readme: ---
  language: es
  tags:
  - sagemaker
  - bertin
  - TextClassification
  - SentimentAnalysis
  license: apache-2.0
  datasets:
  - IMDbreviews_es
  metrics:
  - accuracy
  model-index:
  - name: bertin_base_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: 0.898933
         - name: F1 Score
           type: f1
           value: 0.8989063
         - name: Precision
           type: precision
           value: 0.8771473
         - name: Recall
           type: recall
           value: 0.9217724
  widget:
  - text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
  ---
  
  # Model bertin_base_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 **Bertin base** which is a RoBERTa-base model pre-trained  on the Spanish portion of mC4 using Flax.
  It was trained by the Bertin Project.[Link to base model](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) 
  
  Article: BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling
  - Author = Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury,
  - journal = Procesamiento del Lenguaje Natural,
  - volume = 68, number = 0, year = 2022
  - url = http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403
  
  ## 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": "\"bertin-project/bertin-roberta-base-spanish\"",
      "sagemaker_container_log_level": "20",
      "sagemaker_program": "\"train.py\"",
      }
  
  ## Evaluation results
  
  - Accuracy = 0.8989333333333334
  
  - F1 Score = 0.8989063750333421
  
  - Precision = 0.877147319104633
  
  - Recall = 0.9217724288840262
  
  ## Test results
  
  ## Model in action
  
  ### Usage for Sentiment Analysis
  
  ```python
  import torch
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("edumunozsala/bertin_base_sentiment_analysis_es")
  model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/bertin_base_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)
  