|
--- |
|
language: |
|
- ar |
|
license: apache-2.0 |
|
widget: |
|
- text: "عامل ايه ؟" |
|
--- |
|
# CAMeLBERT-Mix DID NADI Model |
|
## Model description |
|
**CAMeLBERT-Mix DID NADI Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model. |
|
For the fine-tuning, we used the [NADI Coountry-level](https://sites.google.com/view/nadi-shared-task) dataset, which includes 21 labels. |
|
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* Our fine-tuning code can be found [here](https://github.com/CAMeL-Lab/CAMeLBERT). |
|
|
|
## Intended uses |
|
You can use the CAMeLBERT-Mix DID NADI model as part of the transformers pipeline. |
|
This model will also be available in [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools) soon. |
|
|
|
#### How to use |
|
To use the model with a transformers pipeline: |
|
```python |
|
>>> from transformers import pipeline |
|
>>> did = pipeline('text-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi') |
|
>>> sentences = ['عامل ايه ؟', 'شلونك ؟ شخبارك ؟'] |
|
>>> did(sentences) |
|
[{'label': 'Egypt', 'score': 0.920274019241333}, |
|
{'label': 'Saudi_Arabia', 'score': 0.26750022172927856}] |
|
``` |
|
*Note*: to download our models, you would need `transformers>=3.5.0`. |
|
Otherwise, you could download the models manually. |
|
|
|
## Citation |
|
```bibtex |
|
@inproceedings{inoue-etal-2021-interplay, |
|
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", |
|
author = "Inoue, Go and |
|
Alhafni, Bashar and |
|
Baimukan, Nurpeiis and |
|
Bouamor, Houda and |
|
Habash, Nizar", |
|
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", |
|
month = apr, |
|
year = "2021", |
|
address = "Kyiv, Ukraine (Online)", |
|
publisher = "Association for Computational Linguistics", |
|
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", |
|
} |
|
``` |
