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# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_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: 0.7029
- Accuracy: 0.6540
- F1: 0.1254
- Combined Score: 0.3897

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
| 0.8495        | 1.0   | 29671  | 0.7150          | 0.6333   | 0.0086 | 0.3210         |
| 0.7654        | 2.0   | 59342  | 0.7273          | 0.6339   | 0.0121 | 0.3230         |
| 0.7305        | 3.0   | 89013  | 0.7241          | 0.6400   | 0.0479 | 0.3440         |
| 0.7108        | 4.0   | 118684 | 0.7147          | 0.6381   | 0.0380 | 0.3381         |
| 0.698         | 5.0   | 148355 | 0.7192          | 0.6414   | 0.0564 | 0.3489         |
| 0.6891        | 6.0   | 178026 | 0.7239          | 0.6357   | 0.0232 | 0.3295         |
| 0.6823        | 7.0   | 207697 | 0.7141          | 0.6442   | 0.0723 | 0.3583         |
| 0.6771        | 8.0   | 237368 | 0.7112          | 0.6491   | 0.1004 | 0.3748         |
| 0.6729        | 9.0   | 267039 | 0.7156          | 0.6494   | 0.1022 | 0.3758         |
| 0.6694        | 10.0  | 296710 | 0.7185          | 0.6502   | 0.1053 | 0.3777         |
| 0.6664        | 11.0  | 326381 | 0.7129          | 0.6508   | 0.1085 | 0.3796         |
| 0.6639        | 12.0  | 356052 | 0.7112          | 0.6508   | 0.1080 | 0.3794         |
| 0.6617        | 13.0  | 385723 | 0.7105          | 0.6542   | 0.1260 | 0.3901         |
| 0.6597        | 14.0  | 415394 | 0.7029          | 0.6540   | 0.1254 | 0.3897         |
| 0.658         | 15.0  | 445065 | 0.7094          | 0.6486   | 0.0964 | 0.3725         |
| 0.6564        | 16.0  | 474736 | 0.7072          | 0.6510   | 0.1084 | 0.3797         |
| 0.655         | 17.0  | 504407 | 0.7049          | 0.6557   | 0.1333 | 0.3945         |
| 0.6537        | 18.0  | 534078 | 0.7051          | 0.6542   | 0.1269 | 0.3905         |
| 0.6526        | 19.0  | 563749 | 0.7096          | 0.6601   | 0.1573 | 0.4087         |


### 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_data_aug_qqp_192", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.6539698243878308, "name": "Accuracy"}, {"type": "f1", "value": 0.12540635158789698, "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\_data\_aug\_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: 0.7029
* Accuracy: 0.6540
* F1: 0.1254
* Combined Score: 0.3897


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