metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: hBERTv2_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: 0.6838235294117647
          - name: F1
            type: f1
            value: 0.8122270742358079

hBERTv2_data_aug_mrpc

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

Loss: 0.6240
Accuracy: 0.6838
F1: 0.8122
Combined Score: 0.7480

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
0.6319	1.0	980	0.6245	0.6838	0.8122	0.7480
0.6305	2.0	1960	0.6240	0.6838	0.8122	0.7480
0.6303	3.0	2940	0.6259	0.6838	0.8122	0.7480
0.6302	4.0	3920	0.6252	0.6838	0.8122	0.7480
0.6302	5.0	4900	0.6241	0.6838	0.8122	0.7480
0.6302	6.0	5880	0.6241	0.6838	0.8122	0.7480
0.6301	7.0	6860	0.6242	0.6838	0.8122	0.7480

Framework versions

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