---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- genbed
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-bem-genbed-combined-model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-bem-genbed-combined-model

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the GENBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2969
- Wer: 0.4669

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.6407        | 0.5495 | 200  | 0.6847          | 0.8381 |
| 0.458         | 1.0989 | 400  | 0.4856          | 0.6787 |
| 0.4014        | 1.6484 | 600  | 0.4310          | 0.6258 |
| 0.3523        | 2.1978 | 800  | 0.3654          | 0.5422 |
| 0.3298        | 2.7473 | 1000 | 0.3534          | 0.5374 |
| 0.2749        | 3.2967 | 1200 | 0.3402          | 0.5196 |
| 0.2705        | 3.8462 | 1400 | 0.3284          | 0.5250 |
| 0.249         | 4.3956 | 1600 | 0.3499          | 0.5299 |
| 0.2508        | 4.9451 | 1800 | 0.3512          | 0.5582 |
| 0.2081        | 5.4945 | 2000 | 0.3217          | 0.4808 |
| 0.2176        | 6.0440 | 2200 | 0.3141          | 0.472  |
| 0.1784        | 6.5934 | 2400 | 0.2969          | 0.4669 |
| 0.166         | 7.1429 | 2600 | 0.3367          | 0.4914 |
| 0.157         | 7.6923 | 2800 | 0.3206          | 0.4903 |
| 0.1398        | 8.2418 | 3000 | 0.3260          | 0.4617 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0