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---
base_model: facebook/w2v-bert-2.0
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0-nonstudio_and_studioRecords_final
results: []
---
<!-- 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-2.0-nonstudio_and_studioRecords_final
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co./facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1772
- Wer: 0.1266
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 1.055 | 0.4601 | 600 | 0.3683 | 0.4608 |
| 0.1734 | 0.9202 | 1200 | 0.2620 | 0.3546 |
| 0.1242 | 1.3804 | 1800 | 0.2115 | 0.3018 |
| 0.1075 | 1.8405 | 2400 | 0.2004 | 0.2889 |
| 0.0888 | 2.3006 | 3000 | 0.1870 | 0.2573 |
| 0.078 | 2.7607 | 3600 | 0.1724 | 0.2267 |
| 0.0664 | 3.2209 | 4200 | 0.1572 | 0.2244 |
| 0.0576 | 3.6810 | 4800 | 0.1746 | 0.2217 |
| 0.0522 | 4.1411 | 5400 | 0.1643 | 0.1796 |
| 0.0415 | 4.6012 | 6000 | 0.1781 | 0.1851 |
| 0.0398 | 5.0613 | 6600 | 0.1670 | 0.1714 |
| 0.0301 | 5.5215 | 7200 | 0.1531 | 0.1617 |
| 0.0296 | 5.9816 | 7800 | 0.1463 | 0.1590 |
| 0.0211 | 6.4417 | 8400 | 0.1566 | 0.1473 |
| 0.0206 | 6.9018 | 9000 | 0.1423 | 0.1468 |
| 0.0147 | 7.3620 | 9600 | 0.1443 | 0.1413 |
| 0.0136 | 7.8221 | 10200 | 0.1539 | 0.1418 |
| 0.0105 | 8.2822 | 10800 | 0.1611 | 0.1383 |
| 0.0079 | 8.7423 | 11400 | 0.1761 | 0.1351 |
| 0.0063 | 9.2025 | 12000 | 0.1814 | 0.1304 |
| 0.0043 | 9.6626 | 12600 | 0.1772 | 0.1266 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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