---
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-CV_Fleurs_AMMI_ALFFA-sw-5hrs-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-2.0-CV_Fleurs_AMMI_ALFFA-sw-5hrs-v1

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: 0.9564
- Wer: 0.2347
- Cer: 0.0832

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 3.3297        | 0.9972  | 175  | 0.7637          | 0.3590 | 0.1248 |
| 1.348         | 2.0     | 351  | 0.6631          | 0.3172 | 0.1132 |
| 1.0874        | 2.9972  | 526  | 0.6409          | 0.2720 | 0.0965 |
| 0.9039        | 4.0     | 702  | 0.5691          | 0.2759 | 0.1002 |
| 0.7405        | 4.9972  | 877  | 0.5492          | 0.2552 | 0.0905 |
| 0.6896        | 6.0     | 1053 | 0.6369          | 0.2470 | 0.0855 |
| 0.5831        | 6.9972  | 1228 | 0.5966          | 0.2508 | 0.0893 |
| 0.5089        | 8.0     | 1404 | 0.6115          | 0.2403 | 0.0857 |
| 0.4478        | 8.9972  | 1579 | 0.6523          | 0.2300 | 0.0810 |
| 0.4046        | 10.0    | 1755 | 0.6435          | 0.2459 | 0.0842 |
| 0.3745        | 10.9972 | 1930 | 0.6615          | 0.2336 | 0.0821 |
| 0.3461        | 12.0    | 2106 | 0.6885          | 0.2466 | 0.0850 |
| 0.3225        | 12.9972 | 2281 | 0.6068          | 0.2524 | 0.0871 |
| 0.278         | 14.0    | 2457 | 0.6808          | 0.2483 | 0.0850 |
| 0.2494        | 14.9972 | 2632 | 0.7234          | 0.2469 | 0.0846 |
| 0.2273        | 16.0    | 2808 | 0.7661          | 0.2414 | 0.0850 |
| 0.2022        | 16.9972 | 2983 | 0.8284          | 0.2451 | 0.0864 |
| 0.1811        | 18.0    | 3159 | 0.7355          | 0.2431 | 0.0855 |
| 0.1541        | 18.9972 | 3334 | 0.7872          | 0.2426 | 0.0860 |
| 0.1505        | 20.0    | 3510 | 0.7831          | 0.2523 | 0.0875 |
| 0.1373        | 20.9972 | 3685 | 0.8248          | 0.2366 | 0.0845 |
| 0.1213        | 22.0    | 3861 | 0.8190          | 0.2364 | 0.0826 |
| 0.1161        | 22.9972 | 4036 | 0.8505          | 0.2422 | 0.0849 |
| 0.1031        | 24.0    | 4212 | 0.9564          | 0.2347 | 0.0832 |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1