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# mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_mrpc

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 MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2291
- Accuracy: 0.8578
- F1: 0.8993
- Combined Score: 0.8786

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.536         | 1.0   | 29   | 0.4134          | 0.7279   | 0.8284 | 0.7782         |
| 0.3419        | 2.0   | 58   | 0.3005          | 0.8284   | 0.8801 | 0.8543         |
| 0.2413        | 3.0   | 87   | 0.2707          | 0.8235   | 0.8780 | 0.8507         |
| 0.1852        | 4.0   | 116  | 0.3247          | 0.8284   | 0.8837 | 0.8561         |
| 0.1524        | 5.0   | 145  | 0.2856          | 0.8431   | 0.8900 | 0.8666         |
| 0.1297        | 6.0   | 174  | 0.2999          | 0.8456   | 0.8948 | 0.8702         |
| 0.1219        | 7.0   | 203  | 0.2797          | 0.8529   | 0.8986 | 0.8758         |
| 0.1141        | 8.0   | 232  | 0.2462          | 0.8603   | 0.9005 | 0.8804         |
| 0.1127        | 9.0   | 261  | 0.2557          | 0.8578   | 0.8982 | 0.8780         |
| 0.1091        | 10.0  | 290  | 0.2853          | 0.8480   | 0.8967 | 0.8724         |
| 0.1007        | 11.0  | 319  | 0.2472          | 0.8554   | 0.8981 | 0.8767         |
| 0.0979        | 12.0  | 348  | 0.2431          | 0.8505   | 0.8950 | 0.8727         |
| 0.0954        | 13.0  | 377  | 0.2456          | 0.8578   | 0.9007 | 0.8793         |
| 0.0946        | 14.0  | 406  | 0.2526          | 0.8578   | 0.9017 | 0.8798         |
| 0.0946        | 15.0  | 435  | 0.2291          | 0.8578   | 0.8993 | 0.8786         |
| 0.0938        | 16.0  | 464  | 0.2452          | 0.8603   | 0.9029 | 0.8816         |
| 0.0919        | 17.0  | 493  | 0.2365          | 0.8652   | 0.9050 | 0.8851         |
| 0.0916        | 18.0  | 522  | 0.2363          | 0.8652   | 0.9060 | 0.8856         |
| 0.0915        | 19.0  | 551  | 0.2432          | 0.8652   | 0.9063 | 0.8857         |
| 0.0905        | 20.0  | 580  | 0.2297          | 0.8652   | 0.9057 | 0.8854         |


### 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_mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "config": "mrpc", "split": "validation", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8578431372549019, "name": "Accuracy"}, {"type": "f1", "value": 0.8993055555555555, "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\_mrpc
===========================================================


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


* Loss: 0.2291
* Accuracy: 0.8578
* F1: 0.8993
* Combined Score: 0.8786


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