Create README.md

README.md

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+
+checkpoint_path = "model_checkpoint.pt"
+
+if os.path.exists(checkpoint_path):
+    checkpoint = torch.load(checkpoint_path)
+    embedding_layer.load_state_dict(checkpoint['embedding_state'])
+    transformer_encoderLayer.load_state_dict(checkpoint['transformer_state'])
+    output_layer.load_state_dict(checkpoint['output_state'])
+    optimizer.load_state_dict(checkpoint['optimizer_state'])
+    start_epoch = checkpoint['epoch'] + 1
+    print(f" Модель загружена, продолжаем с эпохи {start_epoch}")
+else:
+    start_epoch = 0
+    print(" Чекпоинт не найден, начинаем обучение с нуля")
+
+
+epochNum = 20
+for epoch in range(epochNum):
+    optimizer.zero_grad()
+    epochmy = start_epoch + epoch
+    embedded = embedding_layer(input_ids)
+    src = embedded.transpose(0, 1)
+
+    outputTransformer = transformer_encoderLayer(src, src_key_padding_mask=(attention_mask == 0))
+    outputTransformer = outputTransformer.transpose(0, 1)  # обратно [batch, seq_len, embedding_dim]
+
+    logits = output_layer(outputTransformer)
+    loss = criterion(logits.view(-1, vocab_size), target_ids.view(-1))
+    loss.backward()
+    optimizer.step() 
+
+    with torch.no_grad():
+        embedded = embedding_layer(input_ids)
+        src = embedded.transpose(0, 1)
+        outputTransformer = transformer_encoderLayer(src, src_key_padding_mask=(attention_mask == 0))
+        outputTransformer = outputTransformer.transpose(0, 1)
+        logits = output_layer(outputTransformer)  # [batch, seq_len, vocab_size]
+
+
+        predicted_token_ids = torch.argmax(logits, dim=-1)  # [batch, seq_len]
+
+        predicted_text = tokenizer.batch_decode(predicted_token_ids, skip_special_tokens=True)
+        print("Predicted text:", predicted_text[0])
+
+print(f"Epoch [{epoch + 1}/{epochNum}] — Loss: {loss.item():.4f}")
+torch.save({
+    'embedding_state': embedding_layer.state_dict(),
+    'transformer_state': transformer_encoderLayer.state_dict(),
+    'output_state': output_layer.state_dict(),
+    'optimizer_state': optimizer.state_dict(),
+    'epoch': epochmy
+}, "model_checkpoint.pt")