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

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-stsb](https://huggingface.co/muhtasham/tiny-mlm-glue-stsb) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3868
- Accuracy: 0.8128
- F1: 0.7756

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5792        | 0.04  | 500   | 0.5164          | 0.7290   | 0.6761 |
| 0.5106        | 0.09  | 1000  | 0.4848          | 0.7490   | 0.7008 |
| 0.4974        | 0.13  | 1500  | 0.4907          | 0.7414   | 0.7142 |
| 0.4818        | 0.18  | 2000  | 0.4670          | 0.7555   | 0.7216 |
| 0.4734        | 0.22  | 2500  | 0.4576          | 0.7624   | 0.7274 |
| 0.4564        | 0.26  | 3000  | 0.4486          | 0.7675   | 0.7329 |
| 0.4578        | 0.31  | 3500  | 0.4455          | 0.7688   | 0.7361 |
| 0.451         | 0.35  | 4000  | 0.4490          | 0.7672   | 0.7383 |
| 0.4511        | 0.4   | 4500  | 0.4401          | 0.7720   | 0.7426 |
| 0.4419        | 0.44  | 5000  | 0.4201          | 0.7888   | 0.7491 |
| 0.4308        | 0.48  | 5500  | 0.4266          | 0.7843   | 0.7507 |
| 0.4308        | 0.53  | 6000  | 0.4077          | 0.8005   | 0.7566 |
| 0.4249        | 0.57  | 6500  | 0.4182          | 0.7913   | 0.7558 |
| 0.4256        | 0.62  | 7000  | 0.4051          | 0.8008   | 0.7582 |
| 0.4179        | 0.66  | 7500  | 0.4064          | 0.8037   | 0.7630 |
| 0.4314        | 0.7   | 8000  | 0.4105          | 0.7963   | 0.7607 |
| 0.4202        | 0.75  | 8500  | 0.3970          | 0.8064   | 0.7656 |
| 0.4152        | 0.79  | 9000  | 0.4204          | 0.7894   | 0.7615 |
| 0.4121        | 0.84  | 9500  | 0.3908          | 0.8099   | 0.7667 |
| 0.4135        | 0.88  | 10000 | 0.3863          | 0.8151   | 0.7707 |
| 0.4091        | 0.92  | 10500 | 0.3839          | 0.8149   | 0.7702 |
| 0.4084        | 0.97  | 11000 | 0.3837          | 0.8179   | 0.7719 |
| 0.4068        | 1.01  | 11500 | 0.3990          | 0.8048   | 0.7712 |
| 0.3861        | 1.06  | 12000 | 0.3821          | 0.8173   | 0.7743 |
| 0.3955        | 1.1   | 12500 | 0.3868          | 0.8128   | 0.7756 |


### 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-stsb-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-stsb-target-glue-qqp
==================================


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


* Loss: 0.3868
* Accuracy: 0.8128
* F1: 0.7756


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