---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0-gui
  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-gui

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: 2.9872
- Cer: 0.9839

## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 20.5172       | 0.4329 | 100  | 6.1927          | 1.0    |
| 9.1755        | 0.8658 | 200  | 3.0737          | 1.0    |
| 5.9769        | 1.2987 | 300  | 2.9275          | 0.9839 |
| 5.8767        | 1.7316 | 400  | 2.9258          | 0.9839 |
| 6.0773        | 2.1645 | 500  | 2.9087          | 0.9839 |
| 5.9225        | 2.5974 | 600  | 2.9052          | 0.9839 |
| 5.9328        | 3.0303 | 700  | 2.9009          | 0.9839 |
| 5.8895        | 3.4632 | 800  | 2.8969          | 0.9368 |
| 5.8888        | 3.8961 | 900  | 2.9218          | 0.9839 |
| 5.9117        | 4.3290 | 1000 | 2.9595          | 0.9672 |
| 5.9625        | 4.7619 | 1100 | 2.9033          | 0.9839 |
| 6.0566        | 5.1948 | 1200 | 2.9598          | 0.9839 |
| 5.9214        | 5.6277 | 1300 | 2.9107          | 0.9839 |
| 5.9976        | 6.0606 | 1400 | 2.9289          | 0.9839 |
| 5.9904        | 6.4935 | 1500 | 2.9166          | 0.9839 |
| 5.9354        | 6.9264 | 1600 | 2.9257          | 0.9839 |
| 6.0097        | 7.3593 | 1700 | 2.9428          | 0.9839 |
| 6.0392        | 7.7922 | 1800 | 2.9378          | 0.9839 |
| 5.9639        | 8.2251 | 1900 | 2.9657          | 0.9839 |
| 6.0595        | 8.6580 | 2000 | 2.9771          | 0.9839 |
| 6.0797        | 9.0909 | 2100 | 2.9865          | 0.9839 |
| 6.0741        | 9.5238 | 2200 | 2.9870          | 0.9839 |
| 6.0342        | 9.9567 | 2300 | 2.9872          | 0.9839 |


### Framework versions

- Transformers 5.1.0
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.2