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AmiruMallawarachchi/
mindlens-rag-reranker

Name: mindlens-rag-reranker
Availability: InStock

nbertagnolli/counsel-chat

apache-2.0

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MindLens RAG Reranker (Cross-Encoder)

Model Description

Cross-encoder fine-tuned from cross-encoder/ms-marco-MiniLM-L-6-v2 (pre-trained on MS MARCO passage ranking) on mental-health domain query-passage pairs to re-rank ChromaDB retrieval results before passage injection into the Groq therapy-response generator. This is Model 5 in the MindLens five-model NLP pipeline.

How It Works in MindLens

User message arrives at the orchestrator
ChromaDB retrieves top-20 candidate passages by cosine similarity
This reranker scores each (user message, passage) pair jointly
Top-3 ranked passages are injected into Groq Llama 3.3 70B's system prompt
Groq generates the therapy response, grounded in the most relevant counselling text

Training Data

Built from nbertagnolli/counsel-chat using a three-tier pair construction strategy:

Tier 1 — Automatic positives: (question, its own counsellor answer) → label 1
Tier 2 — Hard negatives: (question, answer from a different topic) → label 0
Tier 3 — Groq-scored: (question, same-topic different-answer) scored by Groq Llama-3.1-8B as LLM-as-judge (Zheng et al., 2023) → binary label

Training pairs: 3,832 | Val: 479 | Test: 480 Groq-scored ambiguous pairs: 0

Evaluation Results (held-out test set)

Metric	Score	Interpretation
NDCG@3	1.0000	Ranking quality of top-3 results
MRR	1.0000	How quickly the first relevant passage is found
Precision@3	0.5833	Fraction of top-3 results that are relevant

Usage

python

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("AmiruMallawarachchi/mindlens-rag-reranker")

query = "I feel completely hopeless and can't stop crying"
passages = [...]  # top-20 from ChromaDB

scores = reranker.predict([[query, p] for p in passages])
top3_indices = scores.argsort()[::-1][:3]
top3_passages = [passages[i] for i in top3_indices]

Citation

Zheng, L. et al. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. NeurIPS 2023. Nogueira, R. & Cho, K. (2019). Passage Re-ranking with BERT.