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jimregan/w2v-bert-2.0-north-sami

🤗 Hugging Face 来源automatic-speech-recognitionmit606M 参数2.4 GBsafetensors✓ 5 个校验和今天更新
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在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对，然后提交。你只需开始做种，并粘贴你账户中的密钥。它只读取你的文件，绝不修改。如果愿意，可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo jimregan/w2v-bert-2.0-north-sami ./model-folder
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w2v-bert-2.0-north-sami

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: 0.9778
  • Cer: 0.2753
  • Wer: 0.8105

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: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 6
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Cer Wer
3.9302 0.28 200 2.6591 0.6390 1.0156
1.9387 0.55 400 0.9924 0.2427 0.8257
1.5598 0.83 600 0.8492 0.2243 0.7684
1.4375 1.11 800 0.7714 0.2179 0.7595
1.389 1.38 1000 0.7074 0.1813 0.6970
1.3134 1.66 1200 0.6397 0.1668 0.6354
1.2836 1.94 1400 0.6282 0.1648 0.6241
1.1205 2.21 1600 0.6423 0.1708 0.6629
1.067 2.49 1800 0.5731 0.1636 0.6376
1.0965 2.76 2000 0.5496 0.1498 0.5932
0.975 3.04 2200 0.5305 0.1381 0.5460
0.8759 3.32 2400 0.5325 0.1404 0.5612
0.9537 3.59 2600 0.5397 0.1478 0.5570
0.887 3.87 2800 0.5264 0.1351 0.5354
0.8123 4.15 3000 0.5117 0.1413 0.5481
0.7681 4.42 3200 0.5105 0.1324 0.5283
0.811 4.7 3400 0.5330 0.1488 0.5612
0.8249 4.98 3600 0.6151 0.1592 0.5873
0.8376 5.25 3800 0.5518 0.1492 0.5755
0.9814 5.53 4000 0.8945 0.2293 0.7979
1.2369 5.81 4200 0.9778 0.2753 0.8105

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2