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# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc_96

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: 0.3968
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4718        | 1.0   | 980   | 0.4083          | 0.875    | 0.8998 | 0.8874         |
| 0.4239        | 2.0   | 1960  | 0.3986          | 0.9902   | 0.9928 | 0.9915         |
| 0.4182        | 3.0   | 2940  | 0.3981          | 0.9877   | 0.9910 | 0.9894         |
| 0.4155        | 4.0   | 3920  | 0.3975          | 0.9828   | 0.9873 | 0.9851         |
| 0.4146        | 5.0   | 4900  | 0.3969          | 0.9902   | 0.9928 | 0.9915         |
| 0.4136        | 6.0   | 5880  | 0.3970          | 0.9853   | 0.9891 | 0.9872         |
| 0.4131        | 7.0   | 6860  | 0.3970          | 0.9926   | 0.9946 | 0.9936         |
| 0.4127        | 8.0   | 7840  | 0.3968          | 1.0      | 1.0    | 1.0            |
| 0.4123        | 9.0   | 8820  | 0.3970          | 1.0      | 1.0    | 1.0            |
| 0.412         | 10.0  | 9800  | 0.3969          | 1.0      | 1.0    | 1.0            |
| 0.4119        | 11.0  | 10780 | 0.3969          | 0.9926   | 0.9946 | 0.9936         |
| 0.4116        | 12.0  | 11760 | 0.3968          | 0.9902   | 0.9928 | 0.9915         |
| 0.4116        | 13.0  | 12740 | 0.3968          | 0.9951   | 0.9964 | 0.9958         |


### 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_96", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}, {"type": "f1", "value": 1.0, "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\_96
================================================================


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: 0.3968
* Accuracy: 1.0
* F1: 1.0
* Combined Score: 1.0


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