Oliver Li commited on
Commit
15103fe
·
1 Parent(s): 393a4bc

added label explanation

Browse files
Files changed (1) hide show
  1. app.py +19 -8
app.py CHANGED
@@ -16,15 +16,26 @@ st.write("Enter a text and select a pre-trained model to get the sentiment analy
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  text = st.text_input("Enter your text:")
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  # Model selection
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- model_options = [
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- "distilbert-base-uncased-finetuned-sst-2-english",
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- "textattack/bert-base-uncased-SST-2",
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- "cardiffnlp/twitter-roberta-base-sentiment",
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- "nlptown/bert-base-multilingual-uncased-sentiment"
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- ]
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-
 
 
 
 
 
 
 
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  selected_model = st.selectbox("Choose a pre-trained model:", model_options)
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  # Load the model and perform sentiment analysis
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  if st.button("Analyze"):
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  if not text:
@@ -36,7 +47,7 @@ if st.button("Analyze"):
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  st.write(f"Sentiment: {result[0]['label']} (confidence: {result[0]['score']:.2f})")
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  if result[0]['label'] == 'POSITIVE':
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  st.balloons()
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- elif result[0]['label']== 'NEGATIVE' and result[0]['score']> 0.9:
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  st.error("Hater detected.")
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  else:
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  st.write("Enter a text and click 'Analyze' to perform sentiment analysis.")
 
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  text = st.text_input("Enter your text:")
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  # Model selection
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+ model_options = {
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+ "distilbert-base-uncased-finetuned-sst-2-english": {
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+ "labels": ["NEGATIVE", "POSITIVE"],
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+ "description": "This model classifies text into positive or negative sentiment. It is based on DistilBERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.",
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+ },
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+ "textattack/bert-base-uncased-SST-2": {
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+ "labels": ["LABEL_0", "LABEL_1"],
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+ "description": "This model classifies text into positive(LABEL_1) or negative(LABEL_0) sentiment. It is based on BERT and fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset.",
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+ },
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+ "cardiffnlp/twitter-roberta-base-sentiment": {
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+ "labels": ["LABEL_0", "LABEL_1", "LABEL_2"],
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+ "description": "This model classifies tweets into negative (LABEL_0), neutral(LABEL_1), or positive(LABEL_2) sentiment. It is based on RoBERTa and fine-tuned on a large dataset of tweets.",
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+ },
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+ }
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  selected_model = st.selectbox("Choose a pre-trained model:", model_options)
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+ st.write("### Model Information")
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+ st.write(f"**Labels:** {', '.join(model_options[selected_model]['labels'])}")
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+ st.write(f"**Description:** {model_options[selected_model]['description']}")
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+
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  # Load the model and perform sentiment analysis
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  if st.button("Analyze"):
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  if not text:
 
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  st.write(f"Sentiment: {result[0]['label']} (confidence: {result[0]['score']:.2f})")
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  if result[0]['label'] == 'POSITIVE':
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  st.balloons()
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+ elif result[0]['label'] in ['NEGATIVE', 'LABEL_0'] and result[0]['score']> 0.9:
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  st.error("Hater detected.")
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  else:
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  st.write("Enter a text and click 'Analyze' to perform sentiment analysis.")