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

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2403
- Wer: 0.4353

## 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: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.5344        | 0.7027 | 500  | 0.5448          | 0.7379 |
| 0.4242        | 1.4055 | 1000 | 0.3055          | 0.6025 |
| 0.3603        | 2.1082 | 1500 | 0.2693          | 0.5385 |
| 0.3144        | 2.8110 | 2000 | 0.2683          | 0.5529 |
| 0.2656        | 3.5137 | 2500 | 0.2472          | 0.5258 |
| 0.2311        | 4.2164 | 3000 | 0.2352          | 0.5026 |
| 0.2106        | 4.9192 | 3500 | 0.2327          | 0.5003 |
| 0.1816        | 5.6219 | 4000 | 0.2298          | 0.4987 |
| 0.1432        | 6.3247 | 4500 | 0.2178          | 0.4686 |
| 0.1431        | 7.0274 | 5000 | 0.2172          | 0.4747 |
| 0.1069        | 7.7301 | 5500 | 0.2136          | 0.4539 |
| 0.0767        | 8.4329 | 6000 | 0.2270          | 0.4403 |
| 0.0667        | 9.1356 | 6500 | 0.2375          | 0.4385 |
| 0.0468        | 9.8384 | 7000 | 0.2403          | 0.4353 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1