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# hBERTv2_stsb

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2](https://huggingface.co/gokuls/bert_12_layer_model_v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9534
- Pearson: 0.7722
- Spearmanr: 0.7707
- Combined Score: 0.7714

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 4.4386        | 1.0   | 23   | 2.5331          | 0.1313  | 0.1071    | 0.1192         |
| 1.8741        | 2.0   | 46   | 2.0517          | 0.4923  | 0.4766    | 0.4844         |
| 1.347         | 3.0   | 69   | 1.3556          | 0.6964  | 0.7079    | 0.7022         |
| 0.8443        | 4.0   | 92   | 1.2583          | 0.7340  | 0.7367    | 0.7353         |
| 0.5822        | 5.0   | 115  | 0.9534          | 0.7722  | 0.7707    | 0.7714         |
| 0.4356        | 6.0   | 138  | 1.1921          | 0.7798  | 0.7771    | 0.7785         |
| 0.3531        | 7.0   | 161  | 1.3849          | 0.7701  | 0.7700    | 0.7700         |
| 0.2712        | 8.0   | 184  | 1.0015          | 0.7886  | 0.7870    | 0.7878         |
| 0.259         | 9.0   | 207  | 1.0523          | 0.7898  | 0.7874    | 0.7886         |
| 0.2003        | 10.0  | 230  | 1.1525          | 0.7836  | 0.7824    | 0.7830         |


### 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": "hBERTv2_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": 0.7706783096515127, "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 

hBERTv2\_stsb
=============


This model is a fine-tuned version of gokuls/bert\_12\_layer\_model\_v2 on the GLUE STSB dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9534
* Pearson: 0.7722
* Spearmanr: 0.7707
* Combined Score: 0.7714


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