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import streamlit as st |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification |
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def analyze(model_name: str, text: str) -> dict: |
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''' |
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Output result of sentiment analysis of a text through a defined model |
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''' |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
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return classifier(text) |
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st.title("Sentiment Analysis App - Milestone2") |
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st.write("This app is to analyze the sentiments behind a text.") |
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st.write("Currently it uses pre-trained models without fine-tuning.") |
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model_descrip = { |
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"distilbert-base-uncased-finetuned-sst-2-english": "This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. \ |
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Labels: POSITIVE; NEGATIVE ", |
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"cardiffnlp/twitter-roberta-base-sentiment": "This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. \ |
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Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive", |
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"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. \ |
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Labels: POS; NEU; NEG" |
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} |
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user_input = st.text_input("Enter your text:", value="NYU is the better than Columbia.") |
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user_model = st.selectbox("Please select a model:", model_descrip) |
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st.write("### Model Description:") |
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st.write(model_descrip[user_model]) |
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if st.button("Analyze"): |
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if not user_input: |
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st.write("Please enter a text.") |
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else: |
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with st.spinner("Hang on.... Analyzing..."): |
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result = analyze(user_model, user_input) |
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st.write("Result:") |
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st.write(f"Label: **{result[0]['label']}**") |
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st.write(f"Confidence Score: **{result[0]['score']}**") |
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else: |
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st.write("Go on! Try the app!") |