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Update app.py
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app.py
CHANGED
@@ -62,7 +62,7 @@ st.sidebar.subheader("Disclaimer/Limitations")
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st.sidebar.write("The model only outputs sadness, joy, love, anger, fear, and surprise. With that said, it does not completely encompass the emotions that a human being feels, and the application only suggests a playlist based on the aforementioned emotions.")
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st.sidebar.subheader("Model Description")
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st.sidebar.write("This application uses the DistilBERT model, a distilled version of BERT. The BERT framework uses a bidirectional transformer that allows it to learn the context of a word based on the left and right of the word. According to a paper by V. Sanh, et al., DistilBERT can \"reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities, and being 60% faster.\" This is why the DistilBERT model was used. For more information about the paper, please check out this [link](https://
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st.sidebar.write("The specific DistilBERT model used for this is Bhadresh Savani's [distilbert-base-uncased-emotion] (https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion). It is fine-tuned on the Emotion Dataset from Twitter, which can be found [here](https://huggingface.co/datasets/viewer/?dataset=emotion).")
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st.sidebar.subheader("Performance Benchmarks")
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st.sidebar.write("The model only outputs sadness, joy, love, anger, fear, and surprise. With that said, it does not completely encompass the emotions that a human being feels, and the application only suggests a playlist based on the aforementioned emotions.")
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st.sidebar.subheader("Model Description")
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st.sidebar.write("This application uses the DistilBERT model, a distilled version of BERT. The BERT framework uses a bidirectional transformer that allows it to learn the context of a word based on the left and right of the word. According to a paper by V. Sanh, et al., DistilBERT can \"reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities, and being 60% faster.\" This is why the DistilBERT model was used. For more information about the paper, please check out this [link](https://arxiv.org/abs/1910.01108).")
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st.sidebar.write("The specific DistilBERT model used for this is Bhadresh Savani's [distilbert-base-uncased-emotion] (https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion). It is fine-tuned on the Emotion Dataset from Twitter, which can be found [here](https://huggingface.co/datasets/viewer/?dataset=emotion).")
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st.sidebar.subheader("Performance Benchmarks")
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