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import streamlit as st |
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from datasets import load_dataset |
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import pandas as pd |
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def test_v01(): |
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default_value = "See how a modern neural network auto-completes your text 🤗 This site, built by the Hugging Face team, lets you write a whole document directly from your browser, and you can trigger the Transformer anywhere using the Tab key. Its like having a smart machine that completes your thoughts 😀 Get started by typing a custom snippet, check out the repository, or try one of the examples. Have fun!" |
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sent = st.text_area("Text", default_value, height = 275) |
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max_length = st.sidebar.slider("Max Length", min_value = 10, max_value=30) |
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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 = 0.9) |
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num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1) |
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return num_return_sequences |
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def test_v02(): |
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dataset = load_dataset("merve/poetry", streaming=True) |
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print(dataset) |
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df = pd.DataFrame.from_dict(dataset["train"]) |
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st.write("Most appearing words including stopwords") |
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return st |
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test_v02() |
