---
title: gliclass-v3-logic-dataset
canonical_url: "https://www.modelscope.cn/datasets/knowledgator/gliclass-v3-logic-dataset"
md_url: "https://www.modelscope.cn/datasets/knowledgator/gliclass-v3-logic-dataset.md"
repository: knowledgator/gliclass-v3-logic-dataset
last_updated: 2025-08-12
license: apache-2.0
storage_size: "4.5 MB"
downloads: 297
stars: 0
---

# gliclass-v3-logic-dataset

> gliclass-v3-logic-dataset - knowledgator 在 ModelScope 开源的数据集。GLiClass‑V3 Logic Dataset

knowledgator/gliclass-v3-logic-dataset 是 ModelScope 魔搭社区上的数据集，存储大小 4.5 MB，采用 apache-2.0 许可。

- **Repository**: knowledgator/gliclass-v3-logic-dataset
- **License**: apache-2.0
- **Storage size**: 4.5 MB
- **Downloads**: 297
- **Stars**: 0
- **Last updated**: 2025-08-12

Source: https://www.modelscope.cn/datasets/knowledgator/gliclass-v3-logic-dataset

---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6405f62ba577649430be5124/I9RAQol7giilBHbbf2T7M.png)

# GLiClass‑V3 Logic Dataset

**Rows**  7 776 | **Split**  train only | **Format**  Parquet | **Language**  EN | **License**  Apache‑2.0

## What it is
A length‑balanced corpus of single‑sentence prompts built purely for inducing reasoning in language models.

## Why it helps
* Teaches symbolic‑logic patterns and multi‑label behaviour.  
* Buckets cover 15 word‑length ranges (4 → 1,024) in equal proportions, exposing models to both tiny and very long inputs.  
* Each example has **1‑50 true** and **1‑50 false** labels, forcing the model to cope with large, variable answer sets.

## Where the prompts come from
Re‑annotated snippets drawn from three public resources:

| Source dataset | Notes |
|----------------|-------|
| **FineWeb** (clean web crawl) | Plain sentences automatically filtered for quality, then labelled with LLM. |
| **tau/CommonsenseQA** | Question stems only; each converted to a declarative premise and re‑labelled multi‑label style. |
| **GLiClass‑2k prototype** (`BioMike/formal‑logic‑reasoning‑gliclass‑2k`) | Earlier formal‑logic items. |
| **nyu‑mll/MultiNLI** | Premise/hypothesis pairs. |


## Data schema
| Column        | Type            | Notes                                    |
|---------------|-----------------|------------------------------------------|
| `text`        | string          | Sentence or short passage.               |
| `true_labels` | list\<string\>  | All correct answers.                     |
| `all_labels`  | list\<string\>  | `true_labels` + distractors (shuffled).   |

## Quick load
```python
from datasets import load_dataset
ds = load_dataset("knowledgator/gliclass-v3-logic-dataset")["train"]
```

## Citation
```bibtex
@misc{stepanov2025gliclassgeneralistlightweightmodel,
      title={GLiClass: Generalist Lightweight Model for Sequence Classification Tasks}, 
      author={Ihor Stepanov and Mykhailo Shtopko and Dmytro Vodianytskyi and Oleksandr Lukashov and Alexander Yavorskyi and Mykyta Yaroshenko},
      year={2025},
      eprint={2508.07662},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.07662}, 
}
```