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# sa_BERT_no_pretrain_mrpc

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6003
- Accuracy: 0.6814
- F1: 0.7782
- Combined Score: 0.7298

## 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: 96
- eval_batch_size: 96
- 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 | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6845        | 1.0   | 39   | 0.6307          | 0.6838   | 0.8122 | 0.7480         |
| 0.6398        | 2.0   | 78   | 0.6313          | 0.6838   | 0.8122 | 0.7480         |
| 0.6384        | 3.0   | 117  | 0.6247          | 0.6838   | 0.8122 | 0.7480         |
| 0.6428        | 4.0   | 156  | 0.6467          | 0.6667   | 0.7806 | 0.7237         |
| 0.6021        | 5.0   | 195  | 0.6003          | 0.6814   | 0.7782 | 0.7298         |
| 0.5125        | 6.0   | 234  | 0.6875          | 0.6863   | 0.7874 | 0.7368         |
| 0.3735        | 7.0   | 273  | 0.8672          | 0.6422   | 0.7355 | 0.6888         |
| 0.2662        | 8.0   | 312  | 0.9928          | 0.6765   | 0.7857 | 0.7311         |
| 0.2247        | 9.0   | 351  | 0.9605          | 0.6789   | 0.7798 | 0.7294         |
| 0.1655        | 10.0  | 390  | 1.0684          | 0.6275   | 0.7206 | 0.6740         |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sa_BERT_no_pretrain_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": 0.6813725490196079, "name": "Accuracy"}, {"type": "f1", "value": 0.7781569965870307, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

sa\_BERT\_no\_pretrain\_mrpc
============================


This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6003
* Accuracy: 0.6814
* F1: 0.7782
* Combined Score: 0.7298


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: 96
* eval\_batch\_size: 96
* 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 4.30.2
* 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: 96
* eval\_batch\_size: 96
* 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 4.30.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #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: 4e-05
* train\_batch\_size: 96
* eval\_batch\_size: 96
* 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 4.30.2
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3