finbeto

Model Card: finbeto-lora

Purpose

finbeto-lora analyzes sentiment in Spanish financial news headlines. It is designed for financial text classification (positive, negative, neutral) in Spanish.

Training Details

• Base model: dccuchile/bert-base-spanish-wwm-cased
• Adapter: LoRA (PEFT)
• Datasets:
  • data/raw/financial_news.csv (Spanish headlines)
  • data/processed/financial_phrasebank_google_translate_es.csv (PhraseBank, translated)
• Key hyperparameters:
  • learning_rate: ~2.8e-5
  • weight_decay: 0.1
  • num_train_epochs: 3
  • batch_size: 16 (train), 32 (eval)
  • LoRA rank: 4, alpha: 32, dropout: 0.1
• Precision: fp16

Metrics

• Classification Report:

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Usage Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("EuclidesHernandez/finbeto")
model = AutoModelForSequenceClassification.from_pretrained("EuclidesHernandez/finbeto")

text = "La empresa reportó un crecimiento significativo en el último trimestre."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
		logits = model(**inputs).logits
		pred = torch.argmax(logits, dim=1).item()
		print(["negative", "neutral", "positive"][pred])

Contact:

For more information or to stay in touch, please visit: https://github.com/euclideshh/FinancialNewsSentimentAnalysis

License

MIT License