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# mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/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

{"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": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "config": "qqp", "split": "validation", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.794855305466238, "name": "Accuracy"}, {"type": "f1", "value": 0.7224044447419508, "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\_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



### 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