| # 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 = ["I love her sooo much", "I hate you!"] | |
| for text in texts: | |
| encoded_text = tokenizer.encode_plus(text, | |
| add_special_tokens=True, | |
| max_length=128, | |
| return_token_type_ids=True, | |
| padding="max_length", | |
| truncation=True, | |
| return_attention_mask=True) | |
| output = model(input_ids=torch.tensor(encoded_text['input_ids'], dtype=torch.long).unsqueeze(0), | |
| token_type_ids=torch.tensor(encoded_text['token_type_ids'], dtype=torch.long).unsqueeze(0), | |
| attention_mask=torch.tensor(encoded_text['attention_mask'], dtype=torch.long).unsqueeze(0)) | |
| 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])) | |
| ```` |