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# hBERTv1_stsb

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1154
- Pearson: 0.7159
- Spearmanr: 0.7156
- Combined Score: 0.7157

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 4.0796        | 1.0   | 23   | 2.3017          | 0.0761  | 0.0547    | 0.0654         |
| 2.0746        | 2.0   | 46   | 2.6181          | 0.0850  | 0.0772    | 0.0811         |
| 1.9142        | 3.0   | 69   | 2.2963          | 0.1878  | 0.1852    | 0.1865         |
| 1.6883        | 4.0   | 92   | 2.1866          | 0.4740  | 0.4777    | 0.4759         |
| 1.1166        | 5.0   | 115  | 1.9367          | 0.6319  | 0.6450    | 0.6384         |
| 0.7598        | 6.0   | 138  | 1.4188          | 0.6801  | 0.6888    | 0.6845         |
| 0.5453        | 7.0   | 161  | 1.2720          | 0.6988  | 0.7001    | 0.6994         |
| 0.3705        | 8.0   | 184  | 1.1154          | 0.7159  | 0.7156    | 0.7157         |
| 0.2976        | 9.0   | 207  | 1.6889          | 0.6754  | 0.6807    | 0.6780         |
| 0.2272        | 10.0  | 230  | 1.3627          | 0.6929  | 0.6899    | 0.6914         |
| 0.1966        | 11.0  | 253  | 1.1278          | 0.7195  | 0.7167    | 0.7181         |
| 0.1708        | 12.0  | 276  | 1.3476          | 0.7171  | 0.7165    | 0.7168         |
| 0.1529        | 13.0  | 299  | 1.2614          | 0.6982  | 0.6942    | 0.6962         |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2

{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "hBERTv1_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": 0.7155715863961268, "name": "Spearmanr"}]}]}]}
transformers pytorch tensorboard hybridbert text-classification generated_from_trainer en dataset:glue model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hybridbert #text-classification #generated_from_trainer #en #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us 

hBERTv1\_stsb
=============


This model is a fine-tuned version of gokuls/bert\_12\_layer\_model\_v1 on the GLUE STSB dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1154
* Pearson: 0.7159
* Spearmanr: 0.7156
* Combined Score: 0.7157


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 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.1
* 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 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #hybridbert #text-classification #generated_from_trainer #en #dataset-glue #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 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #hybridbert #text-classification #generated_from_trainer #en #dataset-glue #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 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.1
* Tokenizers 0.13.2