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