| # This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small | |
| The present model supports 6 languages - | |
| 1) English | |
| 2) Hindi | |
| 3) German | |
| 4) Korean | |
| 5) Japanese | |
| 6) Portuguese | |
| Here is how to use this model | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import torch | |
| model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/mt5-multilingual-sentiment") | |
| tokenizer = AutoTokenizer.from_pretrained("Chirayu/mt5-multilingual-sentiment") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| def get_sentiment(text, num_beams=2,max_length=512, repetition_penalty=2.5, length_penalty=1, early_stopping=True,top_p=.95, top_k=50, num_return_sequences=1): | |
| input_ids = tokenizer.encode( | |
| text, return_tensors="pt", add_special_tokens=True | |
| ) | |
| input_ids = input_ids.to(device) | |
| generated_ids = model.generate( | |
| input_ids=input_ids, | |
| num_beams=num_beams, | |
| max_length=max_length, | |
| repetition_penalty=repetition_penalty, | |
| length_penalty=length_penalty, | |
| early_stopping=early_stopping, | |
| top_p=top_p, | |
| top_k=top_k, | |
| num_return_sequences=num_return_sequences, | |
| ) | |
| sentiment = [tokenizer.decode(generated_id,skip_special_tokens=True,clean_up_tokenization_spaces=True,) for generated_id in generated_ids] | |
| return sentiment | |
| ``` |