|
--- |
|
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: 0.1629 |
|
- Wer: 0.1284 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 1.1293 | 0.46 | 600 | 0.3873 | 0.4777 | |
|
| 0.1724 | 0.92 | 1200 | 0.2435 | 0.3533 | |
|
| 0.1229 | 1.38 | 1800 | 0.2188 | 0.2971 | |
|
| 0.109 | 1.84 | 2400 | 0.2135 | 0.2647 | |
|
| 0.0895 | 2.3 | 3000 | 0.1911 | 0.2441 | |
|
| 0.0779 | 2.76 | 3600 | 0.1738 | 0.2389 | |
|
| 0.0682 | 3.22 | 4200 | 0.1876 | 0.2476 | |
|
| 0.0568 | 3.68 | 4800 | 0.1603 | 0.2140 | |
|
| 0.0527 | 4.14 | 5400 | 0.1697 | 0.1809 | |
|
| 0.0422 | 4.6 | 6000 | 0.1656 | 0.1876 | |
|
| 0.0393 | 5.06 | 6600 | 0.1600 | 0.1732 | |
|
| 0.0311 | 5.52 | 7200 | 0.1522 | 0.1585 | |
|
| 0.0291 | 5.98 | 7800 | 0.1483 | 0.1543 | |
|
| 0.0207 | 6.44 | 8400 | 0.1561 | 0.1483 | |
|
| 0.0208 | 6.9 | 9000 | 0.1502 | 0.1391 | |
|
| 0.0151 | 7.36 | 9600 | 0.1561 | 0.1408 | |
|
| 0.0138 | 7.82 | 10200 | 0.1491 | 0.1296 | |
|
| 0.0108 | 8.28 | 10800 | 0.1472 | 0.1257 | |
|
| 0.008 | 8.74 | 11400 | 0.1658 | 0.1252 | |
|
| 0.0065 | 9.2 | 12000 | 0.1665 | 0.1227 | |
|
| 0.0045 | 9.66 | 12600 | 0.1629 | 0.1284 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.1 |
|
|
