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HBERTv1_emb_compress_48_L10_H768_A12

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metadata
tags:
  - generated_from_trainer
datasets:
  - gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
  - accuracy
model-index:
  - name: HBERTv1_emb_compress_48_L10_H768_A12
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
          type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.3705453911691882

HBERTv1_emb_compress_48_L10_H768_A12

This model is a fine-tuned version of on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 4.1748
  • Accuracy: 0.3705

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: 1e-05
  • train_batch_size: 48
  • eval_batch_size: 48
  • seed: 10
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10000
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
7.1074 0.08 10000 7.0838 0.0828
6.6784 0.16 20000 6.6795 0.1075
6.535 0.25 30000 6.5322 0.1192
6.4482 0.33 40000 6.4390 0.1267
6.3716 0.41 50000 6.3711 0.1324
6.3233 0.49 60000 6.3219 0.1351
6.2821 0.57 70000 6.2781 0.1383
6.251 0.66 80000 6.2431 0.1408
6.2159 0.74 90000 6.2111 0.1425
6.1838 0.82 100000 6.1774 0.1444
6.1338 0.9 110000 6.1349 0.1464
6.1022 0.98 120000 6.0939 0.1481
6.0194 1.07 130000 6.0080 0.1517
5.9309 1.15 140000 5.9199 0.1642
5.8593 1.23 150000 5.8326 0.1769
5.7093 1.31 160000 5.6659 0.2040
5.5018 1.39 170000 5.4433 0.2339
5.3036 1.47 180000 5.2292 0.2576
5.0629 1.56 190000 4.9895 0.2834
4.8311 1.64 200000 4.7638 0.3085
4.6239 1.72 210000 4.5799 0.3278
4.4305 1.8 220000 4.3821 0.3471
4.2209 1.88 230000 4.1749 0.3704

Framework versions

  • Transformers 4.33.2
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.14.5
  • Tokenizers 0.13.3