DjSteker/spanish-gpt2

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Uses

!pip install transformers datasets

from IPython.display import HTML

def display_df(df, max_cols=15, header=True, index=True): return display(HTML(df.to_html(header=header,index=index, max_cols=max_cols)))

import torch from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "DjSteker/spanish-gpt2" tokenizer = AutoTokenizer.from_pretrained(model_name)

Añadimos el token EOS como token PAD para evitar warnings

model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id).to(device)

print(str(device))

def model_size(model): return sum(t.numel() for t in model.parameters())

print(f"Tamaño del GPT español: {model_size(model)/1000**2:.1f}M parámetros")

import pandas as pd

Aqui es el "prompt" para continuar

input_txt =" Le tiro una piedra a la cabeza" # "El amor es eterno mientras dura. "

input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device) iterations = [] n_steps = 12 choices_per_step = 5

with torch.no_grad(): for _ in range(n_steps): iteration = dict() iteration["Input"] = tokenizer.decode(input_ids[0]) output = model(input_ids=input_ids) # Seleccionar los logits del primer batch y del último token y aplicar softmax next_token_logits = output.logits[0, -1, :] next_token_probs = torch.softmax(next_token_logits, dim=-1) sorted_ids = torch.argsort(next_token_probs, dim=-1, descending=True) # Almacenar las tokens con mayores probabilidades for choice_idx in range(choices_per_step): token_id = sorted_ids[choice_idx] token_prob = next_token_probs[token_id].cpu().numpy() token_choice = ( f"{tokenizer.decode(token_id)} ({100 * token_prob:.2f}%)" ) iteration[f"Choice {choice_idx+1}"] = token_choice # Añadir el siguiente token previsto a los inputs input_ids = torch.cat([input_ids, sorted_ids[None, 0, None]], dim=-1) iterations.append(iteration)

display_df(pd.DataFrame.from_records(iterations), index=None)

input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device) output = model.generate(input_ids, max_length=20) print(tokenizer.decode(output[0]))

max_length = 256 input_txt ="""En un hallazgo sorprendente, los científicos descubrieron una manada de unicornios en la Luna
que vivía en un valle remoto, hasta ahora inexplorado, en la cordillera de Mons Agnes.
Más sorprendente aún para los investigadores fue el hecho de que los unicornios hablaban
un inglés perfecto. """ input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device) output_greedy = model.generate(input_ids, max_length=max_length, do_sample=False) print(tokenizer.decode(output_greedy[0]))

torch.manual_seed(42) output_temp = model.generate(input_ids, max_length=max_length, do_sample=True, temperature=2.0, top_k=0) print(tokenizer.decode(output_temp[0]))

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Recommendations

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How to Get Started with the Model

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Training Details

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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