readme: # Vent-roBERTa-emotion
  
  This is a roBERTa pretrained on twitter and then trained for self-labeled emotion classification on the Vent dataset (see https://arxiv.org/abs/1901.04856). The Vent dataset contains 33 million posts annotated with one emotion by the user themselves. <br/>
     
  The model was trained to recognize 5 emotions ("Affection", "Anger", "Fear", "Happiness", "Sadness") on 7 million posts from the dataset. <br/>
  
  Example of how to use the classifier on single texts. <br/>
  
  ```` 
  from transformers import AutoModelForSequenceClassification
  from transformers import AutoTokenizer
  import numpy as np
  from scipy.special import softmax
  import torch
  
  tokenizer = AutoTokenizer.from_pretrained("lumalik/vent-roberta-emotion")
  model = AutoModelForSequenceClassification.from_pretrained("lumalik/vent-roberta-emotion")
  model.eval()
  
  texts = ["You wont believe what happened to me today",
           "You wont believe what happened to me today!",
           "You wont believe what happened to me today...",
           "You wont believe what happened to me today <3",
           "You wont believe what happened to me today :)",
           "You wont believe what happened to me today :("]
  
  for text in texts:
      encoded_text = tokenizer(text, return_tensors="pt")
  
      output = model(**encoded_text)
  
      output = softmax(output[0].detach().numpy(), axis=1)
  
      print("======================")
      print(text)
      print("Affection: {}".format(output[0][0]))
      print("Anger: {}".format(output[0][1]))
      print("Fear: {}".format(output[0][2]))
      print("Happiness: {}".format(output[0][3]))
      print("Sadness: {}".format(output[0][4]))
  ```` 