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# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_192

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3961
- Accuracy: 0.8848
- F1: 0.9080
- Combined Score: 0.8964

## 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: 256
- eval_batch_size: 256
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4524        | 1.0   | 980   | 0.4024          | 0.9779   | 0.9837 | 0.9808         |
| 0.4195        | 2.0   | 1960  | 0.3999          | 0.9779   | 0.9837 | 0.9808         |
| 0.416         | 3.0   | 2940  | 0.3984          | 0.9706   | 0.9781 | 0.9743         |
| 0.4145        | 4.0   | 3920  | 0.3981          | 0.9853   | 0.9892 | 0.9872         |
| 0.4133        | 5.0   | 4900  | 0.3983          | 0.9926   | 0.9946 | 0.9936         |
| 0.4128        | 6.0   | 5880  | 0.3982          | 0.9951   | 0.9964 | 0.9958         |
| 0.4124        | 7.0   | 6860  | 0.3968          | 0.9951   | 0.9964 | 0.9958         |
| 0.4121        | 8.0   | 7840  | 0.3968          | 0.9951   | 0.9964 | 0.9958         |
| 0.4118        | 9.0   | 8820  | 0.3969          | 0.9926   | 0.9946 | 0.9936         |
| 0.4115        | 10.0  | 9800  | 0.3967          | 0.9926   | 0.9946 | 0.9936         |
| 0.4114        | 11.0  | 10780 | 0.3967          | 0.9902   | 0.9928 | 0.9915         |
| 0.4113        | 12.0  | 11760 | 0.3970          | 0.9608   | 0.9705 | 0.9656         |
| 0.4112        | 13.0  | 12740 | 0.3967          | 0.9902   | 0.9928 | 0.9915         |
| 0.4112        | 14.0  | 13720 | 0.3967          | 0.9926   | 0.9946 | 0.9936         |
| 0.4111        | 15.0  | 14700 | 0.3966          | 0.9877   | 0.9910 | 0.9894         |
| 0.411         | 16.0  | 15680 | 0.3966          | 0.9779   | 0.9836 | 0.9808         |
| 0.4109        | 17.0  | 16660 | 0.3965          | 0.9681   | 0.9761 | 0.9721         |
| 0.4109        | 18.0  | 17640 | 0.3969          | 0.9608   | 0.9705 | 0.9656         |
| 0.4108        | 19.0  | 18620 | 0.3964          | 0.9804   | 0.9855 | 0.9829         |
| 0.4107        | 20.0  | 19600 | 0.3966          | 0.9681   | 0.9761 | 0.9721         |
| 0.4106        | 21.0  | 20580 | 0.3962          | 0.9926   | 0.9946 | 0.9936         |
| 0.4107        | 22.0  | 21560 | 0.3965          | 0.8627   | 0.8884 | 0.8756         |
| 0.4105        | 23.0  | 22540 | 0.3962          | 0.9755   | 0.9818 | 0.9786         |
| 0.4105        | 24.0  | 23520 | 0.3964          | 0.9118   | 0.9310 | 0.9214         |
| 0.4105        | 25.0  | 24500 | 0.3963          | 0.9167   | 0.9351 | 0.9259         |
| 0.4104        | 26.0  | 25480 | 0.3962          | 0.9142   | 0.9331 | 0.9236         |
| 0.4104        | 27.0  | 26460 | 0.3962          | 0.9069   | 0.9269 | 0.9169         |
| 0.4104        | 28.0  | 27440 | 0.3962          | 0.8701   | 0.8950 | 0.8826         |
| 0.4104        | 29.0  | 28420 | 0.3962          | 0.875    | 0.8994 | 0.8872         |
| 0.4104        | 30.0  | 29400 | 0.3961          | 0.8848   | 0.9080 | 0.8964         |
| 0.4103        | 31.0  | 30380 | 0.3961          | 0.8922   | 0.9144 | 0.9033         |
| 0.4102        | 32.0  | 31360 | 0.3961          | 0.8897   | 0.9123 | 0.9010         |
| 0.4102        | 33.0  | 32340 | 0.3961          | 0.8971   | 0.9186 | 0.9078         |
| 0.4102        | 34.0  | 33320 | 0.3961          | 0.8505   | 0.8773 | 0.8639         |
| 0.4103        | 35.0  | 34300 | 0.3962          | 0.8333   | 0.8612 | 0.8473         |


### 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": "distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_192", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8848039215686274, "name": "Accuracy"}, {"type": "f1", "value": 0.9080234833659492, "name": "F1"}]}]}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert\_sa\_GLUE\_Experiment\_logit\_kd\_data\_aug\_mrpc\_192
=================================================================


This model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3961
* Accuracy: 0.8848
* F1: 0.9080
* Combined Score: 0.8964


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: 256
* eval\_batch\_size: 256
* 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
* mixed\_precision\_training: Native AMP


### 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: 256
* eval\_batch\_size: 256
* 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
* mixed\_precision\_training: Native AMP ### 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 #distilbert #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: 256
* eval\_batch\_size: 256
* 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
* mixed\_precision\_training: Native AMP ### 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 #distilbert #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: 256
* eval\_batch\_size: 256
* 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
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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