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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(page_title="Web Clusters inspection", layout="wide") |
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st.title("Web clusters inspection") |
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st.markdown(""" |
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We clustered 100k web samples using [text-clustering](https://github.com/huggingface/text-clustering). |
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Our approach involved prompting Mixtral to evaluate whether the topics in each cluster are educational or could be considered college material using a score from 1 to 10. \ |
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Technically, we provide it with 10 random examples from the cluster in the prompt and ask it to judge their topics. |
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Additionally, the model was tasked with finding the topic of each cluster (based on the 10 random examples). |
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""") |
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@st.cache_data |
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def load_data(min_score=1, max_score=10, show_special=False): |
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ds = load_dataset("HuggingFaceTB/FW_clusters_100k_145_topics", split="train", token=HF_TOKEN, num_proc=2) |
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def filter_func(x): |
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try: |
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score = int(x['educational_score']) |
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value = False if show_special else min_score <= score <= max_score |
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return value |
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except (ValueError, TypeError): |
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return show_special |
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ds = ds.filter(filter_func) |
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return ds |
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st.subheader("Cluster information") |
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col_1, col_2, col_3 = st.columns(3) |
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with col_1: |
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show_special = st.checkbox('Show only clusters with undefined educational score', False) |
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with col_2: |
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min_value = st.slider('Select minimum educational score', 1, 10, 1, key='min_score') |
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with col_3: |
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max_value = st.slider('Select maximum educational score', 1, 10, 10, key='max_score') |
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ds = load_data(min_value, max_value, show_special) |
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categories = list(set(ds["category"])) |
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selected_category = st.selectbox("Select a topic", categories) |
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selected_cluster = ds.filter(lambda x: x['category'] == selected_category) |
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n_samples = len(selected_cluster) |
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if n_samples > 0: |
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col_1, col_2 = st.columns(2) |
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with col_1: |
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index_cluster = st.number_input(f"Found {len(selected_cluster)} clusters, choose one", min_value=0, max_value=len(selected_cluster)-1, value=0, step=1) |
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files = selected_cluster[index_cluster]["examples"] |
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with col_2: |
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index_example = st.number_input(f"Found {len(files)} files in the cluster, choose one", min_value=0, max_value=len(files)-1, value=0, step=1) |
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sample = files[index_example] |
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st.markdown(f"**Educational score of the cluster**: {selected_cluster[index_cluster]['educational_score']}") |
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st.markdown(sample) |
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else: |
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st.markdown("No files found, change the cluster.") |
