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

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4097
- Accuracy: 0.7989
- F1: 0.7715

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5775        | 0.04  | 500   | 0.5143          | 0.7309   | 0.6804 |
| 0.5085        | 0.09  | 1000  | 0.4819          | 0.7521   | 0.7031 |
| 0.4956        | 0.13  | 1500  | 0.4903          | 0.7412   | 0.7150 |
| 0.4795        | 0.18  | 2000  | 0.4662          | 0.7565   | 0.7230 |
| 0.4723        | 0.22  | 2500  | 0.4550          | 0.7643   | 0.7288 |
| 0.4562        | 0.26  | 3000  | 0.4472          | 0.7700   | 0.7353 |
| 0.4568        | 0.31  | 3500  | 0.4453          | 0.7695   | 0.7378 |
| 0.4502        | 0.35  | 4000  | 0.4475          | 0.7680   | 0.7398 |
| 0.4504        | 0.4   | 4500  | 0.4412          | 0.7721   | 0.7435 |
| 0.4406        | 0.44  | 5000  | 0.4203          | 0.7893   | 0.7497 |
| 0.4289        | 0.48  | 5500  | 0.4252          | 0.7859   | 0.7521 |
| 0.429         | 0.53  | 6000  | 0.4063          | 0.8017   | 0.7584 |
| 0.4237        | 0.57  | 6500  | 0.4189          | 0.7908   | 0.7566 |
| 0.4257        | 0.62  | 7000  | 0.4035          | 0.8032   | 0.7603 |
| 0.4169        | 0.66  | 7500  | 0.4044          | 0.8059   | 0.7650 |
| 0.4306        | 0.7   | 8000  | 0.4104          | 0.7969   | 0.7617 |
| 0.4199        | 0.75  | 8500  | 0.3954          | 0.8086   | 0.7675 |
| 0.414         | 0.79  | 9000  | 0.4181          | 0.7907   | 0.7625 |
| 0.4117        | 0.84  | 9500  | 0.3887          | 0.8118   | 0.7679 |
| 0.4137        | 0.88  | 10000 | 0.3852          | 0.8166   | 0.7721 |
| 0.4087        | 0.92  | 10500 | 0.3832          | 0.8158   | 0.7709 |
| 0.4082        | 0.97  | 11000 | 0.3831          | 0.8190   | 0.7734 |
| 0.4052        | 1.01  | 11500 | 0.4005          | 0.8039   | 0.7715 |
| 0.3844        | 1.06  | 12000 | 0.3799          | 0.8193   | 0.7751 |
| 0.3951        | 1.1   | 12500 | 0.3833          | 0.8154   | 0.7771 |
| 0.3825        | 1.14  | 13000 | 0.3874          | 0.8155   | 0.7775 |
| 0.3721        | 1.19  | 13500 | 0.4097          | 0.7989   | 0.7715 |


### 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-mnli-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-mnli-target-glue-qqp
==================================


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


* Loss: 0.4097
* Accuracy: 0.7989
* F1: 0.7715


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