Negativo Positivo
---
language: es
tags:
- sagemaker
- roberta-bne
- TextClassification
- SentimentAnalysis
license: apache-2.0
datasets:
- IMDbreviews_es
metrics:
- accuracy
model-index:
- name: roberta_bne_sentiment_analysis_es
  results:
  - task:
        name: Sentiment Analysis
        type: sentiment-analysis
    dataset:
        name: "IMDb Reviews in Spanish" 
        type: IMDbreviews_es
    metrics:
       - name: Accuracy,
         type: accuracy,
         value: 0.9106666666666666
       - name: F1 Score,
         type: f1,
         value: 0.9090909090909091
       - name: Precision,
         type: precision,
         value: 0.9063852813852814
       - name: Recall,
         type: recall,
         value: 0.9118127381600436
widget:
- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
---

# Model roberta_bne_sentiment_analysis_es

## **A finetuned model for Sentiment analysis in Spanish**

This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **RoBERTa-base-bne** which is a RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB.
It was trained by The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html)


**RoBERTa BNE Citation**
Check out the paper for all the details: https://arxiv.org/abs/2107.07253

```
@article{gutierrezfandino2022,
	author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas},
	title = {MarIA: Spanish Language Models},
	journal = {Procesamiento del Lenguaje Natural},
	volume = {68},
	number = {0},
	year = {2022},
	issn = {1989-7553},
	url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405},
	pages = {39--60}
}
```

## Dataset
The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages. 

Sizes of datasets:
- Train dataset: 42,500
- Validation dataset: 3,750
- Test dataset: 3,750

## Intended uses & limitations

This model is intented for Sentiment Analysis for spanish corpus and finetuned specially for movie reviews but it can be applied to other kind of reviews.

## Hyperparameters
    {
    "epochs": "4",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "3e-05",
    "model_name": "\"PlanTL-GOB-ES/roberta-base-bne\"",
    "sagemaker_container_log_level": "20",
    "sagemaker_program": "\"train.py\"",
    }

## Evaluation results

- Accuracy = 0.9106666666666666

- F1 Score = 0.9090909090909091

- Precision = 0.9063852813852814

- Recall = 0.9118127381600436

## Test results

## Model in action

### Usage for Sentiment Analysis

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es")
model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es")

text ="Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"

input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
outputs = model(input_ids)
output = outputs.logits.argmax(1)
```

Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)
