Epoch	Loss	Accuracy	F1 Score	Precision	Recall
1	1.0415	0.3796	0.2859	0.2294	0.3796
2	0.8871	0.5949	0.5811	0.6149	0.5949
3	0.9549	0.5528	0.4932	0.6216	0.5528
4	0.9607	0.6059	0.5713	0.6096	0.6059
5	0.9084	0.6335	0.6301	0.6315	0.6335
6	0.8614	0.6722	0.6702	0.6696	0.6722
7	0.8683	0.6798	0.6774	0.6762	0.6798
8	1.1485	0.6798	0.6786	0.6815	0.6798
9	1.3851	0.6888	0.6876	0.6874	0.6888
10	1.5043	0.6770	0.6762	0.6759	0.6770

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Cargar modelo y tokenizer
tokenizer = AutoTokenizer.from_pretrained("tu-usuario/xlm-roberta-large-tass-sentiment-bs8")
model = AutoModelForSequenceClassification.from_pretrained("tu-usuario/xlm-roberta-large-tass-sentiment-bs8")

# Ejemplo de uso
text = "Me encanta este producto, es excelente"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

labels = {0: "Negativo", 1: "Neutral", 2: "Positivo"}
print(f"Sentimiento: {labels[predicted_class]}")
print(f"Confianza: {predictions[0][predicted_class].item():.4f}")

Como Pipeline

python

from transformers import pipeline

# Usar como pipeline
classifier = pipeline('sentiment-analysis', model='tu-usuario/xlm-roberta-large-tass-sentiment-bs8')

result = classifier("Me encanta este producto, es excelente")
print(result)
# Output: [{'label': 'LABEL_2', 'score': 0.95}]
# LABEL_0 = Negativo, LABEL_1 = Neutral, LABEL_2 = Positivo

Labels

0 (LABEL_0): Negative sentiment
1 (LABEL_1): Neutral sentiment
2 (LABEL_2): Positive sentiment

Training Configuration

The model was trained with weighted loss to handle class imbalance.

Distribution in training set (estimated):

Negative samples: ~1854 (~40%)
Neutral samples: ~1391 (~30%)
Positive samples: ~1391 (~30%)

Limitations and Bias

This model is specifically trained on Spanish Twitter data
Performance may vary on other Spanish text domains
The model classifies sentiment into three categories (negative, neutral, positive)
May reflect biases present in the TASS Twitter dataset

Yenreh/xlm-roberta-large-tass-sentiment-bs8

XLM-RoBERTa Large Fine-tuned for Spanish Sentiment Analysis (TASS)

Model Description

Training Details

Performance (Test Set)

Training History (Validation Set)

Usage

Como Pipeline

Labels

Training Configuration

Limitations and Bias

Yenreh/
xlm-roberta-large-tass-sentiment-bs8