metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: hBERTv1_new_pretrain_w_init__wnli
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE WNLI
          type: glue
          config: wnli
          split: validation
          args: wnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5633802816901409

hBERTv1_new_pretrain_w_init__wnli

This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_wt_init on the GLUE WNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.6855
Accuracy: 0.5634

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: 0.0005
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
12.3688	1.0	5	6.2236	0.5634
3.5093	2.0	10	0.7491	0.4366
1.9112	3.0	15	2.5146	0.5634
1.4995	4.0	20	1.8104	0.4366
1.3047	5.0	25	0.6936	0.5634
1.4685	6.0	30	0.7440	0.5634
0.924	7.0	35	1.1066	0.4366
0.8423	8.0	40	0.8221	0.4366
0.8166	9.0	45	0.6855	0.5634
0.7552	10.0	50	0.7181	0.5634
0.7515	11.0	55	0.6951	0.5634
0.7127	12.0	60	0.7140	0.4366
0.7112	13.0	65	0.6901	0.5634
0.6976	14.0	70	0.7009	0.4366

Framework versions

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