---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
- quran
- ASR
model-index:
- name: muaalem-model-v3
  results: []
language:
- ar
metrics:
- cer
datasets:
- obadx/muaalem-annotated-v3
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# muaalem-model-v3

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/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](https://huggingface.co/papers/2509.00094)

## 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