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---
base_model: ylacombe/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-dutch-colab-CV16.0
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-dutch-colab-CV16.0
This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0920
- Wer: 0.0573
## 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: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3106 | 1.0 | 358 | 0.1833 | 0.1350 |
| 0.0924 | 2.0 | 716 | 0.1365 | 0.0932 |
| 0.0521 | 3.0 | 1074 | 0.1121 | 0.0732 |
| 0.033 | 3.99 | 1432 | 0.0957 | 0.0619 |
| 0.0221 | 4.99 | 1790 | 0.0920 | 0.0573 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.2
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