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# mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2

This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2364
- Accuracy: 0.9266

## 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

{"language": ["en"], "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": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "config": "sst2", "split": "validation", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.926605504587156, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard mobilebert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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 SST2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2364
* Accuracy: 0.9266


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



### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.9.0
* Tokenizers 0.13.2