 2 eggs and 100 ml of milk\",\n", " \"Add salt and pepper to taste\",\n", " \"Bake at 350 degrees for 30 minutes\",\n", " \"Preheat oven to 180 degrees Celsius\",\n", " \"Chop onions and garlic finely\",\n", " \"1/4 cups cooked and pureed fresh butternut squash\",\n", " \"Add vanilla extract and a pinch of salt\",\n", " \"Cook for 20-25 minutes until golden brown\"\n", "]\n", "\n", "print(\"Улучшенные результаты:\\n\")\n", "for recipe in test_recipes:\n", " print(f\"- {recipe}\")\n", " entities = predict_ner_improved(recipe, model, hf_tokenizer, id2label)\n", " if entities:\n", " for entity in entities:\n", " print(f\" {entity['word']}: {entity['entity']}\")\n", " else:\n", " print(\" Сущности не найдены\")\n", " print()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "prtgbmrzv7-o", "outputId": "f5ae7647-67ea-4aac-81b0-1861141448a5" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Улучшенные результаты:\n", "\n", "- Mix 200 grams of flour with 2 eggs and 100 ml of milk\n", " flour: NAME\n", " 2 eggs and 100 ml of milk: QTY\n", "\n", "- Add salt and pepper to taste\n", " salt and pepper: NAME\n", "\n", "- Bake at 350 degrees for 30 minutes\n", " bake: NAME\n", "\n", "- Preheat oven to 180 degrees Celsius\n", " to 180 degrees: NAME\n", " celsius: NAME\n", "\n", "- Chop onions and garlic finely\n", " onions and: NAME\n", " garlic: NAME\n", "\n", "- 1/4 cups cooked and pureed fresh butternut squash\n", " 1: QTY\n", " /: UNIT\n", " 4: QTY\n", " cups: UNIT\n", " butternut squash: NAME\n", "\n", "- Add vanilla extract and a pinch of salt\n", " vanilla extract and: NAME\n", " salt: NAME\n", "\n", "- Cook for 20-25 minutes until golden brown\n", " brown: NAME\n", "\n" ] } ] } ] }