---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- natbed
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-bem-natbed-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-natbed-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 NATBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6289
- Wer: 0.6078

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.0078        | 0.5006 | 200  | 0.9815          | 0.8201 |
| 0.8769        | 1.0013 | 400  | 0.9823          | 1.0433 |
| 0.805         | 1.5019 | 600  | 0.8306          | 0.8606 |
| 0.8141        | 2.0025 | 800  | 0.7548          | 0.7196 |
| 0.7132        | 2.5031 | 1000 | 0.7485          | 0.6932 |
| 0.7058        | 3.0038 | 1200 | 0.7280          | 0.6917 |
| 0.6563        | 3.5044 | 1400 | 0.7046          | 0.7045 |
| 0.6232        | 4.0050 | 1600 | 0.7186          | 0.7409 |
| 0.6093        | 4.5056 | 1800 | 0.7048          | 0.6434 |
| 0.5767        | 5.0063 | 2000 | 0.6521          | 0.6474 |
| 0.5628        | 5.5069 | 2200 | 0.6322          | 0.6018 |
| 0.5569        | 6.0075 | 2400 | 0.6289          | 0.6078 |
| 0.5156        | 6.5081 | 2600 | 0.6504          | 0.6374 |
| 0.5074        | 7.0088 | 2800 | 0.6638          | 0.6222 |
| 0.4906        | 7.5094 | 3000 | 0.6744          | 0.5884 |


### Framework versions

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