README.md

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-czech-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: cs
          split: test
          args: cs
        metrics:
          - name: Wer
            type: wer
            value: 0.05608571906914448

w2v-bert-2.0-czech-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: 0.1030
• Wer: 0.0561

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
1.6496	0.6579	300	0.1459	0.1211
0.0878	1.3158	600	0.1426	0.1034
0.0713	1.9737	900	0.1136	0.0925
0.0478	2.6316	1200	0.1084	0.0815
0.04	3.2895	1500	0.0980	0.0778
0.0309	3.9474	1800	0.0973	0.0723
0.0218	4.6053	2100	0.1035	0.0681
0.0218	5.2632	2400	0.0997	0.0658
0.0157	5.9211	2700	0.0924	0.0693
0.012	6.5789	3000	0.0957	0.0621
0.0103	7.2368	3300	0.0985	0.0623
0.0082	7.8947	3600	0.0942	0.0594
0.0051	8.5526	3900	0.1028	0.0569
0.0042	9.2105	4200	0.1021	0.0567
0.0031	9.8684	4500	0.1030	0.0561

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.1
• Datasets 2.19.1
• Tokenizers 0.20.1