|
--- |
|
license: mit |
|
base_model: facebook/w2v-bert-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- common_voice_17_0 |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: w2v-bert-2.0-sv |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: common_voice_17_0 |
|
type: common_voice_17_0 |
|
config: sv-SE |
|
split: test |
|
args: sv-SE |
|
metrics: |
|
- name: Wer |
|
type: wer |
|
value: 0.10046931592103249 |
|
language: |
|
- sv |
|
--- |
|
|
|
<!-- 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-sv |
|
|
|
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1962 |
|
- Wer: 0.1005 |
|
|
|
## 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 | |
|
|:-------------:|:------:|:----:|:---------------:|:------:| |
|
| 2.075 | 0.7407 | 300 | 0.3441 | 0.3057 | |
|
| 0.2837 | 1.4815 | 600 | 0.2995 | 0.2274 | |
|
| 0.2081 | 2.2222 | 900 | 0.2443 | 0.1768 | |
|
| 0.1579 | 2.9630 | 1200 | 0.2143 | 0.1493 | |
|
| 0.1248 | 3.7037 | 1500 | 0.2165 | 0.1504 | |
|
| 0.0934 | 4.4444 | 1800 | 0.1869 | 0.1284 | |
|
| 0.0719 | 5.1852 | 2100 | 0.2072 | 0.1216 | |
|
| 0.0573 | 5.9259 | 2400 | 0.1949 | 0.1195 | |
|
| 0.0436 | 6.6667 | 2700 | 0.2025 | 0.1142 | |
|
| 0.0317 | 7.4074 | 3000 | 0.2003 | 0.1097 | |
|
| 0.0256 | 8.1481 | 3300 | 0.1942 | 0.1060 | |
|
| 0.0169 | 8.8889 | 3600 | 0.1851 | 0.1030 | |
|
| 0.0121 | 9.6296 | 3900 | 0.1962 | 0.1005 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
