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# distilbert_sa_GLUE_Experiment_logit_kd_qqp_384

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: 0.6771
- Accuracy: 0.6454
- F1: 0.0788
- Combined Score: 0.3621

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.7984        | 1.0   | 1422  | 0.7600          | 0.6318   | 0.0    | 0.3159         |
| 0.7388        | 2.0   | 2844  | 0.7348          | 0.6318   | 0.0    | 0.3159         |
| 0.7037        | 3.0   | 4266  | 0.7082          | 0.6329   | 0.0056 | 0.3192         |
| 0.6717        | 4.0   | 5688  | 0.7014          | 0.6474   | 0.0908 | 0.3691         |
| 0.6462        | 5.0   | 7110  | 0.6841          | 0.6377   | 0.0339 | 0.3358         |
| 0.6259        | 6.0   | 8532  | 0.6795          | 0.6382   | 0.0364 | 0.3373         |
| 0.6092        | 7.0   | 9954  | 0.6782          | 0.6408   | 0.0513 | 0.3461         |
| 0.5941        | 8.0   | 11376 | 0.6771          | 0.6454   | 0.0788 | 0.3621         |
| 0.5812        | 9.0   | 12798 | 0.6841          | 0.6492   | 0.0991 | 0.3741         |
| 0.5703        | 10.0  | 14220 | 0.6774          | 0.6452   | 0.0776 | 0.3614         |
| 0.5604        | 11.0  | 15642 | 0.6791          | 0.6464   | 0.0831 | 0.3647         |
| 0.5523        | 12.0  | 17064 | 0.6817          | 0.6520   | 0.1143 | 0.3831         |
| 0.5448        | 13.0  | 18486 | 0.6774          | 0.6477   | 0.0905 | 0.3691         |


### 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_384", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "config": "qqp", "split": "validation", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.6454365570121197, "name": "Accuracy"}, {"type": "f1", "value": 0.07878671036565774, "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\_384
=====================================================


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: 0.6771
* Accuracy: 0.6454
* F1: 0.0788
* Combined Score: 0.3621


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