Create README.md

README.md

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+---
+language:
+- en
+license: mit
+task_categories:
+- text-classification
+task_ids:
+- binary-classification
+pretty_name: Project Sentinel – Semantic Filtering Dataset
+---
+
+# Project Sentinel – Semantic Filtering Dataset
+
+## Dataset Summary
+
+This dataset is designed to train and evaluate **lightweight semantic filtering models** for **Retrieval-Augmented Generation (RAG)** systems deployed in **resource-constrained / edge environments**.
+
+It supports **binary classification** of `(query, candidate_sentence)` pairs into:
+
+- **1 – Supporting Fact**: Factually useful evidence required to answer the query  
+- **0 – Distractor**: Topically similar but factually insufficient or misleading text
+
+The dataset was created to address the *hallucination-by-distraction* failure mode commonly observed in local RAG pipelines.
+
+This dataset was used to fine-tune a **4-layer TinyBERT cross-encoder** for the *Project Sentinel* semantic gatekeeping module.
+
+---
+
+## Supported Tasks
+
+- Semantic filtering
+- Evidence verification
+- Hallucination reduction in RAG
+- Binary text classification
+
+---
+
+## Dataset Structure
+
+The dataset consists of **sentence-level query–evidence pairs** derived from the **HotpotQA (Distractor) dataset**.
+
+Each sample follows the structure:
+
+| Field | Type | Description |
+|-----|------|------------|
+| `query` | string | Natural language question |
+| `sentence` | string | Candidate sentence extracted from context |
+| `label` | int | 1 = supporting fact, 0 = distractor |
+
+---
+
+## Dataset Splits
+
+| Split | Samples |
+|------|--------|
+| Train | 69,101 |
+| Validation | 7,006 |
+
+The class distribution is intentionally **imbalanced**, reflecting real-world retrieval conditions where distractors vastly outnumber true evidence.
+
+---
+
+## Data Construction
+
+### Source Dataset
+
+- **HotpotQA – Distractor Setting**
+- Multi-hop QA dataset with explicit annotations for supporting facts and distractor paragraphs
+
+### Positive Samples
+
+- Extracted exclusively from annotated *supporting fact sentences*
+- Based on `(title, sentence index)` ground-truth supervision
+
+### Negative Samples
+
+- Non-annotated sentences from gold paragraphs *(hard negatives)*
+- Sentences from distractor paragraphs intentionally selected for topical similarity
+
+This strategy produces **adversarial negatives** closely matching real retrieval errors.
+
+---
+
+## Preprocessing Pipeline
+
+- Removal of null or malformed samples
+- Sentence-level deduplication
+- Text normalization and whitespace cleanup
+- Controlled negative sampling
+- Label integrity verification
+
+---
+
+## Input Representation
+
+The dataset is intended for **cross-encoder architectures**.
+
+Model input format:
+
+```
+[CLS] query [SEP] sentence [SEP]
+```
+
+- Maximum sequence length: 512 tokens
+- Designed for TinyBERT / MiniLM-style encoders
+
+---
+
+## Class Imbalance Handling
+
+Due to natural imbalance, training is expected to use:
+
+- **Weighted cross-entropy loss**
+- Recall-preserving thresholds
+
+This ensures evidence recall is prioritized over aggressive filtering.
+
+---
+
+## Intended Use
+
+✔ Training semantic gatekeepers for RAG  
+✔ Edge-deployed LLM pipelines  
+✔ Hallucination suppression  
+✔ Early-exit decision systems  
+
+---
+
+## Limitations
+
+- Derived from Wikipedia-based QA (HotpotQA)
+- English-only
+- Sentence-level relevance only (not passage-level)
+
+Performance may vary when applied to highly domain-specific corpora.
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+@article{salih2025sentinel,
+  title={Project Sentinel: Lightweight Semantic Filtering for Edge RAG},
+  author={Salih, El Mehdi and Ait El Mouden, Khaoula and Akchouch, Abdelhakim},
+  year={2025}
+}
+```
+
+---
+
+## Contact
+
+**El Mehdi Salih**  
+Mohammed V University – Rabat  
+Email: elmehdi.salih@um5.ac.ma