AlcLaM

AlcLaM is an Arabic dialectal pretrained-language model introduced in the paper.

Huggingface

alclam-base-v1 and alclam-base-v2 are publically available in huggingface:https://huggingface.co/rahbi/

Pipeline

A pipeline for DID is also available on huggingface:

from transformers import pipeline

classifier = pipeline("text-classification", model="rahbi/alclam-base-v1-DID")
result = classifier("ما ذلحين والله ما عاد اصدقك")
print(result)

Output:

[{'label': 'يمني', 'score': 0.999321460723877}]

Comparision

To use our model, simply run fine_tuning.py. To achieve the results presented in the paper, use the folder Ft. Below is a performance comparison of AlcLaM with other popular pretrained-language models on various datasets.

F1 Comparison of Various PLMs on Different Datasets

	Task	mBERT	LaBSE	AraBERT	ArBERT	MdBERT	CAMeL	MARBERT	AlcLaM
DID	MADAR-2	72.9 ± 16.9	86.6 ± 0.5	87.1 ± 0.2	87.1 ± 0.2	86.0 ± 0.6	87.5 ± 1.0	85.3 ± 3.8	98.2 ± 0.1
	MADAR-6	91.3 ± 0.1	91.1 ± 0.2	91.6 ± 0.1	91.6 ± 0.2	91.6 ± 0.0	92.0 ± 0.1	92.2 ± 0.2	93.2 ± 0.1$^*$
	MADAR-9	75.5 ± 0.5	75.7 ± 0.2	76.8 ± 0.3	74.5 ± 4.3	75.9 ± 0.5	77.5 ± 0.4	78.2 ± 0.3	81.9 ± 0.3
	MADAR-26	60.5 ± 0.2	62.0 ± 0.2	62.0 ± 0.1	61.7 ± 0.1	60.2 ± 0.4	62.9 ± 0.1	61.5 ± 0.4	66.3 ± 0.1$^*$
	NADI	17.6 ± 0.5	17.6 ± 0.5	22.6 ± 0.5	22.6 ± 0.5	24.9 ± 0.6	25.9 ± 0.5	28.6 ± 0.8$^*$	25.6 ± 0.6
SA	SemEval	51.3 ± 1.3	64.2 ± 0.7	65.4 ± 0.5	64.4 ± 0.9	65.6 ± 0.3	67.1 ± 0.7	66.4 ± 0.3	69.2 ± 0.4$^*$
	ASAD	59.8 ± 0.0	62.4 ± 0.0	41.3 ± 0.0	66.9 ± 0.0	67.5 ± 0.0	65.8 ± 0.0	66.8 ± 0.0	66.7 ± 0.0
	AJGT	86.4 ± 0.3	92.4 ± 0.7	92.7 ± 0.3	92.6 ± 0.4	93.6 ± 0.0	93.6 ± 0.3	93.7 ± 0.1	95.0 ± 0.3$^*$
	ASTD	46.3 ± 1.4	55.7 ± 0.4	57.5 ± 2.3	59.7 ± 0.1	61.9 ± 0.4	60.2 ± 0.2	61.0 ± 0.5	64.6 ± 0.1$^*$
	LABR	81.1 ± 0.0	85.4 ± 0.0	85.9 ± 0.0	85.9 ± 0.0	84.7 ± 0.0	86.3 ± 0.0	85.0 ± 0.0	84.9 ± 0.0
	ARSAS	73.2 ± 0.7	76.2 ± 0.6	76.8 ± 0.3	76.1 ± 0.2	76.3 ± 0.2	77.1 ± 0.3	76.2 ± 0.2	77.9 ± 0.3$^*$
HSOD	HateSpeech	67.9 ± 1.4	73.7 ± 1.1	76.4 ± 1.2	76.8 ± 1.4	80.0 ± 0.1	78.8 ± 0.6	80.0 ± 0.8	81.4 ± 0.5$^*$
	Offense	85.3 ± 0.5	87.2 ± 0.5	90.5 ± 0.4	90.5 ± 0.4	90.8 ± 0.2	89.2 ± 0.5	90.8 ± 0.3	91.3 ± 0.3$^*$
	Adult	87.9 ± 0.1	87.2 ± 0.3	88.6 ± 0.1	88.4 ± 0.6	88.1 ± 0.0	88.6 ± 0.3	88.3 ± 0.1	89.3 ± 0.3$^*$

Accuracy Comparison of Various PLMs on Different Datasets

	Task	mBERT	LaBSE	AraBERT	ArBERT	MdBERT	CAMeL	MARBERT	AlcLaM
DID	MADAR-2	97.3 ± 0.8	98.0 ± 0.1	98.1 ± 0.0	98.1 ± 0.0	98.0 ± 0.1	98.1 ± 0.1	97.2 ± 0.7	99.7 ± 0.0
	MADAR-6	91.3 ± 0.1	91.1 ± 0.2	91.6 ± 0.1	91.6 ± 0.2	91.6 ± 0.0	92.0 ± 0.1	92.2 ± 0.2	93.2 ± 0.1 *
	MADAR-9	78.5 ± 0.5	79.1 ± 0.1	80.4 ± 0.2	77.7 ± 3.6	79.1 ± 0.5	80.5 ± 0.2	81.1 ± 0.3	83.4 ± 0.4
	MADAR-26	60.6 ± 0.2	61.9 ± 0.2	61.9 ± 0.1	61.7 ± 0.2	60.1 ± 0.3	62.9 ± 0.2	61.3 ± 0.3	66.1 ± 0.2 *
	NADI	33.4 ± 0.6	33.4 ± 0.6	38.9 ± 1.7	38.9 ± 1.7	41.9 ± 1.9	42.7 ± 1.6	47.3 ± 0.1 *	46.6 ± 1.0
SA	SemEval	53.4 ± 1.5	65.0 ± 0.6	66.1 ± 0.5	65.1 ± 0.8	66.1 ± 0.3	68.0 ± 0.3	66.9 ± 0.3	69.5 ± 0.3 *
	ASAD	74.6 ± 0.0	75.2 ± 0.0	70.6 ± 0.0	78.4 ± 0.0	77.6 ± 0.0	77.0 ± 0.0	77.6 ± 0.0	79.5 ± 0.0
	AJGT	86.4 ± 0.3	92.4 ± 0.7	92.8 ± 0.3	92.6 ± 0.4	93.6 ± 0.0	93.6 ± 0.3	93.8 ± 0.1	95.0 ± 0.3 *
	ASTD	46.7 ± 1.7	55.6 ± 0.6	57.7 ± 2.4	59.7 ± 0.3	62.0 ± 0.3	60.1 ± 0.2	61.0 ± 0.3	64.9 ± 0.1 *
	LABR	90.4 ± 0.0	92.3 ± 0.0	92.8 ± 0.0	92.8 ± 0.0	91.9 ± 0.0	93.0 ± 0.0	92.6 ± 0.0	92.6 ± 0.0
	ARSAS	74.5 ± 0.8	77.2 ± 0.7	77.6 ± 0.3	77.0 ± 0.3	77.5 ± 0.3	78.0 ± 0.3	77.4 ± 0.4	78.6 ± 0.5 *
HSOD	HateSpeech	75.2 ± 2.2	80.0 ± 0.7	80.5 ± 1.4	80.8 ± 1.9	84.3 ± 0.3	83.3 ± 0.6	84.4 ± 0.4	84.6 ± 0.7 *
	Offense	91.7 ± 0.1	92.8 ± 0.4	94.5 ± 0.2	94.6 ± 0.4	94.6 ± 0.2	93.6 ± 0.2	94.8 ± 0.0	94.9 ± 0.1 *
	Adult	95.0 ± 0.0	94.4 ± 0.2	95.2 ± 0.1	94.9 ± 0.4	95.1 ± 0.1	95.2 ± 0.2	95.1 ± 0.0	95.6 ± 0.0

If you use the code, please cite the paper:

@article{murtadha2024alclam,
	author       = {Murtadha, Ahmed and Saghir, Alfasly and Wen, Bo and Jamaal, Qasem and  Mohammed, Ahmed and Liu, Yunfeng},
	title        = {AlcLaM: Arabic Dialectal Language Model},
	journal      = {Arabic NLP 2024},
	year         = {2024},
	url          = {https://arxiv.org/abs/2407.13097},
	eprinttype    = {arXiv},
	eprint       = {2407.13097}
}