Last commit not found
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| import torch | |
| from torch.optim import Adam | |
| from torch.utils.data import DataLoader, Dataset | |
| import json | |
| import tqdm | |
| tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") | |
| model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") | |
| class MultilingualChatData(Dataset): | |
| def __init__(self, file_path, tokenizer, max_length=512): | |
| with open(file_path, 'r', encoding='utf-8') as f: | |
| self.data = json.load(f) | |
| self.tokenizer = tokenizer | |
| self.max_length = max_length | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| item = self.data[idx] | |
| input_text = f"<startofstring> {item['input']} <bot>: {item['output']} <endofstring>" | |
| encoding = self.tokenizer(input_text, truncation=True, padding='max_length', max_length=self.max_length, return_tensors="pt") | |
| return encoding['input_ids'].squeeze(), encoding['attention_mask'].squeeze() | |
| class MultilingualChatbot: | |
| def __init__(self): | |
| self.models = { | |
| 'en': GPT2LMHeadModel.from_pretrained("microsoft/DialoGPT-medium"), | |
| 'es': GPT2LMHeadModel.from_pretrained("DeepESP/gpt2-spanish"), | |
| 'fr': GPT2LMHeadModel.from_pretrained("asi/gpt-fr-cased-small") | |
| } | |
| self.tokenizers = { | |
| 'en': GPT2Tokenizer.from_pretrained("microsoft/DialoGPT-medium"), | |
| 'es': GPT2Tokenizer.from_pretrained("DeepESP/gpt2-spanish"), | |
| 'fr': GPT2Tokenizer.from_pretrained("asi/gpt-fr-cased-small") | |
| } | |
| for tokenizer in self.tokenizers.values(): | |
| tokenizer.pad_token = tokenizer.eos_token | |
| tokenizer.add_special_tokens({ | |
| "bos_token": "<startofstring>", | |
| "eos_token": "<endofstring>" | |
| }) | |
| tokenizer.add_tokens(["<bot>:"]) | |
| for model in self.models.values(): | |
| model.resize_token_embeddings(len(self.tokenizers['en'])) # Assuming all tokenizers have the same vocabulary size | |
| self.device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" | |
| for model in self.models.values(): | |
| model.to(self.device) | |
| def train(self, lang, data_file, epochs=5, batch_size=32, learning_rate=1e-4): | |
| model = self.models[lang] | |
| tokenizer = self.tokenizers[lang] | |
| chat_data = MultilingualChatData(data_file, tokenizer) | |
| data_loader = DataLoader(chat_data, batch_size=batch_size, shuffle=True) | |
| optimizer = Adam(model.parameters(), lr=learning_rate) | |
| model.train() | |
| for epoch in range(epochs): | |
| total_loss = 0 | |
| for batch in tqdm.tqdm(data_loader, desc=f"Epoch {epoch+1}/{epochs}"): | |
| input_ids, attention_mask = [b.to(self.device) for b in batch] | |
| optimizer.zero_grad() | |
| outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids) | |
| loss = outputs.loss | |
| loss.backward() | |
| optimizer.step() | |
| total_loss += loss.item() | |
| print(f"Epoch {epoch+1}/{epochs}, Loss: {total_loss/len(data_loader):.4f}") | |
| torch.save(model.state_dict(), f"model_state_{lang}.pt") | |
| def generate_response(self, prompt, src_lang): | |
| model = self.models.get(src_lang, self.models['en']) | |
| tokenizer = self.tokenizers.get(src_lang, self.tokenizers['en']) | |
| input_text = f"<startofstring> {prompt} <bot>: " | |
| input_ids = tokenizer.encode(input_text, return_tensors='pt').to(self.device) | |
| attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=self.device) | |
| output = model.generate( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| max_length=1000, | |
| pad_token_id=tokenizer.eos_token_id, | |
| no_repeat_ngram_size=3, | |
| do_sample=True, | |
| top_k=50, | |
| top_p=0.95, | |
| temperature=0.7, | |
| num_return_sequences=1, | |
| length_penalty=1.0, | |
| repetition_penalty=1.2 | |
| ) | |
| decoded_output = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return decoded_output.split("<bot>:")[-1].strip() | |
| def initialize_chatbot(): | |
| return MultilingualChatbot() | |
| def get_chatbot_response(chatbot, prompt, src_lang): | |
| return chatbot.generate_response(prompt, src_lang) | |
| # Ejemplo de uso | |
| if __name__ == "__main__": | |
| chatbot = initialize_chatbot() | |
| # Entrenar el modelo en español (asumiendo que tienes un archivo de datos en español) | |
| chatbot.train('es', './spanish_chat_data.json', epochs=3) | |
| # Generar respuestas | |
| print(get_chatbot_response(chatbot, "Hola, ¿cómo estás?", 'es')) | |
| print(get_chatbot_response(chatbot, "Hello, how are you?", 'en')) | |
| print(get_chatbot_response(chatbot, "Bonjour, comment allez-vous?", 'fr')) |