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---
license: apache-2.0
language:
- es
metrics:
- accuracy
pipeline_tag: text-classification
widget:
- text: Te quiero. Te amo
output:
- label: 'Positive'
score: 1.000
- label: 'Negative'
score: 0.000
---
# Spanish Sentiment Analysis Classifier
## Overview
This BERT-based text classifier was developed as a thesis project for the Computer Engineering degree at Universidad de Buenos Aires (UBA).
The model is designed to detect sentiments in Spanish and was fine-tuned on the *dccuchile/bert-base-spanish-wwm-uncased* model using a specific set of hyperparameters.
It was trained on a dataset containing 11,500 Spanish tweets collected from various regions, both positive and negative. These tweets were sourced from a well-curated combination of TASS datasets.
## Team Members
- **[Azul Fuentes](https://github.com/azu26)**
- **[Dante Reinaudo](https://github.com/DanteReinaudo)**
- **[Lucía Pardo](https://github.com/luciaPardo)**
- **[Roberto Iskandarani](https://github.com/Robert-Iskandarani)**
## Model Details
* **Base Mode**: dccuchile/bert-base-spanish-wwm-uncased
* **Hyperparameters**:
* **dropout_rate = 0.1**
* **num_classes = 2**
* **max_length = 128**
* **batch_size = 16**
* **num_epochs = 5**
* **learning_rate = 3e-5**
* **Dataset**: 11,500 Spanish tweets (Positive and Negative)
## Metrics
The model's performance was evaluated using the following metrics:
* **Accuracy = _86.47%_**
* **F1-Score = _86.47%_**
* **Precision = _86.46%_**
* **Recall = _86.51%_**
## Usage
### Installation
You can install the required dependencies using pip:
```bash
pip install transformers torch
```
### Loading the Model
```python
from transformers import BertForSequenceClassification, BertTokenizer
model = BertForSequenceClassification.from_pretrained("VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis")
tokenizer = BertTokenizer.from_pretrained("VerificadoProfesional/SaBERT-Spanish-Sentiment-Analysis")
```
### Predict Function
```python
def predict(model,tokenizer,text,threshold = 0.5):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=1).squeeze().tolist()
predicted_class = torch.argmax(logits, dim=1).item()
if probabilities[predicted_class] <= threshold and predicted_class == 1:
predicted_class = 0
return bool(predicted_class), probabilities
```
### Making Predictions
```python
text = "Your Spanish news text here"
predicted_label,probabilities = predict(model,tokenizer,text)
print(f"Text: {text}")
print(f"Predicted Class: {predicted_label}")
print(f"Probabilities: {probabilities}")
```
## License
* Apache License 2.0
* [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
## Acknowledgments
Special thanks to DCC UChile for the base Spanish BERT model and to all contributors to the dataset used for training.
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