metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
config: qqp
split: validation
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.794855305466238
- name: F1
type: f1
value: 0.7224044447419508
mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.6619
- Accuracy: 0.7949
- F1: 0.7224
- Combined Score: 0.7586
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: 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 | F1 | Combined Score |
---|---|---|---|---|---|---|
0.9454 | 1.0 | 2843 | 0.8257 | 0.7556 | 0.6563 | 0.7059 |
0.8165 | 2.0 | 5686 | 0.7682 | 0.7590 | 0.6049 | 0.6820 |
0.7741 | 3.0 | 8529 | 0.7514 | 0.7638 | 0.6203 | 0.6920 |
0.7325 | 4.0 | 11372 | 0.7354 | 0.7675 | 0.6288 | 0.6981 |
0.6849 | 5.0 | 14215 | 0.7063 | 0.7785 | 0.6818 | 0.7302 |
0.6399 | 6.0 | 17058 | 0.6906 | 0.7828 | 0.6876 | 0.7352 |
0.6005 | 7.0 | 19901 | 0.6771 | 0.7868 | 0.6993 | 0.7430 |
0.5666 | 8.0 | 22744 | 0.6809 | 0.7897 | 0.7138 | 0.7517 |
0.5365 | 9.0 | 25587 | 0.6807 | 0.7886 | 0.6921 | 0.7403 |
0.5097 | 10.0 | 28430 | 0.6827 | 0.7873 | 0.7260 | 0.7566 |
0.4856 | 11.0 | 31273 | 0.6619 | 0.7949 | 0.7224 | 0.7586 |
0.4653 | 12.0 | 34116 | 0.7002 | 0.7948 | 0.7197 | 0.7572 |
0.4438 | 13.0 | 36959 | 0.6900 | 0.7965 | 0.7203 | 0.7584 |
0.4255 | 14.0 | 39802 | 0.6847 | 0.7981 | 0.7284 | 0.7632 |
0.4072 | 15.0 | 42645 | 0.6893 | 0.7917 | 0.7336 | 0.7627 |
0.391 | 16.0 | 45488 | 0.7136 | 0.7957 | 0.7300 | 0.7629 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2