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# tiny-mlm-glue-qqp-target-glue-qqp

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qqp](https://huggingface.co/muhtasham/tiny-mlm-glue-qqp) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3945
- Accuracy: 0.8081
- F1: 0.7763

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5728        | 0.04  | 500   | 0.5077          | 0.7372   | 0.6832 |
| 0.5018        | 0.09  | 1000  | 0.4737          | 0.7587   | 0.6993 |
| 0.4915        | 0.13  | 1500  | 0.4739          | 0.7557   | 0.7192 |
| 0.4748        | 0.18  | 2000  | 0.4528          | 0.7665   | 0.7234 |
| 0.4699        | 0.22  | 2500  | 0.4430          | 0.7738   | 0.7303 |
| 0.4528        | 0.26  | 3000  | 0.4336          | 0.7811   | 0.7379 |
| 0.4548        | 0.31  | 3500  | 0.4330          | 0.7785   | 0.7392 |
| 0.4466        | 0.35  | 4000  | 0.4314          | 0.7798   | 0.7431 |
| 0.4463        | 0.4   | 4500  | 0.4276          | 0.7846   | 0.7489 |
| 0.4379        | 0.44  | 5000  | 0.4106          | 0.7971   | 0.7493 |
| 0.4262        | 0.48  | 5500  | 0.4159          | 0.7930   | 0.7550 |
| 0.4272        | 0.53  | 6000  | 0.3997          | 0.8068   | 0.7579 |
| 0.4215        | 0.57  | 6500  | 0.4072          | 0.7995   | 0.7590 |
| 0.4234        | 0.62  | 7000  | 0.3957          | 0.8095   | 0.7603 |
| 0.4147        | 0.66  | 7500  | 0.3968          | 0.8102   | 0.7639 |
| 0.4293        | 0.7   | 8000  | 0.4016          | 0.8045   | 0.7654 |
| 0.4181        | 0.75  | 8500  | 0.3888          | 0.8121   | 0.7667 |
| 0.4121        | 0.79  | 9000  | 0.4029          | 0.8024   | 0.7681 |
| 0.4097        | 0.84  | 9500  | 0.3838          | 0.8165   | 0.7681 |
| 0.4118        | 0.88  | 10000 | 0.3792          | 0.8201   | 0.7702 |
| 0.4075        | 0.92  | 10500 | 0.3780          | 0.8199   | 0.7717 |
| 0.4058        | 0.97  | 11000 | 0.3770          | 0.8226   | 0.7712 |
| 0.4037        | 1.01  | 11500 | 0.3891          | 0.8131   | 0.7762 |
| 0.3828        | 1.06  | 12000 | 0.3746          | 0.8241   | 0.7738 |
| 0.3936        | 1.1   | 12500 | 0.3770          | 0.8211   | 0.7801 |
| 0.3823        | 1.14  | 13000 | 0.3761          | 0.8213   | 0.7782 |
| 0.371         | 1.19  | 13500 | 0.3945          | 0.8081   | 0.7763 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "tiny-mlm-glue-qqp-target-glue-qqp", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

tiny-mlm-glue-qqp-target-glue-qqp
=================================


This model is a fine-tuned version of muhtasham/tiny-mlm-glue-qqp on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3945
* Accuracy: 0.8081
* F1: 0.7763


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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 200### Training results### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2