---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-seeyuh
  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-seeyuh

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4176
- Wer: 0.1450

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.5769        | 0.7524  | 500   | 0.5834          | 0.4912 |
| 0.3206        | 1.5049  | 1000  | 0.4060          | 0.3510 |
| 0.2663        | 2.2573  | 1500  | 0.3600          | 0.2888 |
| 0.2271        | 3.0098  | 2000  | 0.3411          | 0.2574 |
| 0.1823        | 3.7622  | 2500  | 0.3274          | 0.2485 |
| 0.15          | 4.5147  | 3000  | 0.3120          | 0.2219 |
| 0.1328        | 5.2671  | 3500  | 0.2971          | 0.2033 |
| 0.1354        | 6.0196  | 4000  | 0.2908          | 0.1974 |
| 0.1186        | 6.7720  | 4500  | 0.2875          | 0.1917 |
| 0.0617        | 7.5245  | 5000  | 0.3074          | 0.1832 |
| 0.0673        | 8.2769  | 5500  | 0.3146          | 0.1790 |
| 0.0882        | 9.0293  | 6000  | 0.3023          | 0.1687 |
| 0.0622        | 9.7818  | 6500  | 0.3038          | 0.1651 |
| 0.0398        | 10.5342 | 7000  | 0.3230          | 0.1672 |
| 0.027         | 11.2867 | 7500  | 0.3674          | 0.1578 |
| 0.0316        | 12.0391 | 8000  | 0.3585          | 0.1542 |
| 0.0271        | 12.7916 | 8500  | 0.3803          | 0.1499 |
| 0.0364        | 13.5440 | 9000  | 0.3918          | 0.1496 |
| 0.0047        | 14.2965 | 9500  | 0.4113          | 0.1465 |
| 0.0101        | 15.0489 | 10000 | 0.4176          | 0.1450 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1