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import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim.lr_scheduler
from transformers import pipeline

# Hiperparámetros simples
max_new_tokens = 200  # For generation

# Carga el modelo pre-entrenado
generator = pipeline('text-generation', model='DeepESP/gpt2-spanish')

# Genera texto
def generate(start, max_new_tokens):
    result = generator(start, max_length=len(start.split()) + max_new_tokens, do_sample=True, temperature=0.8, num_return_sequences=1,truncation=True)
    return result[0]['generated_text']

print(generate('entonces', max_new_tokens))  # Prueba generando texto

Code: Select all | Expand

import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim.lr_scheduler
from transformers import pipeline

# Hiperparámetros simples
max_new_tokens = 200  # For generation

# Carga el modelo pre-entrenado
generator = pipeline('text-generation', model='gpt2')

# Genera texto
def generate(start, max_new_tokens):
    result = generator(start, max_length=len(start.split()) + max_new_tokens, do_sample=True, temperature=0.8, num_return_sequences=1,truncation=True)
    return result[0]['generated_text']

print(generate('then', max_new_tokens))  # Prueba generando texto

Code: Select all | Expand

import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim.lr_scheduler
from transformers import pipeline

# Hiperparámetros simples
max_new_tokens = 200  # For generation

# Carga el modelo pre-entrenado
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')

# Genera texto
def generate(start, max_new_tokens):
    result = generator(start, max_length=len(start.split()) + max_new_tokens, do_sample=True, temperature=0.8, num_return_sequences=1,truncation=True)
    return result[0]['generated_text']

print(generate('then', max_new_tokens))  # Prueba generando texto