README.md

metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: hBERTv1_data_aug_mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 1
          - name: F1
            type: f1
            value: 1

hBERTv1_data_aug_mrpc

This model is a fine-tuned version of gokuls/bert_12_layer_model_v1 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

• Loss: 0.0001
• Accuracy: 1.0
• F1: 1.0
• Combined Score: 1.0

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	F1	Combined Score
0.1151	1.0	980	0.0045	0.9975	0.9982	0.9979
0.0108	2.0	1960	0.0001	1.0	1.0	1.0
0.0063	3.0	2940	0.0001	1.0	1.0	1.0
0.0054	4.0	3920	0.0001	1.0	1.0	1.0
0.004	5.0	4900	0.0001	1.0	1.0	1.0
0.0053	6.0	5880	0.0002	1.0	1.0	1.0
0.0046	7.0	6860	0.0003	1.0	1.0	1.0
0.0116	8.0	7840	0.0150	0.9975	0.9982	0.9979
0.0093	9.0	8820	0.0015	1.0	1.0	1.0
0.0123	10.0	9800	0.0164	0.9975	0.9982	0.9979

Framework versions

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