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# mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_qqp

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 QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1715
- Accuracy: 0.9133
- F1: 0.8843
- Combined Score: 0.8988

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3902        | 1.0   | 2843  | 0.2870          | 0.8788   | 0.8463 | 0.8625         |
| 0.226         | 2.0   | 5686  | 0.1968          | 0.8979   | 0.8649 | 0.8814         |
| 0.1681        | 3.0   | 8529  | 0.1910          | 0.9038   | 0.8719 | 0.8878         |
| 0.1346        | 4.0   | 11372 | 0.1989          | 0.9055   | 0.8772 | 0.8914         |
| 0.1124        | 5.0   | 14215 | 0.1970          | 0.9049   | 0.8747 | 0.8898         |
| 0.0978        | 6.0   | 17058 | 0.1876          | 0.9095   | 0.8806 | 0.8950         |
| 0.0876        | 7.0   | 19901 | 0.1893          | 0.9077   | 0.8773 | 0.8925         |
| 0.0802        | 8.0   | 22744 | 0.1940          | 0.9067   | 0.8773 | 0.8920         |
| 0.0746        | 9.0   | 25587 | 0.1846          | 0.9090   | 0.8787 | 0.8938         |
| 0.0699        | 10.0  | 28430 | 0.1890          | 0.9093   | 0.8809 | 0.8951         |
| 0.0663        | 11.0  | 31273 | 0.1803          | 0.9103   | 0.8804 | 0.8953         |
| 0.0632        | 12.0  | 34116 | 0.1905          | 0.9084   | 0.8805 | 0.8945         |
| 0.0606        | 13.0  | 36959 | 0.1835          | 0.9094   | 0.8813 | 0.8953         |
| 0.0583        | 14.0  | 39802 | 0.1786          | 0.9112   | 0.8805 | 0.8958         |
| 0.0562        | 15.0  | 42645 | 0.1900          | 0.9091   | 0.8817 | 0.8954         |
| 0.0546        | 16.0  | 45488 | 0.1753          | 0.9126   | 0.8825 | 0.8975         |
| 0.0529        | 17.0  | 48331 | 0.1761          | 0.9121   | 0.8825 | 0.8973         |
| 0.0515        | 18.0  | 51174 | 0.1784          | 0.9129   | 0.8842 | 0.8986         |
| 0.0501        | 19.0  | 54017 | 0.1730          | 0.9129   | 0.8847 | 0.8988         |
| 0.049         | 20.0  | 56860 | 0.1812          | 0.9116   | 0.8835 | 0.8975         |
| 0.0479        | 21.0  | 59703 | 0.1751          | 0.9115   | 0.8830 | 0.8972         |
| 0.0469        | 22.0  | 62546 | 0.1737          | 0.9120   | 0.8833 | 0.8976         |
| 0.0461        | 23.0  | 65389 | 0.1739          | 0.9129   | 0.8844 | 0.8986         |
| 0.0452        | 24.0  | 68232 | 0.1715          | 0.9133   | 0.8843 | 0.8988         |
| 0.0447        | 25.0  | 71075 | 0.1748          | 0.9119   | 0.8844 | 0.8982         |
| 0.0437        | 26.0  | 73918 | 0.1734          | 0.9129   | 0.8841 | 0.8985         |
| 0.0431        | 27.0  | 76761 | 0.1727          | 0.9125   | 0.8830 | 0.8977         |
| 0.0425        | 28.0  | 79604 | 0.1803          | 0.9120   | 0.8851 | 0.8985         |
| 0.0419        | 29.0  | 82447 | 0.1720          | 0.9124   | 0.8835 | 0.8980         |


### 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", "f1"], "model-index": [{"name": "mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "config": "qqp", "split": "validation", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.9133069502844422, "name": "Accuracy"}, {"type": "f1", "value": 0.8843348843348843, "name": "F1"}]}]}]}
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\_qqp
==========================================================


This model is a fine-tuned version of gokuls/mobilebert\_sa\_pre-training-complete on the GLUE QQP dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1715
* Accuracy: 0.9133
* F1: 0.8843
* Combined Score: 0.8988


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