readme: ---
  language: es
  tags:
  - sagemaker
  - ruperta
  - TextClassification
  - SentimentAnalysis
  license: apache-2.0
  datasets:
  - IMDbreviews_es
  model-index:
  name: RuPERTa_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: 0.881866
         - name: F1 Score,
           type: f1,
           value: 0.008272
         - name: Precision,
           type: precision,
           value: 0.858605
         - name: Recall,
           type: recall,
           value: 0.920062
  widget:
  - text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
  ---
  
  ## Model `RuPERTa_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 **RuPERTa-base (uncased)** which is a RoBERTa model trained on a uncased version of big Spanish corpus.
  It was trained by mrm8488, Manuel Romero.[Link to base model](https://huggingface.co/mrm8488/RuPERTa-base) 
  
  ## 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
  
  
  ## Hyperparameters
      {
      "epochs": "4",
      "train_batch_size": "32",    
      "eval_batch_size": "8",
      "fp16": "true",
      "learning_rate": "3e-05",
      "model_name": "\"mrm8488/RuPERTa-base\"",
      "sagemaker_container_log_level": "20",
      "sagemaker_program": "\"train.py\"",
      }
  
  ## Evaluation results
  Accuracy = 0.8629333333333333
  F1 Score = 0.8648790746582545
  Precision = 0.8479381443298969
  Recall = 0.8825107296137339
  
  ## Test results
  Accuracy = 0.8066666666666666
  F1 Score = 0.8057862309134743
  Precision = 0.7928307854507116
  Recall = 0.8191721132897604
  
  ## Model in action
  
  ### Usage for Sentiment Analysis
  
  ```python
  import torch
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("edumunozsala/RuPERTa_base_sentiment_analysis_es")
  model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/RuPERTa_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)
  