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# distilbert_add_GLUE_Experiment_qqp_96

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: 0.4726
- Accuracy: 0.7906
- F1: 0.7104
- Combined Score: 0.7505

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5993        | 1.0   | 1422  | 0.5459          | 0.7243   | 0.6353 | 0.6798         |
| 0.5167        | 2.0   | 2844  | 0.5176          | 0.7471   | 0.6481 | 0.6976         |
| 0.4956        | 3.0   | 4266  | 0.5036          | 0.7588   | 0.6463 | 0.7025         |
| 0.4849        | 4.0   | 5688  | 0.5056          | 0.7546   | 0.6610 | 0.7078         |
| 0.4762        | 5.0   | 7110  | 0.5127          | 0.7530   | 0.6705 | 0.7118         |
| 0.4689        | 6.0   | 8532  | 0.5218          | 0.7476   | 0.6754 | 0.7115         |
| 0.4622        | 7.0   | 9954  | 0.4935          | 0.7661   | 0.6571 | 0.7116         |
| 0.4554        | 8.0   | 11376 | 0.5039          | 0.7605   | 0.6537 | 0.7071         |
| 0.4483        | 9.0   | 12798 | 0.5009          | 0.7625   | 0.6732 | 0.7178         |
| 0.4393        | 10.0  | 14220 | 0.4991          | 0.7594   | 0.6857 | 0.7226         |
| 0.4293        | 11.0  | 15642 | 0.4857          | 0.7761   | 0.6548 | 0.7155         |
| 0.4162        | 12.0  | 17064 | 0.4897          | 0.7735   | 0.6935 | 0.7335         |
| 0.4021        | 13.0  | 18486 | 0.4758          | 0.7822   | 0.6881 | 0.7352         |
| 0.3871        | 14.0  | 19908 | 0.4801          | 0.7815   | 0.7050 | 0.7433         |
| 0.3714        | 15.0  | 21330 | 0.4846          | 0.7827   | 0.7111 | 0.7469         |
| 0.3556        | 16.0  | 22752 | 0.4726          | 0.7906   | 0.7104 | 0.7505         |
| 0.341         | 17.0  | 24174 | 0.4787          | 0.7942   | 0.7047 | 0.7494         |
| 0.3269        | 18.0  | 25596 | 0.4914          | 0.7884   | 0.7198 | 0.7541         |
| 0.3127        | 19.0  | 27018 | 0.4774          | 0.7950   | 0.7156 | 0.7553         |
| 0.3           | 20.0  | 28440 | 0.4862          | 0.7965   | 0.7253 | 0.7609         |
| 0.2885        | 21.0  | 29862 | 0.4982          | 0.7939   | 0.7300 | 0.7620         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- 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_qqp_96", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "config": "qqp", "split": "validation", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.7905763047242147, "name": "Accuracy"}, {"type": "f1", "value": 0.7103813921669232, "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\_qqp\_96
==========================================


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: 0.4726
* Accuracy: 0.7906
* F1: 0.7104
* Combined Score: 0.7505


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 2.8.0
* 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 2.8.0
* 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 2.8.0
* 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 2.8.0
* Tokenizers 0.13.2