metadata
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
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