metadata

base_model: ylacombe/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_16_0
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-600m-turkish-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_0
          type: common_voice_16_0
          config: tr
          split: test
          args: tr
        metrics:
          - name: Wer
            type: wer
            value: 0.13727393664832993

w2v-bert-2.0-600m-turkish-colab

This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1441
Wer: 0.1373

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: 0.0001
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: 1000
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.252	0.29	400	0.3121	0.3150
0.2541	0.58	800	0.3786	0.3441
0.2505	0.88	1200	0.4106	0.3766
0.1958	1.17	1600	0.2974	0.2877
0.1686	1.46	2000	0.2854	0.2736
0.1498	1.75	2400	0.2508	0.2486
0.1343	2.05	2800	0.2315	0.2263
0.1045	2.34	3200	0.2207	0.2243
0.0983	2.63	3600	0.2109	0.2046
0.089	2.92	4000	0.1970	0.1896
0.0726	3.21	4400	0.1963	0.1799
0.0552	3.51	4800	0.1879	0.1778
0.0573	3.8	5200	0.1821	0.1693
0.0421	4.09	5600	0.1602	0.1517
0.0363	4.38	6000	0.1564	0.1485
0.0345	4.67	6400	0.1466	0.1437
0.0294	4.97	6800	0.1441	0.1373

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0