metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: hBERTv1_new_pretrain_qnli
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QNLI
          type: glue
          config: qnli
          split: validation
          args: qnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6031484532308256

hBERTv1_new_pretrain_qnli

This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6591
Accuracy: 0.6031

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: 4e-05
train_batch_size: 128
eval_batch_size: 128
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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6783	1.0	819	0.6740	0.5861
0.6609	2.0	1638	0.6591	0.6031
0.6594	3.0	2457	0.6743	0.5923
0.6438	4.0	3276	0.6644	0.5876
0.6421	5.0	4095	0.6731	0.5883
0.6488	6.0	4914	0.6720	0.5936
0.6432	7.0	5733	0.6781	0.5923

Framework versions

Transformers 4.29.2
Pytorch 1.14.0a0+410ce96
Datasets 2.12.0
Tokenizers 0.13.3