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# distilbert_sa_GLUE_Experiment_data_aug_mrpc_256

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: 0.0
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2052        | 1.0   | 980   | 0.0476          | 0.9853   | 0.9894 | 0.9873         |
| 0.0409        | 2.0   | 1960  | 0.0031          | 1.0      | 1.0    | 1.0            |
| 0.0211        | 3.0   | 2940  | 0.0006          | 1.0      | 1.0    | 1.0            |
| 0.0131        | 4.0   | 3920  | 0.0005          | 1.0      | 1.0    | 1.0            |
| 0.0078        | 5.0   | 4900  | 0.0001          | 1.0      | 1.0    | 1.0            |
| 0.0058        | 6.0   | 5880  | 0.0002          | 1.0      | 1.0    | 1.0            |
| 0.0041        | 7.0   | 6860  | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0035        | 8.0   | 7840  | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0029        | 9.0   | 8820  | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0022        | 10.0  | 9800  | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0021        | 11.0  | 10780 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0015        | 12.0  | 11760 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0017        | 13.0  | 12740 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0011        | 14.0  | 13720 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0013        | 15.0  | 14700 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0011        | 16.0  | 15680 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0009        | 17.0  | 16660 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0008        | 18.0  | 17640 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0008        | 19.0  | 18620 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0007        | 20.0  | 19600 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0006        | 21.0  | 20580 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0007        | 22.0  | 21560 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0005        | 23.0  | 22540 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0003        | 24.0  | 23520 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0003        | 25.0  | 24500 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0003        | 26.0  | 25480 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0003        | 27.0  | 26460 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0003        | 28.0  | 27440 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0002        | 29.0  | 28420 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0002        | 30.0  | 29400 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0002        | 31.0  | 30380 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0002        | 32.0  | 31360 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0001        | 33.0  | 32340 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0001        | 34.0  | 33320 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0002        | 35.0  | 34300 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0001        | 36.0  | 35280 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0           | 37.0  | 36260 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0           | 38.0  | 37240 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0           | 39.0  | 38220 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0001        | 40.0  | 39200 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0           | 41.0  | 40180 | 0.0             | 1.0      | 1.0    | 1.0            |
| 0.0           | 42.0  | 41160 | 0.0             | 1.0      | 1.0    | 1.0            |
| 0.0001        | 43.0  | 42140 | 0.0000          | 1.0      | 1.0    | 1.0            |
| 0.0           | 44.0  | 43120 | 0.0             | 1.0      | 1.0    | 1.0            |
| 0.0           | 45.0  | 44100 | 0.0             | 1.0      | 1.0    | 1.0            |
| 0.0           | 46.0  | 45080 | 0.0             | 1.0      | 1.0    | 1.0            |


### 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_data_aug_mrpc_256", "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\_data\_aug\_mrpc\_256
======================================================


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: 0.0
* 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