Modelpublic

rafaelwt/beto-amazon-reviews-es-3clases

sourceHugging Faceapache-2.0updated 20d agoView on Hugging Face

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Model Card

BETO fine-tuned for 3-class Spanish customer sentiment

Fine-tuned version of dccuchile/bert-base-spanish-wwm-cased (BETO) for classifying short Spanish customer texts into three business classes: negativo, neutro, positivo. Built for Laboratorio 4 (Módulo 10, Natural Language Processing) by Grupo 5.

Labels

id	label	source stars
0	negativo	1-2
1	neutro	3
2	positivo	4-5

Training

—Dataset: full train split of SetFit/amazon_reviews_multi_es (200,000 reviews); official validation (5,000) and test (5,000).
—Max length 128 WordPiece tokens, batch size 32, learning rate 2e-5, weight decay 0.01, 10% warmup, fp16, 3 epochs with evaluation per epoch and best checkpoint by macro F1 (epoch 2).
—Hardware: single NVIDIA GeForce RTX 4060 (8 GB), 38.6 min.

Test results (5,000 reviews)

model	accuracy	macro F1	F1 negativo	F1 neutro	F1 positivo
TF-IDF + Logistic Regression (baseline)	0.737	0.705	0.798	0.492	0.823
BETO fine-tuned (this model)	0.793	0.738	0.850	0.487	0.876

Most errors involve the neutro class. Sarcastic texts tend to be classified as positivo.

Usage

python

from transformers import pipeline

clf = pipeline("text-classification", model="rafaelwt/beto-amazon-reviews-es-3clases")
clf("Me cobraron un monto que no corresponde en mi factura.")