
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert_sa_GLUE_Experiment_logit_kd_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: 0.7423
- Accuracy: 0.6329
- F1: 0.0062
- Combined Score: 0.3195

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.8963        | 1.0   | 1422  | 0.7832          | 0.6318   | 0.0    | 0.3159         |
| 0.7734        | 2.0   | 2844  | 0.7741          | 0.6318   | 0.0    | 0.3159         |
| 0.7598        | 3.0   | 4266  | 0.7727          | 0.6318   | 0.0    | 0.3159         |
| 0.7474        | 4.0   | 5688  | 0.7675          | 0.6318   | 0.0    | 0.3159         |
| 0.7366        | 5.0   | 7110  | 0.7626          | 0.6318   | 0.0    | 0.3159         |
| 0.7272        | 6.0   | 8532  | 0.7568          | 0.6318   | 0.0    | 0.3159         |
| 0.7177        | 7.0   | 9954  | 0.7539          | 0.6318   | 0.0    | 0.3159         |
| 0.7084        | 8.0   | 11376 | 0.7500          | 0.6318   | 0.0    | 0.3159         |
| 0.6998        | 9.0   | 12798 | 0.7543          | 0.6318   | 0.0    | 0.3159         |
| 0.692         | 10.0  | 14220 | 0.7469          | 0.6318   | 0.0    | 0.3159         |
| 0.6846        | 11.0  | 15642 | 0.7481          | 0.6318   | 0.0    | 0.3159         |
| 0.6774        | 12.0  | 17064 | 0.7486          | 0.6318   | 0.0    | 0.3159         |
| 0.6705        | 13.0  | 18486 | 0.7440          | 0.6318   | 0.0    | 0.3159         |
| 0.6648        | 14.0  | 19908 | 0.7464          | 0.6318   | 0.0    | 0.3159         |
| 0.659         | 15.0  | 21330 | 0.7430          | 0.6318   | 0.0    | 0.3159         |
| 0.6531        | 16.0  | 22752 | 0.7423          | 0.6329   | 0.0062 | 0.3195         |
| 0.6479        | 17.0  | 24174 | 0.7452          | 0.6321   | 0.0016 | 0.3169         |
| 0.643         | 18.0  | 25596 | 0.7443          | 0.6354   | 0.0214 | 0.3284         |
| 0.6387        | 19.0  | 27018 | 0.7431          | 0.6335   | 0.0092 | 0.3213         |
| 0.6343        | 20.0  | 28440 | 0.7436          | 0.6370   | 0.0318 | 0.3344         |
| 0.6297        | 21.0  | 29862 | 0.7444          | 0.6362   | 0.0266 | 0.3314         |


### 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_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": 0.6329210981944101, "name": "Accuracy"}, {"type": "f1", "value": 0.006160851804727785, "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\_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: 0.7423
* Accuracy: 0.6329
* F1: 0.0062
* Combined Score: 0.3195


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