|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
base_model: facebook/w2v-bert-2.0 |
|
model-index: |
|
- name: w2v-bert-2.0-nonstudio_and_studioRecords |
|
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 |
|
|
|
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: 0.1641 |
|
- Wer: 0.1184 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 1.1077 | 0.46 | 600 | 0.4029 | 0.4897 | |
|
| 0.1727 | 0.92 | 1200 | 0.2339 | 0.3573 | |
|
| 0.1224 | 1.38 | 1800 | 0.2159 | 0.3225 | |
|
| 0.1103 | 1.84 | 2400 | 0.1838 | 0.2764 | |
|
| 0.0907 | 2.3 | 3000 | 0.1844 | 0.2603 | |
|
| 0.0796 | 2.76 | 3600 | 0.1829 | 0.2498 | |
|
| 0.0685 | 3.22 | 4200 | 0.1719 | 0.2336 | |
|
| 0.0588 | 3.68 | 4800 | 0.1607 | 0.2030 | |
|
| 0.054 | 4.14 | 5400 | 0.1611 | 0.1941 | |
|
| 0.0424 | 4.6 | 6000 | 0.1536 | 0.1821 | |
|
| 0.0402 | 5.06 | 6600 | 0.1562 | 0.1769 | |
|
| 0.0312 | 5.52 | 7200 | 0.1494 | 0.1655 | |
|
| 0.0303 | 5.98 | 7800 | 0.1471 | 0.1510 | |
|
| 0.0218 | 6.44 | 8400 | 0.1707 | 0.1488 | |
|
| 0.0218 | 6.9 | 9000 | 0.1458 | 0.1296 | |
|
| 0.0151 | 7.36 | 9600 | 0.1424 | 0.1326 | |
|
| 0.014 | 7.82 | 10200 | 0.1406 | 0.1266 | |
|
| 0.0107 | 8.28 | 10800 | 0.1476 | 0.1291 | |
|
| 0.0078 | 8.74 | 11400 | 0.1563 | 0.1254 | |
|
| 0.007 | 9.2 | 12000 | 0.1528 | 0.1197 | |
|
| 0.0041 | 9.66 | 12600 | 0.1641 | 0.1184 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.1 |
|
|
