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# hBERTv1_new_pretrain_w_init_48_stsb

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9800
- Pearson: 0.7515
- Spearmanr: 0.7472
- Combined Score: 0.7493

## 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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.5456        | 1.0   | 45   | 2.2706          | 0.1246  | 0.1141    | 0.1194         |
| 2.0514        | 2.0   | 90   | 2.0613          | 0.5266  | 0.5198    | 0.5232         |
| 1.3837        | 3.0   | 135  | 1.1984          | 0.6853  | 0.6942    | 0.6897         |
| 1.0297        | 4.0   | 180  | 1.6176          | 0.6869  | 0.6961    | 0.6915         |
| 0.8064        | 5.0   | 225  | 1.1444          | 0.7476  | 0.7445    | 0.7460         |
| 0.604         | 6.0   | 270  | 1.2754          | 0.7422  | 0.7450    | 0.7436         |
| 0.4818        | 7.0   | 315  | 1.1407          | 0.7687  | 0.7673    | 0.7680         |
| 0.3905        | 8.0   | 360  | 1.1860          | 0.7560  | 0.7604    | 0.7582         |
| 0.3476        | 9.0   | 405  | 0.9800          | 0.7515  | 0.7472    | 0.7493         |
| 0.2819        | 10.0  | 450  | 1.0156          | 0.7521  | 0.7507    | 0.7514         |
| 0.2418        | 11.0  | 495  | 1.0174          | 0.7516  | 0.7480    | 0.7498         |
| 0.2068        | 12.0  | 540  | 1.2367          | 0.7530  | 0.7523    | 0.7527         |
| 0.1863        | 13.0  | 585  | 1.0073          | 0.7491  | 0.7468    | 0.7480         |
| 0.1929        | 14.0  | 630  | 1.0470          | 0.7517  | 0.7505    | 0.7511         |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "hBERTv1_new_pretrain_w_init_48_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": 0.7471924680940966, "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\_new\_pretrain\_w\_init\_48\_stsb
=========================================


This model is a fine-tuned version of gokuls/bert\_12\_layer\_model\_v1\_complete\_training\_new\_wt\_init\_48 on the GLUE STSB dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9800
* Pearson: 0.7515
* Spearmanr: 0.7472
* Combined Score: 0.7493


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: 4e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* 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


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* 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 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 4e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* 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 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3