metadata
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: hBERTv1_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_wnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v1 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6877
- 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: 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 |
|---|---|---|---|---|
| 0.7359 | 1.0 | 3 | 0.7194 | 0.4366 |
| 0.6989 | 2.0 | 6 | 0.6899 | 0.5634 |
| 0.7031 | 3.0 | 9 | 0.7028 | 0.4366 |
| 0.7012 | 4.0 | 12 | 0.6889 | 0.5634 |
| 0.697 | 5.0 | 15 | 0.6894 | 0.5634 |
| 0.6971 | 6.0 | 18 | 0.7015 | 0.4366 |
| 0.7 | 7.0 | 21 | 0.6882 | 0.5634 |
| 0.6928 | 8.0 | 24 | 0.6890 | 0.5634 |
| 0.6932 | 9.0 | 27 | 0.6897 | 0.5634 |
| 0.6954 | 10.0 | 30 | 0.6956 | 0.4366 |
| 0.6962 | 11.0 | 33 | 0.6913 | 0.5634 |
| 0.6956 | 12.0 | 36 | 0.6877 | 0.5634 |
| 0.6973 | 13.0 | 39 | 0.6926 | 0.5070 |
| 0.6978 | 14.0 | 42 | 0.6933 | 0.4930 |
| 0.6945 | 15.0 | 45 | 0.6883 | 0.5634 |
| 0.6974 | 16.0 | 48 | 0.6881 | 0.5634 |
| 0.6936 | 17.0 | 51 | 0.6925 | 0.5211 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2