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nli

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Welcome to the Neural Language Interface (NLI) Explain project! This repository is dedicated to exploring and explaining the decision-making process of BERT models in the context of Natural Language Inference (NLI) tasks. We employ Feature Interaction methods to shed light on why BERT makes specific predictions in NLI.

  • Updated Oct 14, 2023
  • Jupyter Notebook
Attention-Classification

This repository is to understand Attention mechanism for the Classification task. The task used here for explanation is Recognizing Textual Entailment. It is a Natural Language Inference task.

  • Updated Jun 29, 2020

Fully offline, GPU-accelerated RAG system for confidential board documents. Hybrid retrieval (BM25 + BGE-M3 dense + RRF + cross-encoder reranking), Qwen3-14B on vLLM, NLI hallucination guard with governance-aware verb checks, page-level citations, AES-256 ephemeral sessions. Zero network egress.

  • Updated Jul 16, 2026
  • Python

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