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"cells": [
{
"cell_type": "markdown",
"id": "056645ed",
"metadata": {},
"source": [
"# Обучение классификатора тематик статей\n",
"\n",
"Этот ноутбук повторяет логику `src/train.py`, но в формате, удобном для сдачи и экспериментов.\n",
"Перед запуском скачайте датасет и положите его в `data/`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7563cfb3",
"metadata": {},
"outputs": [],
"source": [
"!pip install -q transformers datasets accelerate evaluate scikit-learn pandas pyarrow"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4fc396eb",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"DATA_PATH = Path(\"data/arxiv_data.csv\")\n",
"MODEL_NAME = \"allenai/scibert_scivocab_uncased\"\n",
"OUTPUT_DIR = Path(\"artifacts/article_topic_model\")\n",
"MAX_LENGTH = 256\n",
"TEST_SIZE = 0.15\n",
"SEED = 42"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a84a37ac",
"metadata": {},
"outputs": [],
"source": [
"from src.data_utils import load_dataset_frame, filter_rare_classes\n",
"\n",
"df = load_dataset_frame(DATA_PATH, text_cols=[\"title\", \"abstract\"], label_col=\"tag\")\n",
"df = filter_rare_classes(df, min_examples_per_class=20)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b02568e7",
"metadata": {},
"outputs": [],
"source": [
"df[\"label\"].value_counts().head(20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9721b73f",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"labels = sorted(df[\"label\"].unique().tolist())\n",
"label2id = {label: idx for idx, label in enumerate(labels)}\n",
"id2label = {idx: label for label, idx in label2id.items()}\n",
"df[\"label_id\"] = df[\"label\"].map(label2id)\n",
"\n",
"train_df, valid_df = train_test_split(\n",
" df,\n",
" test_size=TEST_SIZE,\n",
" random_state=SEED,\n",
" stratify=df[\"label_id\"],\n",
")\n",
"\n",
"len(train_df), len(valid_df), len(labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11dc01f7",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import evaluate\n",
"from datasets import Dataset, DatasetDict\n",
"from transformers import (\n",
" AutoTokenizer,\n",
" AutoModelForSequenceClassification,\n",
" DataCollatorWithPadding,\n",
" Trainer,\n",
" TrainingArguments,\n",
")\n",
"\n",
"ds = DatasetDict({\n",
" \"train\": Dataset.from_pandas(train_df[[\"text\", \"label_id\"]], preserve_index=False),\n",
" \"validation\": Dataset.from_pandas(valid_df[[\"text\", \"label_id\"]], preserve_index=False),\n",
"})\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
"\n",
"def tokenize_batch(batch):\n",
" return tokenizer(batch[\"text\"], truncation=True, max_length=MAX_LENGTH)\n",
"\n",
"tokenized = ds.map(tokenize_batch, batched=True)\n",
"tokenized = tokenized.rename_column(\"label_id\", \"labels\")\n",
"tokenized.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
"\n",
"model = AutoModelForSequenceClassification.from_pretrained(\n",
" MODEL_NAME,\n",
" num_labels=len(labels),\n",
" id2label={int(k): v for k, v in id2label.items()},\n",
" label2id=label2id,\n",
")\n",
"\n",
"accuracy_metric = evaluate.load(\"accuracy\")\n",
"f1_metric = evaluate.load(\"f1\")\n",
"\n",
"def compute_metrics(eval_pred):\n",
" logits, labels_np = eval_pred\n",
" preds = np.argmax(logits, axis=-1)\n",
" return {\n",
" \"accuracy\": accuracy_metric.compute(predictions=preds, references=labels_np)[\"accuracy\"],\n",
" \"f1_macro\": f1_metric.compute(predictions=preds, references=labels_np, average=\"macro\")[\"f1\"],\n",
" \"f1_weighted\": f1_metric.compute(predictions=preds, references=labels_np, average=\"weighted\")[\"f1\"],\n",
" }\n",
"\n",
"args = TrainingArguments(\n",
" output_dir=str(OUTPUT_DIR / \"checkpoints\"),\n",
" learning_rate=2e-5,\n",
" per_device_train_batch_size=8,\n",
" per_device_eval_batch_size=16,\n",
" num_train_epochs=3,\n",
" weight_decay=0.01,\n",
" evaluation_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" logging_steps=50,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"f1_macro\",\n",
" report_to=\"none\",\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=tokenized[\"train\"],\n",
" eval_dataset=tokenized[\"validation\"],\n",
" tokenizer=tokenizer,\n",
" data_collator=DataCollatorWithPadding(tokenizer=tokenizer),\n",
" compute_metrics=compute_metrics,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0f53c46",
"metadata": {},
"outputs": [],
"source": [
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17552445",
"metadata": {},
"outputs": [],
"source": [
"metrics = trainer.evaluate()\n",
"metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "702cda86",
"metadata": {},
"outputs": [],
"source": [
"OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
"trainer.model.save_pretrained(OUTPUT_DIR)\n",
"tokenizer.save_pretrained(OUTPUT_DIR)\n",
"\n",
"import json\n",
"(OUTPUT_DIR / \"label_mapping.json\").write_text(\n",
" json.dumps({\n",
" \"label2id\": label2id,\n",
" \"id2label\": {str(k): v for k, v in id2label.items()},\n",
" }, ensure_ascii=False, indent=2),\n",
" encoding=\"utf-8\",\n",
")\n",
"(OUTPUT_DIR / \"metrics.json\").write_text(\n",
" json.dumps(metrics, ensure_ascii=False, indent=2),\n",
" encoding=\"utf-8\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8cc9ca80",
"metadata": {},
"outputs": [],
"source": [
"from src.predictor import ArticleTopicPredictor\n",
"\n",
"predictor = ArticleTopicPredictor(OUTPUT_DIR)\n",
"predictor.predict(\n",
" title=\"Graph neural networks for molecular property prediction\",\n",
" abstract=\"We benchmark graph neural architectures on several chemistry tasks.\",\n",
")"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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