| import streamlit as st | |
| import torch | |
| import numpy as np | |
| import transformers | |
| import pickle | |
| def load_model(): | |
| model_finetuned = transformers.AutoModel.from_pretrained( | |
| "nghuyong/ernie-2.0-base-en", | |
| output_attentions = False, | |
| output_hidden_states = False | |
| ) | |
| model_finetuned.load_state_dict(torch.load('ErnieModel_imdb.pt')) | |
| tokenizer = transformers.AutoTokenizer.from_pretrained("nghuyong/ernie-2.0-base-en") | |
| return model_finetuned, tokenizer | |
| def preprocess_text(text_input, max_len, tokenizer): | |
| input_tokens = tokenizer( | |
| text_input, | |
| return_tensors='pt', | |
| padding=True, | |
| max_length=max_len, | |
| truncation = True | |
| ) | |
| return input_tokens | |
| def predict_sentiment(model, input_tokens): | |
| id2label = {0: "NEGATIVE", 1: "POSITIVE"} | |
| output = model(**input_tokens).pooler_output.detach().numpy() | |
| with open('LogReg_imdb_Ernie.pkl', 'rb') as file: | |
| cls = pickle.load(file) | |
| result = id2label[cls.predict(output)] | |
| return result | |
| st.title('Text sentiment analysis by ErnieModel') | |
| max_len = st.slider('Maximum word length', 0, 500, 250) | |
| text_input = st.text_input("Enter some text about movie") | |
| model, tokenizer = load_model() | |
| if text_input: | |
| input_tokens = preprocess_text(text_input, max_len, tokenizer) | |
| output = predict_sentiment(model, input_tokens) | |
| st.write(output) | |