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metadata
license: apache-2.0
base_model: bert-large-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: BERT_pretraining_h_100_wo_deepspeed
    results: []

BERT_pretraining_h_100_wo_deepspeed

This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 5.7886
  • Accuracy: 0.1537

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: 208
  • eval_batch_size: 208
  • seed: 10
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100000
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
6.8769 0.36 10000 6.7582 0.1101
6.4647 0.71 20000 6.4764 0.1314
6.3679 1.07 30000 6.3218 0.1407
6.252 1.42 40000 6.2139 0.1454
6.2132 1.78 50000 6.1398 0.1478
6.0407 2.13 60000 6.0774 0.1502
6.0694 2.49 70000 6.0303 0.1516
5.9996 2.84 80000 5.9893 0.1521
5.9166 3.2 90000 5.9553 0.1526
5.8915 3.55 100000 5.9261 0.1530
5.8924 3.91 110000 5.8996 0.1534
5.8972 4.26 120000 5.8814 0.1533
5.8454 4.62 130000 5.8626 0.1532
5.8104 4.97 140000 5.8494 0.1534
5.8461 5.33 150000 5.8378 0.1534
5.8476 5.68 160000 5.8246 0.1536
5.7255 6.04 170000 5.8155 0.1532
5.8431 6.39 180000 5.8068 0.1537
5.7526 6.75 190000 5.7981 0.1537
5.7826 7.1 200000 5.7886 0.1537

Framework versions

  • Transformers 4.37.1
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1