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# distilbert_sa_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: 0.6955
- Accuracy: 0.6386
- F1: 0.0394
- Combined Score: 0.3390

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.8148        | 1.0   | 1422  | 0.7743          | 0.6318   | 0.0    | 0.3159         |
| 0.7531        | 2.0   | 2844  | 0.7520          | 0.6318   | 0.0    | 0.3159         |
| 0.7248        | 3.0   | 4266  | 0.7365          | 0.6318   | 0.0    | 0.3159         |
| 0.6964        | 4.0   | 5688  | 0.7183          | 0.6444   | 0.0751 | 0.3598         |
| 0.676         | 5.0   | 7110  | 0.7078          | 0.6346   | 0.0165 | 0.3256         |
| 0.6579        | 6.0   | 8532  | 0.7030          | 0.6359   | 0.0241 | 0.3300         |
| 0.6427        | 7.0   | 9954  | 0.7007          | 0.6332   | 0.0078 | 0.3205         |
| 0.6291        | 8.0   | 11376 | 0.7044          | 0.6389   | 0.0415 | 0.3402         |
| 0.6173        | 9.0   | 12798 | 0.7002          | 0.6407   | 0.0522 | 0.3465         |
| 0.6061        | 10.0  | 14220 | 0.6971          | 0.6406   | 0.0526 | 0.3466         |
| 0.5967        | 11.0  | 15642 | 0.6955          | 0.6386   | 0.0394 | 0.3390         |
| 0.5871        | 12.0  | 17064 | 0.7006          | 0.6470   | 0.0874 | 0.3672         |
| 0.5792        | 13.0  | 18486 | 0.6957          | 0.6426   | 0.0625 | 0.3526         |
| 0.5722        | 14.0  | 19908 | 0.7026          | 0.6390   | 0.0415 | 0.3402         |
| 0.5658        | 15.0  | 21330 | 0.6983          | 0.6432   | 0.0658 | 0.3545         |
| 0.5597        | 16.0  | 22752 | 0.7013          | 0.6405   | 0.0503 | 0.3454         |


### 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_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": 0.6386099431115508, "name": "Accuracy"}, {"type": "f1", "value": 0.039445138386693834, "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\_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: 0.6955
* Accuracy: 0.6386
* F1: 0.0394
* Combined Score: 0.3390


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