hBERTv2_sst2 / README.md
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metadata
language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: hBERTv2_sst2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE SST2
          type: glue
          config: sst2
          split: validation
          args: sst2
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5091743119266054

hBERTv2_sst2

This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE SST2 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6964
  • Accuracy: 0.5092

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
0.6916 1.0 264 0.6999 0.5092
0.6885 2.0 528 0.6978 0.5092
0.6871 3.0 792 0.6984 0.5092
0.6869 4.0 1056 0.6990 0.5092
0.6868 5.0 1320 0.6974 0.5092
0.6869 6.0 1584 0.6980 0.5092
0.6867 7.0 1848 0.6984 0.5092
0.6868 8.0 2112 0.6975 0.5092
0.6868 9.0 2376 0.6964 0.5092
0.6865 10.0 2640 0.6978 0.5092
0.6868 11.0 2904 0.6980 0.5092
0.6865 12.0 3168 0.7001 0.5092
0.6867 13.0 3432 0.6966 0.5092
0.6867 14.0 3696 0.6980 0.5092

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.10.1
  • Tokenizers 0.13.2