---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2vbert-ctc-salt
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2vbert-ctc-salt

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: 0.3020
- Wer: 0.3905
- Cer: 0.0840

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- training_steps: 15000

### Training results

| Training Loss | Epoch  | Step  | Cer    | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:------:|:---------------:|:------:|
| 5.9489        | 0.2076 | 1500  | 1.0    | 3.0230          | 1.0    |
| 1.5319        | 0.4152 | 3000  | 0.5960 | 0.5589          | 0.1293 |
| 1.1602        | 0.6228 | 4500  | 0.4309 | 0.4809          | 0.1054 |
| 1.0148        | 0.8304 | 6000  | 0.3715 | 0.4499          | 0.0974 |
| 0.9507        | 1.0381 | 7500  | 0.3443 | 0.4274          | 0.0927 |
| 0.9469        | 1.2457 | 9000  | 0.3220 | 0.4031          | 0.0876 |
| 0.8564        | 1.4533 | 10500 | 0.3134 | 0.3995          | 0.0864 |
| 0.8318        | 1.6609 | 12000 | 0.3061 | 0.3951          | 0.0848 |
| 0.8707        | 1.8685 | 13500 | 0.3033 | 0.3904          | 0.0841 |
| 0.9274        | 2.0761 | 15000 | 0.3020 | 0.3905          | 0.0840 |


### Framework versions

- Transformers 5.2.0
- Pytorch 2.10.0+cu130
- Datasets 4.6.0
- Tokenizers 0.22.2