Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
fact-checking
Languages:
English
Size:
10K - 100K
License:
File size: 3,766 Bytes
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language:
- en
license: mit
task_categories:
- text-classification
task_ids:
- fact-checking
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{salih2026sentinel,
title={Project Sentinel: Lightweight Semantic Filtering for Edge RAG},
author={Salih, El Mehdi and Ait El Mouden, Khaoula and Akchouch, Abdelhakim},
year={2026}
}
```
---
## Contact
**El Mehdi Salih**
Mohammed V University – Rabat
Email: elmehdi_salih@um5.ac.ma
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