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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!") |