
<!-- 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-mrpc-target-glue-qqp

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mrpc](https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4096
- Accuracy: 0.7995
- F1: 0.7718

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5796        | 0.04  | 500   | 0.5174          | 0.7297   | 0.6813 |
| 0.5102        | 0.09  | 1000  | 0.4804          | 0.7541   | 0.7035 |
| 0.4957        | 0.13  | 1500  | 0.4916          | 0.7412   | 0.7152 |
| 0.4798        | 0.18  | 2000  | 0.4679          | 0.7549   | 0.7221 |
| 0.4728        | 0.22  | 2500  | 0.4563          | 0.7624   | 0.7270 |
| 0.4569        | 0.26  | 3000  | 0.4501          | 0.7673   | 0.7340 |
| 0.4583        | 0.31  | 3500  | 0.4480          | 0.7682   | 0.7375 |
| 0.4502        | 0.35  | 4000  | 0.4498          | 0.7665   | 0.7387 |
| 0.4514        | 0.4   | 4500  | 0.4452          | 0.7681   | 0.7410 |
| 0.4416        | 0.44  | 5000  | 0.4209          | 0.7884   | 0.7491 |
| 0.4297        | 0.48  | 5500  | 0.4288          | 0.7826   | 0.7502 |
| 0.4299        | 0.53  | 6000  | 0.4069          | 0.8001   | 0.7559 |
| 0.4248        | 0.57  | 6500  | 0.4194          | 0.7896   | 0.7547 |
| 0.4257        | 0.62  | 7000  | 0.4063          | 0.7998   | 0.7582 |
| 0.418         | 0.66  | 7500  | 0.4059          | 0.8038   | 0.7639 |
| 0.4306        | 0.7   | 8000  | 0.4111          | 0.7964   | 0.7615 |
| 0.4212        | 0.75  | 8500  | 0.3990          | 0.8065   | 0.7672 |
| 0.4143        | 0.79  | 9000  | 0.4227          | 0.7875   | 0.7604 |
| 0.4121        | 0.84  | 9500  | 0.3906          | 0.8098   | 0.7667 |
| 0.4138        | 0.88  | 10000 | 0.3872          | 0.8152   | 0.7725 |
| 0.4082        | 0.92  | 10500 | 0.3843          | 0.8148   | 0.7700 |
| 0.4084        | 0.97  | 11000 | 0.3863          | 0.8170   | 0.7740 |
| 0.4067        | 1.01  | 11500 | 0.4001          | 0.8037   | 0.7707 |
| 0.3854        | 1.06  | 12000 | 0.3814          | 0.8182   | 0.7756 |
| 0.3945        | 1.1   | 12500 | 0.3861          | 0.8132   | 0.7761 |
| 0.3831        | 1.14  | 13000 | 0.3917          | 0.8110   | 0.7750 |
| 0.3722        | 1.19  | 13500 | 0.4096          | 0.7995   | 0.7718 |


### 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-mrpc-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-mrpc-target-glue-qqp
==================================


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


* Loss: 0.4096
* Accuracy: 0.7995
* F1: 0.7718


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