| import streamlit as st | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| def analyze(model_name, text): | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
| return classifier(text) | |
| st.title("Sentiment Analysis App - beta") | |
| st.write("This app is to analyze the sentiments behind a text. \n Currently it uses \ | |
| pre-trained models without fine-tuning.") | |
| model_descrip = { | |
| "distilbert-base-uncased-finetuned-sst-2-english": "This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.\n \ | |
| Labels: POSITIVE; NEGATIVE ", | |
| "cardiffnlp/twitter-roberta-base-sentiment": "This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark.\n \ | |
| Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive", | |
| "finiteautomata/bertweet-base-sentiment-analysis": "Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets. \n \ | |
| Labels: POS; NEU; NEG" | |
| } | |
| user_input = st.text_input("Enter your text:", value="Missing Sophie.Z...") | |
| user_model = st.selectbox("Please select a model:", | |
| model_descrip) | |
| st.write("### Model Description:") | |
| st.write(model_descrip[user_model]) | |
| if st.button("Analyze"): | |
| if not user_input: | |
| st.write("Please enter a text.") | |
| else: | |
| with st.spinner("Hang on.... Analyzing..."): | |
| result = analyze(user_model, user_input) | |
| st.write(f"Result: \nLabel: {result[0]['label']} Score: {result[0]['score']}") | |
| else: | |
| st.write("Go on! Try the app!") |