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| # Import statements | |
| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| # Get Input | |
| models = {"Distilbert": "distilbert-base-uncased-finetuned-sst-2-english", "roBERTa" : "cardiffnlp/twitter-roberta-base-sentiment", "Bert" : "finiteautomata/bertweet-base-sentiment-analysis"} | |
| text = st.text_area('Text to analyze', "On the review aggregation website Rotten Tomatoes, The Super Mario Bros Movie has a critics score of just 54 per cent positive, marking it as Rotten.") | |
| model_name = st.selectbox("What model do you wanna use?", ("Distilbert", "roBERTa", "Bert")) | |
| # Default Model - DistilBert | |
| if text and st.button('Submit!'): | |
| model = AutoModelForSequenceClassification.from_pretrained(models[model_name]) | |
| tokenizer = AutoTokenizer.from_pretrained(models[model_name]) | |
| classifier = pipeline('sentiment-analysis', model = model, tokenizer = tokenizer) | |
| out = classifier(text) | |
| st.json(out) | |