readme: # roberta_sentiments_es_en , A Sentiment Analysis model for Spanish sentences
  
  This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis. This model currently supports Spanish sentences
  
  This is a enhanced version of 'Manauu17/roberta_sentiments_es' following the BERT's SOAT to acquire best results. The last 4 hidden layers were concatenated folowing dense layers to get classification results. 
  
  ## Example of classification
  
  ```python
  from transformers import AutoModelForSequenceClassification
  from transformers import AutoTokenizer
  import numpy as np
  import pandas as pd
  from scipy.special import softmax
  
  MODEL = 'Manauu17/enhanced_roberta_sentiments_es'
  
  tokenizer = AutoTokenizer.from_pretrained(MODEL)
  
  # PyTorch
  model = AutoModelForSequenceClassification.from_pretrained(MODEL)
  
  text = ['@usuario siempre es bueno la opinión de un playo',
  'Bendito año el que me espera']
  
  encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
  output = model(**encoded_input)
  scores = output[0].detach().numpy()
  
  labels_dict = model.config.id2label
  
  # Results
  def get_scores(model_output, labels_dict):
    scores = softmax(model_output)
    frame = pd.DataFrame(scores, columns=model.config.id2label.values())
    frame.style.highlight_max(axis=1,color="green")
    return frame
  
  
  # PyTorch
  get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")
  
  
  ```
  Output: 
  
  ```
  # PyTorch
  get_scores(scores, labels_dict).style.highlight_max(axis=1, color="green")
  
       Negative    Neutral     Positive
  0    0.000607    0.004851    0.906596
  1    0.079812    0.006650    0.001484
  
  ```
  