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W2V2-BERT-withLM-Malayalam-Studio
vrclc/W2V2-BERT-withLM-Malayalam-Studio
W2V2-BERT-withLM-Malayalam-Studio is a text generation model from vrclc. Use it when you need the model to write or continue text. 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
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Jul 19, 2024
- Updated
- Jul 31, 2024
- SHA
- bccc85c7
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. -->
W2V2-BERT-withLM-Studio-Malayalam
This model is a fine-tuned version of facebook/w2v-bert-2.0 on IMASC, OpenSLR Malayalam Train split, Festvox Malayalamdataset. It achieves the following results on the evaluation set:
- Loss: 0.1587
- Wer: 0.1157
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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0335 | 0.4932 | 600 | 0.3654 | 0.4387 |
| 0.1531 | 0.9864 | 1200 | 0.2373 | 0.3332 |
| 0.1074 | 1.4797 | 1800 | 0.2069 | 0.2953 |
| 0.0928 | 1.9729 | 2400 | 0.2146 | 0.2814 |
| 0.0734 | 2.4661 | 3000 | 0.1947 | 0.2433 |
| 0.0678 | 2.9593 | 3600 | 0.1938 | 0.2406 |
| 0.0522 | 3.4525 | 4200 | 0.1566 | 0.2053 |
| 0.0493 | 3.9457 | 4800 | 0.1649 | 0.1988 |
| 0.0366 | 4.4390 | 5400 | 0.1417 | 0.1834 |
| 0.0372 | 4.9322 | 6000 | 0.1542 | 0.1749 |
| 0.028 | 5.4254 | 6600 | 0.1476 | 0.1620 |
| 0.0263 | 5.9186 | 7200 | 0.1388 | 0.1622 |
| 0.0195 | 6.4118 | 7800 | 0.1384 | 0.1495 |
| 0.0185 | 6.9051 | 8400 | 0.1351 | 0.1383 |
| 0.0136 | 7.3983 | 9000 | 0.1404 | 0.1344 |
| 0.0119 | 7.8915 | 9600 | 0.1253 | 0.1276 |
| 0.0087 | 8.3847 | 10200 | 0.1443 | 0.1284 |
| 0.0066 | 8.8779 | 10800 | 0.1475 | 0.1252 |
| 0.0049 | 9.3711 | 11400 | 0.1577 | 0.1227 |
| 0.0038 | 9.8644 | 12000 | 0.1587 | 0.1157 |
Framework versions
- Transformers 4.42.2
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Generation
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Languages
- ml
- Created
- Jul 19, 2024
- Updated
- Jul 31, 2024