README.md

metadata

library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: w2v2-bert-Wolof-1-hour-Google-Fleurs-dataset
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: wo_sn
          split: None
          args: wo_sn
        metrics:
          - name: Wer
            type: wer
            value: 0.5129422403074488

w2v2-bert-Wolof-1-hour-Google-Fleurs-dataset

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

• Loss: 2.0719
• Wer: 0.5129
• Cer: 0.1804

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: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.8009	25.0	200	1.5180	0.5218	0.1838
0.0103	50.0	400	2.0719	0.5129	0.1804

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.0+cu118
• Datasets 2.17.0
• Tokenizers 0.19.1