Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
fact-checking
Languages:
English
Size:
1K - 10K
License:
File size: 1,777 Bytes
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language:
- en
pretty_name: SciFact Chat-Format
license: cc-by-nc-2.0
task_categories:
- text-classification
task_ids:
- fact-checking
source_datasets:
- allenai/scifact
tags:
- text
- science
- claim-verification
- chat-format
- instruction-tuning
- datasets
- allenai/scifact
---
# SciFact (Chat-Format Preparation)
This dataset is a chat-format preparation of SciFact for supervised fine-tuning (SFT).
## Format
This format is commonly referred to as:
- chat-format SFT data
- instruction-tuning conversations
- OpenAI-style `messages` format
## Included files
- `train.jsonl`
- `validation.jsonl`
- `stats.json`
- `prepare_scifact_unsloth.py`
## Source
- Base dataset: `allenai/scifact`
## Original Dataset Highlights
- Original dataset: `allenai/scifact`
- Focus: scientific claim verification with rationales.
- Labels: `SUPPORT`, `CONTRADICT`, and `NOT ENOUGH INFO` style supervision.
- Reported scale on source card: ~1.4K claims with evidence-containing abstracts.
- Paper: [Fact or Fiction: Verifying Scientific Claims](https://aclanthology.org/2020.emnlp-main.609/)
## Preparation summary
- Claim verification labels:
- `SUPPORTS`
- `CONTRADICTS`
- `NOT ENOUGH INFORMATION`
- Abstract sentences are indexed (`[0]`, `[1]`, ...).
- One row is emitted per claim-evidence set.
- NEI rows are created when no support/contradict evidence is present.
Assistant response format:
- `Label: ...`
- `Rationale sentence ids: [...]`
- `Explanation: ...`
## Schema
Each JSONL row contains:
- `messages`
- `user`: instruction + claim + title + indexed abstract
- `assistant`: structured label/rationale/explanation
- `meta`: claim/doc ids, label, rationale sentence ids, split, variant
## Reproduction
```bash
python prepare_scifact_unsloth.py
```
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