| 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")
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| model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
|
|
|
| class MultilingualChatData(Dataset):
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| def __init__(self, file_path, tokenizer, max_length=512):
|
| with open(file_path, 'r', encoding='utf-8') as f:
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| self.data = json.load(f)
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| self.tokenizer = tokenizer
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| 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 = {
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| 'en': GPT2LMHeadModel.from_pretrained("microsoft/DialoGPT-medium"),
|
| 'es': GPT2LMHeadModel.from_pretrained("DeepESP/gpt2-spanish"),
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| 'fr': GPT2LMHeadModel.from_pretrained("asi/gpt-fr-cased-small")
|
| }
|
| self.tokenizers = {
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| 'en': GPT2Tokenizer.from_pretrained("microsoft/DialoGPT-medium"),
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| 'es': GPT2Tokenizer.from_pretrained("DeepESP/gpt2-spanish"),
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| '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>",
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| "eos_token": "<endofstring>"
|
| })
|
| tokenizer.add_tokens(["<bot>:"])
|
|
|
| for model in self.models.values():
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| model.resize_token_embeddings(len(self.tokenizers['en']))
|
|
|
| self.device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
|
| for model in self.models.values():
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| model.to(self.device)
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|
|
| def train(self, lang, data_file, epochs=5, batch_size=32, learning_rate=1e-4):
|
| model = self.models[lang]
|
| tokenizer = self.tokenizers[lang]
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|
|
| 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()
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| 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)
|
|
|
|
|
| if __name__ == "__main__":
|
| chatbot = initialize_chatbot()
|
|
|
|
|
| chatbot.train('es', './spanish_chat_data.json', epochs=3)
|
|
|
|
|
| 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')) |
