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End of training
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metadata
language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_mnli_96
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MNLI
          type: glue
          config: mnli
          split: validation_matched
          args: mnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5431244914564687

distilbert_sa_GLUE_Experiment_logit_kd_mnli_96

This model is a fine-tuned version of distilbert-base-uncased on the GLUE MNLI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5438
  • Accuracy: 0.5431

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: 256
  • eval_batch_size: 256
  • seed: 10
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6023 1.0 1534 0.5718 0.4960
0.5673 2.0 3068 0.5547 0.5184
0.5555 3.0 4602 0.5505 0.5278
0.5481 4.0 6136 0.5466 0.5381
0.5426 5.0 7670 0.5454 0.5403
0.5382 6.0 9204 0.5454 0.5354
0.5341 7.0 10738 0.5452 0.5344
0.5308 8.0 12272 0.5428 0.5410
0.5271 9.0 13806 0.5460 0.5451
0.5239 10.0 15340 0.5450 0.5462
0.5209 11.0 16874 0.5447 0.5449
0.5179 12.0 18408 0.5452 0.5475
0.5152 13.0 19942 0.5495 0.5454

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.9.0
  • Tokenizers 0.13.2