muaalem-model-v3

obadx/muaalem-model-v3

muaalem-model-v3 is a machine learning model from obadx. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: mit
Model type: multi_level_ctc
Access: Public
Created: Aug 23, 2025
Updated: Sep 4, 2025
SHA: 820ae412

Base models

facebook/w2v-bert-2.0

Datasets

obadx/muaalem-annotated-v3

Model card

From the Hugging Face model README

muaalem-model-v3

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0118
Per Phonemes: 0.0043
Per Hams Or Jahr: 0.0020
Per Shidda Or Rakhawa: 0.0027
Per Tafkheem Or Taqeeq: 0.0031
Per Itbaq: 0.0013
Per Safeer: 0.0014
Per Qalqla: 0.0013
Per Tikraar: 0.0037
Per Tafashie: 0.0019
Per Istitala: 0.0012
Per Ghonna: 0.0017
Average Per: 0.0022

Model description

The model was presented in the paper Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.2
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Per Phonemes	Per Hams Or Jahr	Per Shidda Or Rakhawa	Per Tafkheem Or Taqeeq	Per Itbaq	Per Safeer	Per Qalqla	Per Tikraar	Per Tafashie	Per Istitala	Per Ghonna	Average Per
0.128	0.2002	650	0.0237	0.0075	0.0031	0.0043	0.0071	0.0020	0.0020	0.0019	0.0056	0.0037	0.0018	0.0024	0.0038
0.0172	0.4005	1300	0.0128	0.0038	0.0017	0.0025	0.0039	0.0013	0.0013	0.0012	0.0044	0.0024	0.0011	0.0016	0.0023
0.0146	0.6007	1950	0.0105	0.0033	0.0014	0.0022	0.0028	0.0010	0.0012	0.0011	0.0039	0.0017	0.0009	0.0015	0.0019
0.0111	0.8010	2600	0.0118	0.0043	0.0020	0.0027	0.0031	0.0013	0.0014	0.0013	0.0037	0.0019	0.0012	0.0017	0.0022

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu128
Datasets 3.3.2
Tokenizers 0.21.4

Library: transformers
Type: multi_level_ctc
License: mit
Languages: ar
Created: Aug 23, 2025
Updated: Sep 4, 2025