Text Classification
Transformers
Safetensors
Arabic
quality_classifier
feature-extraction
quality-classifier
data-filtering
pretraining
custom_code
Instructions to use AdaMLLab/mmBERT-Arabic-Quality-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaMLLab/mmBERT-Arabic-Quality-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AdaMLLab/mmBERT-Arabic-Quality-Classifier", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AdaMLLab/mmBERT-Arabic-Quality-Classifier", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,399 Bytes
31ce063 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ---
language:
- ar
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: jhu-clsp/mmBERT-small
tags:
- quality-classifier
- data-filtering
- pretraining
---
<p align="center">
<a href="https://huggingface.co/collections/AdaMLLab/mixminmatch">
<img src="https://img.shields.io/badge/🤗_Collection-MixMinMatch-blue" alt="MixMinMatch Collection">
</a>
</p>
# mmBERT Arabic Quality Classifier
A text quality classifier for Arabic pretraining data, trained from [mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small). Used to create [AraMix-HQ](https://huggingface.co/datasets/AdaMLLab/AraMix-HQ).
This model implements the FineWeb2-HQ approach ([Messmer et al., 2025](https://arxiv.org/abs/2502.10361)) but uses mmBERT as the encoder for improved Arabic understanding.
## Usage
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="AdaMLLab/mmBERT-Arabic-Quality-Classifier")
result = classifier("النص العربي هنا")
```
## Citation
```bib
@misc{alrashed2025mixminmatch,
title={Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets},
author={Sultan Alrashed and Francesco Orabona},
year={2025},
eprint={2512.18834v2},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.18834v2},
}
``` |