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UNIST-Eunchan
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Parent(s):
a9ea355
Update app.py
Browse files
app.py
CHANGED
@@ -83,21 +83,24 @@ def chunking(book_text):
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'''
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'''
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#prompts
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st.title("Book Summarization π")
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st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
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book_index = st.sidebar.slider("Select Book Example", value = 0,min_value = 0, max_value=4)
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_book = test_book[book_index]['book']
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chunked_segments = chunking(_book)
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sent = st.text_area("Text", _book[:512], height = 550)
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max_length = st.sidebar.slider("Max Length", value = 512,min_value = 10, max_value=1024)
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temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
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top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
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top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.92)
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def generate_output(test_samples):
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inputs = tokenizer(
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test_samples,
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'''
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'''
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_book = test_book[book_index]['book']
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#prompts
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st.title("Book Summarization π")
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st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
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book_index = st.sidebar.slider("Select Book Example", value = 0,min_value = 0, max_value=4)
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sent = st.text_area("Text", _book[:512], height = 550)
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max_length = st.sidebar.slider("Max Length", value = 512,min_value = 10, max_value=1024)
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temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
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top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
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top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.92)
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chunked_segments = chunking(_book)
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def generate_output(test_samples):
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inputs = tokenizer(
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test_samples,
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