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# distilbert_add_GLUE_Experiment_logit_kd_qqp_256

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6586
- Accuracy: 0.6554
- F1: 0.1310
- Combined Score: 0.3932

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.8355        | 1.0   | 1422  | 0.8004          | 0.6318   | 0.0    | 0.3159         |
| 0.7677        | 2.0   | 2844  | 0.7488          | 0.6318   | 0.0    | 0.3159         |
| 0.7048        | 3.0   | 4266  | 0.6935          | 0.6318   | 0.0    | 0.3159         |
| 0.6449        | 4.0   | 5688  | 0.6875          | 0.6337   | 0.0106 | 0.3221         |
| 0.6082        | 5.0   | 7110  | 0.6688          | 0.6354   | 0.0205 | 0.3279         |
| 0.5829        | 6.0   | 8532  | 0.6854          | 0.6386   | 0.0393 | 0.3389         |
| 0.5637        | 7.0   | 9954  | 0.6707          | 0.6522   | 0.1155 | 0.3839         |
| 0.5502        | 8.0   | 11376 | 0.6752          | 0.6522   | 0.1145 | 0.3833         |
| 0.5389        | 9.0   | 12798 | 0.6677          | 0.6561   | 0.1348 | 0.3954         |
| 0.5304        | 10.0  | 14220 | 0.6693          | 0.6622   | 0.1659 | 0.4140         |
| 0.5234        | 11.0  | 15642 | 0.6728          | 0.6511   | 0.1082 | 0.3797         |
| 0.5175        | 12.0  | 17064 | 0.6812          | 0.6554   | 0.1303 | 0.3928         |
| 0.5127        | 13.0  | 18486 | 0.6644          | 0.6540   | 0.1235 | 0.3888         |
| 0.5085        | 14.0  | 19908 | 0.6605          | 0.6622   | 0.1677 | 0.4149         |
| 0.505         | 15.0  | 21330 | 0.6647          | 0.6570   | 0.1391 | 0.3980         |
| 0.502         | 16.0  | 22752 | 0.6667          | 0.6528   | 0.1170 | 0.3849         |
| 0.499         | 17.0  | 24174 | 0.6586          | 0.6554   | 0.1310 | 0.3932         |
| 0.497         | 18.0  | 25596 | 0.6589          | 0.6597   | 0.1535 | 0.4066         |
| 0.4947        | 19.0  | 27018 | 0.6715          | 0.6599   | 0.1535 | 0.4067         |
| 0.4928        | 20.0  | 28440 | 0.6631          | 0.6535   | 0.1202 | 0.3868         |
| 0.4907        | 21.0  | 29862 | 0.6690          | 0.6651   | 0.1796 | 0.4224         |
| 0.4891        | 22.0  | 31284 | 0.6603          | 0.6652   | 0.1830 | 0.4241         |


### 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_add_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": 0.6553549344546129, "name": "Accuracy"}, {"type": "f1", "value": 0.13097168516901583, "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\_add\_GLUE\_Experiment\_logit\_kd\_qqp\_256
======================================================


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


* Loss: 0.6586
* Accuracy: 0.6554
* F1: 0.1310
* Combined Score: 0.3932


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