Project: Build a Semantic Duplicate Detector

Separate Candidate Retrieval from Duplicate Decisions

• Define candidate generation, pair scoring and accept-review-reject routes as separate stages.
• Score labelled sentence pairs and convert the scores into accept, review and reject routes.
• Evaluate duplicate families and hard negatives without leaking near-identical pairs across splits.
• Evaluate duplicate, related and unrelated pairs separately, then justify the review band around your decision threshold.

From Nearest Neighbours to a Reviewed Merge Route

Random pair splits leak near-identical documents. Group families of duplicates into one fold and preserve hard negatives that share vocabulary but describe different outcomes.

The labelled pairs expose false merges and missed duplicates directly. Review them before running approximate search across a large collection.

Review the Pairs the Threshold Cannot Settle

Thresholds come from the cost of missed duplicates and false merges. Offer a review band instead of pretending one cosine value is universal.

Calibrate Three Routes, Not One Magic Cutoff

A similarity score alone cannot authorise a merge. Verify family-level splits, decision costs and the manual review band.

For Project Build a Semantic Duplicate Detector, change one setting at a time while the data split, comparison baseline and metric remain fixed.

What to record before scaling Project Build a Semantic Duplicate Detector

Before a longer semantic duplicate detection NLP run, write the data source, split rule, dependency versions and acceptance criteria.

Keep a fixed Project Build a Semantic Duplicate Detector failure set and a short limitations note so later changes can be compared rather than guessed.

Build the Project Build a Semantic Duplicate Detector example

Score the supplied pairs and reproduce all three routes first. Add records only after assigning family IDs and hard negatives.

Keep the data and split fixed while you change the semantic duplicate detection NLP decision.

Install:

python -m pip install sentence-transformers scikit-learn

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model=SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
pairs=[('Cannot reset my password','Password reset does not work'),('Refund is late','Password reset does not work'),('Invoice PDF missing','Cannot download my invoice')]
flat=[text for pair in pairs for text in pair]
E=model.encode(flat,normalize_embeddings=True)
for i,pair in enumerate(pairs):
    score=float(cosine_similarity(E[2*i:2*i+1],E[2*i+1:2*i+2])[0,0])
    route='duplicate' if score>=0.82 else 'review' if score>=0.60 else 'different'
    print(round(score,3),route,pair)

Expected Duplicate, Review and Reject Routes

Three similarity scores and routes. The thresholds are teaching placeholders and must be selected from reviewed development pairs before use.

Confirm accept, review and reject outcomes against labelled pairs, especially cases that share vocabulary but differ in meaning.

If the Project Build a Semantic Duplicate Detector result differs, print intermediate values and confirm the documented dependency versions first.

Diagnose failures in Project Build a Semantic Duplicate Detector

Inspect family grouping, candidate recall, score calibration and route thresholds before retraining the encoder.

Check your Project Build a Semantic Duplicate Detector reasoning

Complete the duplicate-detection questions before opening the explanations. They check candidate recall, leakage control and cost-based routing.

Primary references for Project Build a Semantic Duplicate Detector

• Sentence Transformers documentation: official semantic similarity and search guidance.
• scikit-learn model evaluation: official metric definitions.
• Hugging Face model cards: document encoder scope and limits before calibrating duplicate routes.

Recalibrate the detector when the encoder, candidate index or merge policy changes. Archive labelled families, hard negatives and route costs.