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Muaalem-model-dev
nour-world/Muaalem-model-dev
Muaalem-model-dev is a machine learning model from nour-world. 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
- Sep 15, 2026
- Updated
- Sep 15, 2026
- SHA
- 7cc7e12b
Base models
Model card
From the Hugging Face model README
<!-- 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-dev
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: 0.0212
- Per Phonemes: 0.0058
- Per Hams Or Jahr: 0.0026
- Per Shidda Or Rakhawa: 0.0040
- Per Tafkheem Or Taqeeq: 0.0030
- Per Itbaq: 0.0019
- Per Safeer: 0.0022
- Per Qalqla: 0.0020
- Per Tikraar: 0.0023
- Per Tafashie: 0.0160
- Per Istitala: 0.0019
- Per Ghonna: 0.0027
- Average Per: 0.0040
Model description
More information needed
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: 90
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8303 | 0.2022 | 73 | 0.1027 | 0.0612 | 0.0229 | 0.0245 | 0.1080 | 0.0327 | 0.0658 | 0.0270 | 0.0433 | 0.2467 | 0.0236 | 0.0243 | 0.0618 |
| 0.0593 | 0.4044 | 146 | 0.0451 | 0.0207 | 0.0041 | 0.0055 | 0.0066 | 0.0032 | 0.0042 | 0.0042 | 0.0032 | 0.0566 | 0.0029 | 0.0038 | 0.0105 |
| 0.0434 | 0.6066 | 219 | 0.0306 | 0.0081 | 0.0031 | 0.0043 | 0.0032 | 0.0021 | 0.0025 | 0.0026 | 0.0028 | 0.0322 | 0.0024 | 0.0034 | 0.0061 |
| 0.032 | 0.8089 | 292 | 0.0212 | 0.0058 | 0.0026 | 0.0040 | 0.0030 | 0.0019 | 0.0022 | 0.0020 | 0.0023 | 0.0160 | 0.0019 | 0.0027 | 0.0040 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 3.3.2
- Tokenizers 0.21.4
- Library
- transformers
- Type
- multi_level_ctc
- License
- mit
- Created
- Sep 15, 2026
- Updated
- Sep 15, 2026