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# distilbert_add_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: 0.6704
- Accuracy: 0.6417
- F1: 0.0575
- Combined Score: 0.3496

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.8489        | 1.0   | 1422  | 0.8086          | 0.6318   | 0.0    | 0.3159         |
| 0.776         | 2.0   | 2844  | 0.7797          | 0.6318   | 0.0    | 0.3159         |
| 0.7501        | 3.0   | 4266  | 0.7462          | 0.6318   | 0.0    | 0.3159         |
| 0.7008        | 4.0   | 5688  | 0.7089          | 0.6318   | 0.0    | 0.3159         |
| 0.6547        | 5.0   | 7110  | 0.7026          | 0.6318   | 0.0    | 0.3159         |
| 0.6221        | 6.0   | 8532  | 0.6962          | 0.6338   | 0.0115 | 0.3226         |
| 0.5981        | 7.0   | 9954  | 0.6812          | 0.6437   | 0.0693 | 0.3565         |
| 0.5797        | 8.0   | 11376 | 0.6846          | 0.6361   | 0.0261 | 0.3311         |
| 0.565         | 9.0   | 12798 | 0.6835          | 0.6423   | 0.0609 | 0.3516         |
| 0.5537        | 10.0  | 14220 | 0.6816          | 0.6516   | 0.1130 | 0.3823         |
| 0.5446        | 11.0  | 15642 | 0.6907          | 0.6392   | 0.0427 | 0.3409         |
| 0.5368        | 12.0  | 17064 | 0.6788          | 0.6476   | 0.0921 | 0.3699         |
| 0.5305        | 13.0  | 18486 | 0.6729          | 0.6531   | 0.1216 | 0.3874         |
| 0.525         | 14.0  | 19908 | 0.6704          | 0.6417   | 0.0575 | 0.3496         |
| 0.5206        | 15.0  | 21330 | 0.6757          | 0.6467   | 0.0873 | 0.3670         |
| 0.5165        | 16.0  | 22752 | 0.6805          | 0.6481   | 0.0940 | 0.3711         |
| 0.513         | 17.0  | 24174 | 0.6760          | 0.6474   | 0.0901 | 0.3688         |
| 0.5103        | 18.0  | 25596 | 0.6767          | 0.6505   | 0.1071 | 0.3788         |
| 0.5074        | 19.0  | 27018 | 0.6798          | 0.6486   | 0.0964 | 0.3725         |


### 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_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": 0.6416522384368043, "name": "Accuracy"}, {"type": "f1", "value": 0.057507155867811605, "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\_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: 0.6704
* Accuracy: 0.6417
* F1: 0.0575
* Combined Score: 0.3496


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