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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)
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