readme: ---
  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)
  