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# hBERTv1_mrpc

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 MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6051
- Accuracy: 0.6863
- F1: 0.8000
- Combined Score: 0.7431

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6536        | 1.0   | 15   | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
| 0.6275        | 2.0   | 30   | 0.6174          | 0.7010   | 0.8117 | 0.7564         |
| 0.6129        | 3.0   | 45   | 0.6089          | 0.6961   | 0.8182 | 0.7571         |
| 0.6087        | 4.0   | 60   | 0.6062          | 0.6887   | 0.8130 | 0.7508         |
| 0.5939        | 5.0   | 75   | 0.6104          | 0.6863   | 0.7935 | 0.7399         |
| 0.5707        | 6.0   | 90   | 0.6184          | 0.7083   | 0.8183 | 0.7633         |
| 0.5426        | 7.0   | 105  | 0.6051          | 0.6863   | 0.8000 | 0.7431         |
| 0.4819        | 8.0   | 120  | 0.6560          | 0.6936   | 0.8019 | 0.7478         |
| 0.4279        | 9.0   | 135  | 0.6673          | 0.6887   | 0.7678 | 0.7283         |
| 0.3374        | 10.0  | 150  | 0.8092          | 0.6863   | 0.7902 | 0.7382         |
| 0.2789        | 11.0  | 165  | 0.9342          | 0.6887   | 0.7935 | 0.7411         |
| 0.2216        | 12.0  | 180  | 0.9708          | 0.6838   | 0.7810 | 0.7324         |


### 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": ["accuracy", "f1"], "model-index": [{"name": "hBERTv1_mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "config": "mrpc", "split": "validation", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.6862745098039216, "name": "Accuracy"}, {"type": "f1", "value": 0.7999999999999999, "name": "F1"}]}]}]}
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\_mrpc
=============


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


* Loss: 0.6051
* Accuracy: 0.6863
* F1: 0.8000
* Combined Score: 0.7431


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