Hugging Face's logo

philschmid
/
bert-mini-sst2-distilled

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
2
bert-mini-sst2-distilled / README.md
philschmid's picture
philschmid HF staff
End of training
d1e81c8
preview code
|
raw
history blame contribute delete
No virus
2.1 kB
metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: bert-mini-sst2-distilled
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          args: sst2
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.856651376146789

bert-mini-sst2-distilled

This model is a fine-tuned version of google/bert_uncased_L-4_H-256_A-4 on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1792
  • Accuracy: 0.8567

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: 0.00021185586235152412
  • train_batch_size: 1024
  • eval_batch_size: 1024
  • seed: 33
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.1552 1.0 66 1.4847 0.8349
0.8451 2.0 132 1.3495 0.8624
0.5864 3.0 198 1.2257 0.8532
0.4553 4.0 264 1.2571 0.8544
0.3708 5.0 330 1.2132 0.8658
0.3086 6.0 396 1.2370 0.8589
0.2701 7.0 462 1.1900 0.8635
0.246 8.0 528 1.1792 0.8567

Framework versions

  • Transformers 4.12.3
  • Pytorch 1.9.1
  • Datasets 1.15.1
  • Tokenizers 0.10.3