| """Find potential duplicates in the data using cosine similarity.""" |
| import streamlit as st |
| from sentence_transformers.util import cos_sim |
|
|
| from src.subpages.page import Context, Page |
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|
| @st.cache() |
| def get_sims(texts: list[str], sentence_encoder): |
| embeddings = sentence_encoder.encode(texts, batch_size=8, convert_to_numpy=True) |
| return cos_sim(embeddings, embeddings) |
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|
| class FindDuplicatesPage(Page): |
| name = "Find Duplicates" |
| icon = "fingerprint" |
|
|
| def _get_widget_defaults(self): |
| return { |
| "cutoff": 0.95, |
| } |
|
|
| def render(self, context: Context): |
| st.title("Find Duplicates") |
| with st.expander("💡", expanded=True): |
| st.write("Find potential duplicates in the data using cosine similarity.") |
|
|
| cutoff = st.slider("Similarity threshold", min_value=0.0, max_value=1.0, key="cutoff") |
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| texts = [" ".join(ts) for ts in context.split["tokens"]] |
| sims = get_sims(texts, context.sentence_encoder) |
|
|
| candidates = [] |
| for i in range(len(sims)): |
| for j in range(i + 1, len(sims)): |
| if sims[i][j] >= cutoff: |
| candidates.append((sims[i][j], i, j)) |
| candidates.sort(reverse=False) |
|
|
| for (sim, i, j) in candidates[:100]: |
| st.markdown(f"**Possible duplicate ({i}, {j}, sim: {sim:.3f}):**") |
| st.markdown("* " + " ".join(context.split["tokens"][i])) |
| st.markdown("* " + " ".join(context.split["tokens"][j])) |
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