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# distilbert_sa_GLUE_Experiment_logit_kd_pretrain_stsb

This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2956
- Pearson: 0.8628
- Spearmanr: 0.8598
- Combined Score: 0.8613

## 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 | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 1.8052        | 1.0   | 23   | 1.2355          | 0.2355  | 0.2368    | 0.2361         |
| 0.8213        | 2.0   | 46   | 0.5382          | 0.7641  | 0.7680    | 0.7660         |
| 0.4216        | 3.0   | 69   | 0.3781          | 0.8401  | 0.8371    | 0.8386         |
| 0.2818        | 4.0   | 92   | 0.3205          | 0.8486  | 0.8448    | 0.8467         |
| 0.1988        | 5.0   | 115  | 0.3463          | 0.8489  | 0.8498    | 0.8494         |
| 0.1583        | 6.0   | 138  | 0.3100          | 0.8574  | 0.8539    | 0.8557         |
| 0.1249        | 7.0   | 161  | 0.3252          | 0.8556  | 0.8527    | 0.8542         |
| 0.111         | 8.0   | 184  | 0.3495          | 0.8529  | 0.8497    | 0.8513         |
| 0.099         | 9.0   | 207  | 0.2956          | 0.8628  | 0.8598    | 0.8613         |
| 0.0825        | 10.0  | 230  | 0.3060          | 0.8587  | 0.8555    | 0.8571         |
| 0.0682        | 11.0  | 253  | 0.2985          | 0.8584  | 0.8564    | 0.8574         |
| 0.0671        | 12.0  | 276  | 0.3001          | 0.8568  | 0.8538    | 0.8553         |
| 0.0555        | 13.0  | 299  | 0.3107          | 0.8600  | 0.8568    | 0.8584         |
| 0.0575        | 14.0  | 322  | 0.3221          | 0.8592  | 0.8560    | 0.8576         |


### 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": ["spearmanr"], "model-index": [{"name": "distilbert_sa_GLUE_Experiment_logit_kd_pretrain_stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "config": "stsb", "split": "validation", "args": "stsb"}, "metrics": [{"type": "spearmanr", "value": 0.8598187538084905, "name": "Spearmanr"}]}]}]}
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\_pretrain\_stsb
===========================================================


This model is a fine-tuned version of gokuls/distilbert\_sa\_pre-training-complete on the GLUE STSB dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2956
* Pearson: 0.8628
* Spearmanr: 0.8598
* Combined Score: 0.8613


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