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w2v-bert-2.0-tamil-gpu-custom_clean_v2

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metadata
license: mit
tags:
  - generated_from_trainer
base_model: facebook/w2v-bert-2.0
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-tamil-gpu-custom_clean_v2
    results: []

w2v-bert-2.0-tamil-gpu-custom_clean_v2

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: 0.1042
  • Wer: 0.1892

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: 2.5356e-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
2.9949 0.25 300 0.5158 0.6736
0.4051 0.5 600 0.1858 0.3035
0.2789 0.76 900 0.1670 0.2730
0.2352 1.01 1200 0.1479 0.2594
0.1988 1.26 1500 0.1428 0.2464
0.1879 1.51 1800 0.1388 0.2391
0.1757 1.76 2100 0.1244 0.2412
0.1683 2.01 2400 0.1278 0.2231
0.1475 2.27 2700 0.1231 0.2240
0.1438 2.52 3000 0.1219 0.2192
0.1422 2.77 3300 0.1216 0.2128
0.1337 3.02 3600 0.1145 0.2087
0.1212 3.27 3900 0.1131 0.2061
0.1195 3.52 4200 0.1159 0.2147
0.1169 3.78 4500 0.1111 0.2083
0.1162 4.03 4800 0.1116 0.2058
0.1073 4.28 5100 0.1138 0.2114
0.1011 4.53 5400 0.1103 0.2057
0.1008 4.78 5700 0.1096 0.2018
0.1016 5.03 6000 0.1045 0.2008
0.092 5.29 6300 0.1104 0.2008
0.0889 5.54 6600 0.1079 0.2005
0.0936 5.79 6900 0.1036 0.2026
0.0888 6.04 7200 0.1106 0.2109
0.0836 6.29 7500 0.1115 0.2103
0.0807 6.54 7800 0.1104 0.2045
0.0807 6.8 8100 0.1051 0.2039
0.0784 7.05 8400 0.1067 0.1947
0.0719 7.3 8700 0.1051 0.1957
0.0735 7.55 9000 0.1084 0.1894
0.0715 7.8 9300 0.1029 0.1916
0.0732 8.05 9600 0.1059 0.1894
0.0673 8.31 9900 0.1053 0.1890
0.0642 8.56 10200 0.1042 0.1879
0.0669 8.81 10500 0.1039 0.1877
0.0665 9.06 10800 0.1043 0.1881
0.0606 9.31 11100 0.1027 0.1870
0.0615 9.56 11400 0.1046 0.1887
0.0602 9.82 11700 0.1042 0.1892

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2