release

.gitattributes

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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text

main.py

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+from dataset import examples
+
+count_text = len(examples.train_texts)
+count_lb = len(examples.train_labels)
+
+print(f"{count_text} / {count_lb}")
+
+print(examples.train_labels)
+

train.py

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+from dataset import examples
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+from datasets import Dataset
+import numpy as np
+from evaluate import load
+
+
+classes = examples.classes
+train_texts = examples.train_texts
+train_labels = examples.train_labels
+val_texts = examples.val_texts
+val_labels = examples.val_labels
+
+# ==============================================================================================================================================================================
+
+train_dataset = Dataset.from_dict({"text": train_texts, "label": train_labels})
+val_dataset = Dataset.from_dict({"text": val_texts, "label": val_labels})
+
+model_name = "DeepPavlov/rubert-base-cased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=128)
+
+train_dataset = train_dataset.map(tokenize_function, batched=True)
+val_dataset = val_dataset.map(tokenize_function, batched=True)
+
+train_dataset = train_dataset.remove_columns(["text"])
+val_dataset = val_dataset.remove_columns(["text"])
+
+train_dataset = train_dataset.with_format("torch")
+val_dataset = val_dataset.with_format("torch")
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(classes))
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=10,
+    load_best_model_at_end=True,
+    metric_for_best_model="accuracy"
+)
+
+accuracy_metric = load("accuracy")
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+    return accuracy_metric.compute(predictions=predictions, references=labels)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=val_dataset,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics
+)
+
+trainer.train()
+trainer.save_model("./trained_model")
+
+# Пример предсказания
+test_text = "Когда починят светофор на перекрестке?"
+inputs = tokenizer(test_text, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
+outputs = model(**inputs)
+predictions = torch.argmax(outputs.logits, dim=1).item()
+predicted_class = classes[predictions]
+print("Predicted class:", predicted_class)