metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9208715596330275
mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2
This model is a fine-tuned version of gokuls/mobilebert_sa_pre-training-complete on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2677
- Accuracy: 0.9209
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 |
---|---|---|---|---|
0.4176 | 1.0 | 527 | 0.2978 | 0.9197 |
0.1807 | 2.0 | 1054 | 0.2951 | 0.9174 |
0.1163 | 3.0 | 1581 | 0.2749 | 0.9186 |
0.0862 | 4.0 | 2108 | 0.2988 | 0.9083 |
0.0695 | 5.0 | 2635 | 0.2760 | 0.9174 |
0.0598 | 6.0 | 3162 | 0.2695 | 0.9151 |
0.0525 | 7.0 | 3689 | 0.2723 | 0.9255 |
0.0464 | 8.0 | 4216 | 0.2430 | 0.9243 |
0.0422 | 9.0 | 4743 | 0.2814 | 0.9243 |
0.0395 | 10.0 | 5270 | 0.2464 | 0.9163 |
0.0357 | 11.0 | 5797 | 0.2390 | 0.9197 |
0.0341 | 12.0 | 6324 | 0.2713 | 0.9197 |
0.0328 | 13.0 | 6851 | 0.2685 | 0.9220 |
0.0315 | 14.0 | 7378 | 0.2585 | 0.9186 |
0.0296 | 15.0 | 7905 | 0.2367 | 0.9220 |
0.0283 | 16.0 | 8432 | 0.2560 | 0.9186 |
0.0277 | 17.0 | 8959 | 0.2635 | 0.9174 |
0.0269 | 18.0 | 9486 | 0.2364 | 0.9266 |
0.026 | 19.0 | 10013 | 0.2749 | 0.9209 |
0.0252 | 20.0 | 10540 | 0.2507 | 0.9174 |
0.0248 | 21.0 | 11067 | 0.2769 | 0.9163 |
0.0248 | 22.0 | 11594 | 0.2543 | 0.9220 |
0.024 | 23.0 | 12121 | 0.2677 | 0.9209 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2