---
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-lg-cv-5hr-v1
  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-lg-cv-5hr-v1

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: 2.8566
- Model Preparation Time: 0.0165
- Wer: 0.9775
- Cer: 0.8923

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Model Preparation Time | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:----------------------:|:------:|:------:|
| 9.9607        | 0.9948  | 95   | 6.8754          | 0.0165                 | 1.0    | 1.0    |
| 5.2586        | 2.0     | 191  | 4.0569          | 0.0165                 | 1.0    | 1.0    |
| 3.4197        | 2.9948  | 286  | 3.0508          | 0.0165                 | 1.0    | 1.0    |
| 2.9792        | 4.0     | 382  | 2.9586          | 0.0165                 | 1.0    | 1.0    |
| 2.9646        | 4.9948  | 477  | 2.9354          | 0.0165                 | 1.0    | 1.0    |
| 2.9169        | 6.0     | 573  | 2.9220          | 0.0165                 | 1.0    | 1.0    |
| 2.9372        | 6.9948  | 668  | 2.9116          | 0.0165                 | 1.0    | 1.0    |
| 2.8971        | 8.0     | 764  | 2.8998          | 0.0165                 | 1.0    | 0.9811 |
| 2.918         | 8.9948  | 859  | 2.8893          | 0.0165                 | 0.9983 | 0.9652 |
| 2.8795        | 10.0    | 955  | 2.8804          | 0.0165                 | 0.9985 | 0.9534 |
| 2.9006        | 10.9948 | 1050 | 2.8683          | 0.0165                 | 1.0    | 0.9048 |
| 2.8598        | 12.0    | 1146 | 2.8554          | 0.0165                 | 1.0    | 0.9067 |
| 2.8776        | 12.9948 | 1241 | 2.8417          | 0.0165                 | 1.0    | 0.8954 |
| 2.8393        | 14.0    | 1337 | 2.8407          | 0.0165                 | 0.9970 | 0.9074 |
| 2.8637        | 14.9948 | 1432 | 2.8304          | 0.0165                 | 0.9787 | 0.8824 |
| 2.8264        | 16.0    | 1528 | 2.8257          | 0.0165                 | 0.9776 | 0.8934 |
| 2.846         | 16.9948 | 1623 | 2.8045          | 0.0165                 | 1.0    | 0.8653 |
| 2.8001        | 18.0    | 1719 | 2.7907          | 0.0165                 | 1.0022 | 0.8459 |
| 2.8103        | 18.9948 | 1814 | 2.7686          | 0.0165                 | 0.9991 | 0.8579 |
| 2.7683        | 20.0    | 1910 | 2.7518          | 0.0165                 | 0.9991 | 0.8534 |
| 2.7903        | 20.9948 | 2005 | 2.7481          | 0.0165                 | 0.9980 | 0.8568 |
| 2.7561        | 22.0    | 2101 | 2.7468          | 0.0165                 | 0.9991 | 0.8478 |
| 2.782         | 22.9948 | 2196 | 2.7383          | 0.0165                 | 0.9978 | 0.8497 |
| 2.7473        | 24.0    | 2292 | 2.7345          | 0.0165                 | 0.9993 | 0.8492 |
| 2.771         | 24.9948 | 2387 | 2.7175          | 0.0165                 | 0.9970 | 0.8258 |
| 2.7049        | 26.0    | 2483 | 2.6822          | 0.0165                 | 1.0260 | 0.7733 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1