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Model Details
Model Description
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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]))
Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Results
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Summary
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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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