Instructions to use AbdullahHedeya/muaalem-model-w2v2bert-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbdullahHedeya/muaalem-model-w2v2bert-384 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Wav2Vec2BertForMultilevelCTC model = Wav2Vec2BertForMultilevelCTC.from_pretrained("AbdullahHedeya/muaalem-model-w2v2bert-384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
muaalem-model-w2v2bert-384
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0142
- Per Phonemes: 0.0061
- Per Hams Or Jahr: 0.0035
- Per Shidda Or Rakhawa: 0.0056
- Per Tafkheem Or Taqeeq: 0.0031
- Per Itbaq: 0.0072
- Per Safeer: 0.0024
- Per Qalqla: 0.0021
- Per Tikraar: 0.0024
- Per Tafashie: 0.0018
- Per Istitala: 0.0018
- Per Ghonna: 0.0034
- Average Per: 0.0036
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: 16
- eval_batch_size: 16
- 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_steps: 0.2
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Average Per | Validation Loss | Per Ghonna | Per Hams Or Jahr | Per Istitala | Per Itbaq | Per Phonemes | Per Qalqla | Per Safeer | Per Shidda Or Rakhawa | Per Tafashie | Per Tafkheem Or Taqeeq | Per Tikraar |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3752 | 1.0 | 12131 | 0.0681 | 0.3741 | 0.0652 | 0.0813 | 0.0328 | 0.0463 | 0.1614 | 0.0402 | 0.0467 | 0.1293 | 0.0355 | 0.0639 | 0.0470 |
| 0.1774 | 2.0 | 24262 | 0.0326 | 0.1703 | 0.0321 | 0.0369 | 0.0168 | 0.0314 | 0.0708 | 0.0185 | 0.0214 | 0.0583 | 0.0198 | 0.0305 | 0.0220 |
| 0.1179 | 3.0 | 36393 | 0.0256 | 0.1186 | 0.0247 | 0.0273 | 0.0145 | 0.0284 | 0.0507 | 0.0153 | 0.0179 | 0.0420 | 0.0172 | 0.0248 | 0.0192 |
| 0.0847 | 4.0 | 48524 | 0.0182 | 0.0869 | 0.0173 | 0.0185 | 0.0094 | 0.0243 | 0.0363 | 0.0109 | 0.0121 | 0.0290 | 0.0126 | 0.0165 | 0.0130 |
| 0.0681 | 5.0 | 60655 | 0.0200 | 0.0620 | 0.0194 | 0.0181 | 0.0132 | 0.0350 | 0.0266 | 0.0137 | 0.0150 | 0.0270 | 0.0188 | 0.0180 | 0.0149 |
| 0.0482 | 6.0 | 72786 | 0.0139 | 0.0503 | 0.0142 | 0.0132 | 0.0085 | 0.0227 | 0.0210 | 0.0094 | 0.0107 | 0.0193 | 0.0113 | 0.0123 | 0.0106 |
| 0.0443 | 7.0 | 84917 | 0.0107 | 0.0517 | 0.0100 | 0.0103 | 0.0053 | 0.0158 | 0.0226 | 0.0063 | 0.0075 | 0.0172 | 0.0067 | 0.0096 | 0.0067 |
| 0.0455 | 8.0 | 97048 | 0.0104 | 0.0397 | 0.0099 | 0.0102 | 0.0059 | 0.0181 | 0.0174 | 0.0074 | 0.0079 | 0.0147 | 0.0059 | 0.0090 | 0.0077 |
| 0.0434 | 9.0 | 109179 | 0.0094 | 0.0374 | 0.0086 | 0.0088 | 0.0055 | 0.0164 | 0.0156 | 0.0064 | 0.0068 | 0.0134 | 0.0056 | 0.0092 | 0.0071 |
| 0.0425 | 10.0 | 121310 | 0.0101 | 0.0376 | 0.0086 | 0.0092 | 0.0045 | 0.0275 | 0.0156 | 0.0054 | 0.0059 | 0.0144 | 0.0056 | 0.0074 | 0.0065 |
| 0.0174 | 11.0 | 133441 | 0.0209 | 0.0084 | 0.0042 | 0.0071 | 0.0038 | 0.0067 | 0.0030 | 0.0025 | 0.0029 | 0.0025 | 0.0021 | 0.0041 | 0.0043 |
| 0.0102 | 12.0 | 145572 | 0.0142 | 0.0061 | 0.0035 | 0.0056 | 0.0031 | 0.0072 | 0.0024 | 0.0021 | 0.0024 | 0.0018 | 0.0018 | 0.0034 | 0.0036 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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