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w2v-bert-2.0-slovak-colab-CV22.0_test_val_seperated
sabroo/w2v-bert-2.0-slovak-colab-CV22.0_test_val_seperated
w2v-bert-2.0-slovak-colab-CV22.0_test_val_seperated is a automatic speech recognition model from sabroo. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Jul 28, 2025
- Updated
- Jul 28, 2025
- SHA
- ca68d339
Base models
Model card
From the Hugging Face model README
<!-- 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-slovak-colab-CV22.0_test_val_seperated
This model is a fine-tuned version of facebook/w2v-bert-2.0 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3680
- Wer: 0.1583
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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 | Wer |
|---|---|---|---|---|
| 2.3268 | 2.6087 | 300 | 0.3564 | 0.1929 |
| 0.0732 | 5.2174 | 600 | 0.3193 | 0.1722 |
| 0.028 | 7.8261 | 900 | 0.3680 | 0.1583 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Jul 28, 2025
- Updated
- Jul 28, 2025