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# distilbert_sa_GLUE_Experiment_logit_kd_qqp_192

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: 0.7047
- Accuracy: 0.6401
- F1: 0.0484
- Combined Score: 0.3442

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.824         | 1.0   | 1422  | 0.7761          | 0.6318   | 0.0    | 0.3159         |
| 0.7581        | 2.0   | 2844  | 0.7544          | 0.6318   | 0.0    | 0.3159         |
| 0.7333        | 3.0   | 4266  | 0.7480          | 0.6318   | 0.0    | 0.3159         |
| 0.713         | 4.0   | 5688  | 0.7358          | 0.6318   | 0.0    | 0.3159         |
| 0.695         | 5.0   | 7110  | 0.7279          | 0.6365   | 0.0280 | 0.3323         |
| 0.6783        | 6.0   | 8532  | 0.7193          | 0.6370   | 0.0313 | 0.3341         |
| 0.6642        | 7.0   | 9954  | 0.7148          | 0.6332   | 0.0083 | 0.3208         |
| 0.6509        | 8.0   | 11376 | 0.7138          | 0.6409   | 0.0536 | 0.3472         |
| 0.639         | 9.0   | 12798 | 0.7084          | 0.6385   | 0.0398 | 0.3391         |
| 0.6287        | 10.0  | 14220 | 0.7117          | 0.6418   | 0.0591 | 0.3504         |
| 0.6195        | 11.0  | 15642 | 0.7064          | 0.6379   | 0.0363 | 0.3371         |
| 0.6101        | 12.0  | 17064 | 0.7079          | 0.6444   | 0.0725 | 0.3584         |
| 0.6023        | 13.0  | 18486 | 0.7059          | 0.6495   | 0.1028 | 0.3762         |
| 0.5948        | 14.0  | 19908 | 0.7103          | 0.6418   | 0.0577 | 0.3497         |
| 0.589         | 15.0  | 21330 | 0.7077          | 0.6415   | 0.0567 | 0.3491         |
| 0.5827        | 16.0  | 22752 | 0.7047          | 0.6401   | 0.0484 | 0.3442         |
| 0.5769        | 17.0  | 24174 | 0.7051          | 0.6409   | 0.0525 | 0.3467         |
| 0.572         | 18.0  | 25596 | 0.7090          | 0.6492   | 0.1017 | 0.3754         |
| 0.5673        | 19.0  | 27018 | 0.7078          | 0.6398   | 0.0467 | 0.3433         |
| 0.563         | 20.0  | 28440 | 0.7065          | 0.6427   | 0.0630 | 0.3529         |
| 0.5591        | 21.0  | 29862 | 0.7084          | 0.6464   | 0.0839 | 0.3651         |


### 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_qqp_192", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "config": "qqp", "split": "validation", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.6400692555033392, "name": "Accuracy"}, {"type": "f1", "value": 0.04839131572063824, "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\_qqp\_192
=====================================================


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: 0.7047
* Accuracy: 0.6401
* F1: 0.0484
* Combined Score: 0.3442


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