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

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4125
- Accuracy: 0.7971
- F1: 0.7707

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5776        | 0.04  | 500   | 0.5177          | 0.7272   | 0.6831 |
| 0.5088        | 0.09  | 1000  | 0.4828          | 0.7515   | 0.7055 |
| 0.4952        | 0.13  | 1500  | 0.4939          | 0.7383   | 0.7143 |
| 0.4797        | 0.18  | 2000  | 0.4681          | 0.7547   | 0.7225 |
| 0.4723        | 0.22  | 2500  | 0.4564          | 0.7621   | 0.7274 |
| 0.4551        | 0.26  | 3000  | 0.4475          | 0.7693   | 0.7351 |
| 0.4573        | 0.31  | 3500  | 0.4479          | 0.7676   | 0.7372 |
| 0.4496        | 0.35  | 4000  | 0.4483          | 0.7668   | 0.7390 |
| 0.4503        | 0.4   | 4500  | 0.4413          | 0.7720   | 0.7436 |
| 0.4407        | 0.44  | 5000  | 0.4192          | 0.7899   | 0.7498 |
| 0.4288        | 0.48  | 5500  | 0.4261          | 0.7845   | 0.7512 |
| 0.4292        | 0.53  | 6000  | 0.4058          | 0.8022   | 0.7581 |
| 0.4235        | 0.57  | 6500  | 0.4201          | 0.7893   | 0.7560 |
| 0.4251        | 0.62  | 7000  | 0.4050          | 0.8007   | 0.7593 |
| 0.4161        | 0.66  | 7500  | 0.4063          | 0.8040   | 0.7652 |
| 0.4297        | 0.7   | 8000  | 0.4116          | 0.7959   | 0.7617 |
| 0.4201        | 0.75  | 8500  | 0.3975          | 0.8069   | 0.7677 |
| 0.4142        | 0.79  | 9000  | 0.4186          | 0.7889   | 0.7609 |
| 0.4113        | 0.84  | 9500  | 0.3900          | 0.8112   | 0.7687 |
| 0.413         | 0.88  | 10000 | 0.3852          | 0.8161   | 0.7732 |
| 0.4084        | 0.92  | 10500 | 0.3826          | 0.8161   | 0.7714 |
| 0.4083        | 0.97  | 11000 | 0.3826          | 0.8187   | 0.7733 |
| 0.4057        | 1.01  | 11500 | 0.4016          | 0.8029   | 0.7711 |
| 0.3846        | 1.06  | 12000 | 0.3803          | 0.8187   | 0.7759 |
| 0.3949        | 1.1   | 12500 | 0.3827          | 0.8154   | 0.7773 |
| 0.3823        | 1.14  | 13000 | 0.3878          | 0.8136   | 0.7763 |
| 0.3717        | 1.19  | 13500 | 0.4125          | 0.7971   | 0.7707 |


### 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-qnli-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-qnli-target-glue-qqp
==================================


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


* Loss: 0.4125
* Accuracy: 0.7971
* F1: 0.7707


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