Datasets:
File size: 1,664 Bytes
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license: mit
task_categories:
- text-generation
language:
- en
tags:
- xai
- attribution
- decoder
- short-context
size_categories:
- 1K<n<10K
---
# Orca-Mini - Short Context (<100 tokens)
This dataset is a filtered version of [ermiaazarkhalili/orca-mini-v1-high-prob-qwen-0.5b-10k](https://huggingface.co/datasets/ermiaazarkhalili/orca-mini-v1-high-prob-qwen-0.5b-10k)
containing only samples with **fewer than 100 tokens**.
## Dataset Description
This dataset is designed for efficient XAI (Explainable AI) attribution evaluation on decoder models.
Short context samples allow for faster evaluation while maintaining meaningful attribution analysis.
### Statistics
- **Total samples**: 5,000
- **Token count range**: 5 - 99
- **Mean token count**: 23.1 (std: 11.8)
- **Median token count**: 21.0
### Columns
| Column | Description |
|--------|-------------|
| `sentence_prefix` | Text prefix before predicted token |
| `predicted_token` | Model's top-1 prediction |
| `actual_token` | Ground truth token |
| `probability` | Prediction confidence (0-1) |
### Source Dataset
This dataset was created by filtering [ermiaazarkhalili/orca-mini-v1-high-prob-qwen-0.5b-10k](https://huggingface.co/datasets/ermiaazarkhalili/orca-mini-v1-high-prob-qwen-0.5b-10k)
with the following criteria:
- Token length: 5 <= tokens < 100
- Tokenizer: `Qwen/Qwen2.5-0.5B`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("ermiaazarkhalili/orca-mini-short-100tok", split="train")
print(f"Loaded {len(dataset)} samples")
```
### Citation
If you use this dataset, please cite the ALTI-XAI framework.
### License
MIT License
|