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import streamlit as st |
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from datasets import load_dataset |
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import os |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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st.set_page_config( |
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page_title="Logprobs inspection", layout="wide" |
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) |
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st.markdown("# Logprobs inspection") |
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@st.cache_data |
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def load_data(): |
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ds = load_dataset( |
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"HuggingFaceTB/sample_log_probs", |
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split="train", |
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token=HF_TOKEN, |
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) |
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return ds |
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ds = load_data() |
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min_log = min(ds["logprobs"]) |
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max_log = max(ds["logprobs"]) |
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col_1, col_2 = st.columns(2) |
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with col_1: |
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min_score = st.slider("Select minimum logprob", min_value=min_log, max_value=max_log, value=min_log, step=0.2, key="min_score") |
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with col_2: |
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max_score = st.slider("Select maximum logprob", min_value=min_log, max_value=max_log, value=max_log, step=0.2, key="max_score") |
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filtered_ds = ds.filter(lambda x: min_score <= x["logprobs"] <= max_score) |
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index = st.slider("Select a sample", 0, len(filtered_ds), 0) |
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with st.expander("The prompt"): |
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st.markdown(filtered_ds[index]['prompt']) |
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st.markdown(f"**Metadata:** log_prob is {filtered_ds[index]['logprobs']:.2f}, seed: {filtered_ds[index]['seed_data']}, {filtered_ds[index]['format']} for {filtered_ds[index]['audience']}.") |
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st.markdown(filtered_ds[index]["text"]) |
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