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malayalam_combined_Read
asr-malayalam/malayalam_combined_Read
malayalam_combined_Read is a machine learning model from asr-malayalam. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Aug 20, 2024
- Updated
- Aug 21, 2024
- SHA
- 7227aa81
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. -->
malayalam_combined_Read
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: 0.1389
- Wer: 0.1305
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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3294 | 0.4378 | 500 | 0.2872 | 0.3888 |
| 0.2522 | 0.8757 | 1000 | 0.2404 | 0.3214 |
| 0.2017 | 1.3135 | 1500 | 0.2100 | 0.3247 |
| 0.19 | 1.7513 | 2000 | 0.2004 | 0.2744 |
| 0.1443 | 2.1891 | 2500 | 0.1791 | 0.2370 |
| 0.1484 | 2.6270 | 3000 | 0.1684 | 0.2309 |
| 0.1232 | 3.0648 | 3500 | 0.1672 | 0.2114 |
| 0.1135 | 3.5026 | 4000 | 0.1584 | 0.2031 |
| 0.1152 | 3.9405 | 4500 | 0.1457 | 0.1841 |
| 0.0904 | 4.3783 | 5000 | 0.1528 | 0.1870 |
| 0.0887 | 4.8161 | 5500 | 0.1455 | 0.1726 |
| 0.0768 | 5.2539 | 6000 | 0.1471 | 0.1636 |
| 0.069 | 5.6918 | 6500 | 0.1463 | 0.1578 |
| 0.0587 | 6.1296 | 7000 | 0.1401 | 0.1582 |
| 0.0537 | 6.5674 | 7500 | 0.1377 | 0.1485 |
| 0.0534 | 7.0053 | 8000 | 0.1278 | 0.1459 |
| 0.0397 | 7.4431 | 8500 | 0.1284 | 0.1418 |
| 0.0433 | 7.8809 | 9000 | 0.1274 | 0.1414 |
| 0.0323 | 8.3187 | 9500 | 0.1299 | 0.1357 |
| 0.0364 | 8.7566 | 10000 | 0.1340 | 0.1348 |
| 0.0219 | 9.1944 | 10500 | 0.1350 | 0.1335 |
| 0.0245 | 9.6322 | 11000 | 0.1389 | 0.1305 |
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.16.1
- Tokenizers 0.19.1
- Type
- wav2vec2-bert
- License
- mit
- Created
- Aug 20, 2024
- Updated
- Aug 21, 2024