metadata

library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-slovak-colab-CV17.0
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: sk
          split: test
          args: sk
        metrics:
          - name: Wer
            type: wer
            value: 0.13279330117411486

w2v-bert-2.0-slovak-colab-CV17.0

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.3002
Wer: 0.1328

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: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.3467	1.6393	300	0.3488	0.2605
0.1905	3.2787	600	0.3339	0.2059
0.1121	4.9180	900	0.3009	0.1849
0.0592	6.5574	1200	0.2817	0.1482
0.0264	8.1967	1500	0.3114	0.1385
0.0094	9.8361	1800	0.3002	0.1328

Framework versions

Transformers 4.45.2
Pytorch 2.5.1
Datasets 2.19.1
Tokenizers 0.20.1