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w2v-bert-tamil_new
cdactvm/w2v-bert-tamil_new
w2v-bert-tamil_new is a machine learning model from cdactvm. 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
- Dec 4, 2024
- Updated
- Dec 16, 2024
- SHA
- e21e77a7
Base models
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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. -->
w2v-bert-tamil_new
This model is a fine-tuned version of facebook/w2v-bert-2.0 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0960
- Wer: 0.1781
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: 4e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3099 | 0.1547 | 2000 | 0.2685 | 0.4726 |
| 0.2319 | 0.3094 | 4000 | 0.2052 | 0.3246 |
| 0.21 | 0.4640 | 6000 | 0.1702 | 0.2968 |
| 0.1907 | 0.6187 | 8000 | 0.1591 | 0.2809 |
| 0.1789 | 0.7734 | 10000 | 0.1468 | 0.2703 |
| 0.1626 | 0.9281 | 12000 | 0.1482 | 0.2540 |
| 0.1469 | 1.0828 | 14000 | 0.1390 | 0.2536 |
| 0.144 | 1.2375 | 16000 | 0.1298 | 0.2433 |
| 0.1418 | 1.3921 | 18000 | 0.1287 | 0.2399 |
| 0.1349 | 1.5468 | 20000 | 0.1219 | 0.2343 |
| 0.1266 | 1.7015 | 22000 | 0.1229 | 0.2349 |
| 0.1257 | 1.8562 | 24000 | 0.1202 | 0.2241 |
| 0.1209 | 2.0109 | 26000 | 0.1193 | 0.2176 |
| 0.1113 | 2.1655 | 28000 | 0.1146 | 0.2150 |
| 0.1052 | 2.3202 | 30000 | 0.1165 | 0.2234 |
| 0.103 | 2.4749 | 32000 | 0.1130 | 0.2112 |
| 0.0988 | 2.6296 | 34000 | 0.1092 | 0.2029 |
| 0.098 | 2.7843 | 36000 | 0.1061 | 0.2022 |
| 0.1007 | 2.9390 | 38000 | 0.1054 | 0.2036 |
| 0.0823 | 3.0936 | 40000 | 0.1042 | 0.1997 |
| 0.0866 | 3.2483 | 42000 | 0.1020 | 0.1945 |
| 0.0874 | 3.4030 | 44000 | 0.0993 | 0.1972 |
| 0.0825 | 3.5577 | 46000 | 0.1012 | 0.1941 |
| 0.083 | 3.7124 | 48000 | 0.1017 | 0.1911 |
| 0.0724 | 3.8671 | 50000 | 0.0992 | 0.1904 |
| 0.0761 | 4.0217 | 52000 | 0.0983 | 0.1856 |
| 0.0641 | 4.1764 | 54000 | 0.1011 | 0.1857 |
| 0.0611 | 4.3311 | 56000 | 0.0980 | 0.1821 |
| 0.0646 | 4.4858 | 58000 | 0.0982 | 0.1816 |
| 0.062 | 4.6405 | 60000 | 0.0962 | 0.1786 |
| 0.0616 | 4.7951 | 62000 | 0.0951 | 0.1787 |
| 0.0607 | 4.9498 | 64000 | 0.0960 | 0.1781 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Type
- wav2vec2-bert
- License
- mit
- Created
- Dec 4, 2024
- Updated
- Dec 16, 2024