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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')) |