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| import streamlit as st #Web App | |
| from transformers import pipeline | |
| from pysentimiento import create_analyzer | |
| #title | |
| st.title("Sentiment Analysis - Classify Sentiment of text") | |
| model = st.selectbox("Which pretrained model would you like to use?",("roberta-large-mnli","twitter-XLM-roBERTa-base","bertweet-sentiment-analysis")) | |
| data = [] | |
| text = st.text_input("Enter text here:","Artificial Intelligence is useful") | |
| data.append(text) | |
| if model == "roberta-large-mnli": | |
| #1 | |
| if st.button("Run Sentiment Analysis of Text"): | |
| model_path = "roberta-large-mnli" | |
| sentiment_pipeline = pipeline(model=model_path) | |
| result = sentiment_pipeline(data) | |
| label = result[0]["label"] | |
| score = result[0]["score"] | |
| st.write("The classification of the given text is " + label + " with a score of " + str(score)) | |
| elif model == "twitter-XLM-roBERTa-base": | |
| #2 | |
| if st.button("Run Sentiment Analysis of Text"): | |
| model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment" | |
| sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path) | |
| result = sentiment_task(text) | |
| label = result[0]["label"].capitalize() | |
| score = result[0]["score"] | |
| st.write("The classification of the given text is " + label + " with a score of " + str(score)) | |
| elif model == "bertweet-sentiment-analysis": | |
| #3 | |
| if st.button("Run Sentiment Analysis of Text"): | |
| analyzer = create_analyzer(task="sentiment", lang="en") | |
| result = analyzer.predict(text) | |
| if result.output == "POS": | |
| label = "POSITIVE" | |
| elif result.output == "NEU": | |
| label = "NEUTRAL" | |
| else: | |
| label = "NEGATIVE" | |
| neg = result.probas["NEG"] | |
| pos = result.probas["POS"] | |
| neu = result.probas["NEU"] | |
| st.write("The classification of the given text is " + label + " with the scores broken down as: Positive - " + str(pos) + ", Neutral - " + str(neu) + ", Negative - " + str(neg)) | |