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# tiny-mlm-glue-qnli-target-glue-mnli

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: 0.7907
- Accuracy: 0.6507

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0753        | 0.04  | 500   | 1.0327          | 0.4677   |
| 1.0084        | 0.08  | 1000  | 0.9655          | 0.5434   |
| 0.962         | 0.12  | 1500  | 0.9232          | 0.5779   |
| 0.9358        | 0.16  | 2000  | 0.9087          | 0.5874   |
| 0.9241        | 0.2   | 2500  | 0.8928          | 0.5963   |
| 0.9157        | 0.24  | 3000  | 0.8772          | 0.5988   |
| 0.8992        | 0.29  | 3500  | 0.8687          | 0.6088   |
| 0.8928        | 0.33  | 4000  | 0.8571          | 0.6173   |
| 0.8757        | 0.37  | 4500  | 0.8529          | 0.6164   |
| 0.8774        | 0.41  | 5000  | 0.8438          | 0.6232   |
| 0.8694        | 0.45  | 5500  | 0.8372          | 0.6246   |
| 0.8653        | 0.49  | 6000  | 0.8350          | 0.6265   |
| 0.8677        | 0.53  | 6500  | 0.8268          | 0.6292   |
| 0.8584        | 0.57  | 7000  | 0.8270          | 0.6326   |
| 0.8508        | 0.61  | 7500  | 0.8134          | 0.6391   |
| 0.8521        | 0.65  | 8000  | 0.8110          | 0.6416   |
| 0.8447        | 0.69  | 8500  | 0.8264          | 0.6323   |
| 0.8466        | 0.73  | 9000  | 0.7951          | 0.6468   |
| 0.8379        | 0.77  | 9500  | 0.8089          | 0.6401   |
| 0.8277        | 0.81  | 10000 | 0.7941          | 0.6477   |
| 0.8307        | 0.86  | 10500 | 0.7999          | 0.6437   |
| 0.8289        | 0.9   | 11000 | 0.7874          | 0.6530   |
| 0.8228        | 0.94  | 11500 | 0.7835          | 0.6524   |
| 0.8228        | 0.98  | 12000 | 0.7851          | 0.6511   |
| 0.8078        | 1.02  | 12500 | 0.7907          | 0.6507   |


### 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"], "model-index": [{"name": "tiny-mlm-glue-qnli-target-glue-mnli", "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-mnli
===================================


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: 0.7907
* Accuracy: 0.6507


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