
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# tiny-mlm-glue-sst2-target-glue-qqp

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-sst2](https://huggingface.co/muhtasham/tiny-mlm-glue-sst2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4117
- Accuracy: 0.7972
- F1: 0.7705

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.578         | 0.04  | 500   | 0.5173          | 0.7295   | 0.6786 |
| 0.5102        | 0.09  | 1000  | 0.4813          | 0.7532   | 0.7023 |
| 0.4981        | 0.13  | 1500  | 0.4910          | 0.7409   | 0.7150 |
| 0.4808        | 0.18  | 2000  | 0.4655          | 0.7558   | 0.7214 |
| 0.4728        | 0.22  | 2500  | 0.4552          | 0.7634   | 0.7282 |
| 0.4557        | 0.26  | 3000  | 0.4475          | 0.7693   | 0.7353 |
| 0.4577        | 0.31  | 3500  | 0.4464          | 0.7690   | 0.7379 |
| 0.4507        | 0.35  | 4000  | 0.4495          | 0.7670   | 0.7397 |
| 0.4511        | 0.4   | 4500  | 0.4409          | 0.7721   | 0.7437 |
| 0.4414        | 0.44  | 5000  | 0.4189          | 0.7903   | 0.7499 |
| 0.4291        | 0.48  | 5500  | 0.4267          | 0.7838   | 0.7510 |
| 0.431         | 0.53  | 6000  | 0.4064          | 0.8005   | 0.7566 |
| 0.4236        | 0.57  | 6500  | 0.4161          | 0.7930   | 0.7573 |
| 0.4258        | 0.62  | 7000  | 0.4038          | 0.8030   | 0.7608 |
| 0.4167        | 0.66  | 7500  | 0.4066          | 0.8041   | 0.7648 |
| 0.4312        | 0.7   | 8000  | 0.4111          | 0.7966   | 0.7621 |
| 0.4203        | 0.75  | 8500  | 0.3971          | 0.8068   | 0.7671 |
| 0.4143        | 0.79  | 9000  | 0.4187          | 0.7894   | 0.7613 |
| 0.4115        | 0.84  | 9500  | 0.3884          | 0.8127   | 0.7688 |
| 0.4133        | 0.88  | 10000 | 0.3849          | 0.8172   | 0.7731 |
| 0.4091        | 0.92  | 10500 | 0.3826          | 0.8178   | 0.7725 |
| 0.4085        | 0.97  | 11000 | 0.3832          | 0.8186   | 0.7723 |
| 0.4066        | 1.01  | 11500 | 0.4000          | 0.8039   | 0.7711 |
| 0.3859        | 1.06  | 12000 | 0.3798          | 0.8195   | 0.7758 |
| 0.3955        | 1.1   | 12500 | 0.3835          | 0.8159   | 0.7781 |
| 0.3833        | 1.14  | 13000 | 0.3872          | 0.8138   | 0.7764 |
| 0.3722        | 1.19  | 13500 | 0.4117          | 0.7972   | 0.7705 |


### 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-sst2-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-sst2-target-glue-qqp
==================================


This model is a fine-tuned version of muhtasham/tiny-mlm-glue-sst2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4117
* Accuracy: 0.7972
* F1: 0.7705


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