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# tiny-mlm-glue-rte-target-glue-qqp

This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-rte](https://huggingface.co/muhtasham/tiny-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4155
- Accuracy: 0.7949
- F1: 0.7691

## 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   | 0.5189          | 0.7264   | 0.6855 |
| 0.5081        | 0.09  | 1000  | 0.4824          | 0.7519   | 0.7059 |
| 0.4951        | 0.13  | 1500  | 0.4940          | 0.7377   | 0.7141 |
| 0.4792        | 0.18  | 2000  | 0.4704          | 0.7526   | 0.7221 |
| 0.4722        | 0.22  | 2500  | 0.4571          | 0.7618   | 0.7277 |
| 0.4557        | 0.26  | 3000  | 0.4496          | 0.7677   | 0.7346 |
| 0.4567        | 0.31  | 3500  | 0.4480          | 0.7677   | 0.7378 |
| 0.4497        | 0.35  | 4000  | 0.4502          | 0.7655   | 0.7386 |
| 0.4503        | 0.4   | 4500  | 0.4426          | 0.7712   | 0.7432 |
| 0.4412        | 0.44  | 5000  | 0.4216          | 0.7889   | 0.7501 |
| 0.4291        | 0.48  | 5500  | 0.4284          | 0.7837   | 0.7515 |
| 0.4293        | 0.53  | 6000  | 0.4075          | 0.8004   | 0.7577 |
| 0.4241        | 0.57  | 6500  | 0.4230          | 0.7879   | 0.7559 |
| 0.4253        | 0.62  | 7000  | 0.4067          | 0.8002   | 0.7601 |
| 0.4166        | 0.66  | 7500  | 0.4083          | 0.8026   | 0.7646 |
| 0.4302        | 0.7   | 8000  | 0.4121          | 0.7964   | 0.7624 |
| 0.4206        | 0.75  | 8500  | 0.3993          | 0.8051   | 0.7667 |
| 0.4147        | 0.79  | 9000  | 0.4202          | 0.7884   | 0.7610 |
| 0.4117        | 0.84  | 9500  | 0.3915          | 0.8094   | 0.7677 |
| 0.4131        | 0.88  | 10000 | 0.3863          | 0.8156   | 0.7735 |
| 0.4089        | 0.92  | 10500 | 0.3832          | 0.8157   | 0.7713 |
| 0.4086        | 0.97  | 11000 | 0.3836          | 0.8180   | 0.7732 |
| 0.406         | 1.01  | 11500 | 0.4042          | 0.8018   | 0.7707 |
| 0.3854        | 1.06  | 12000 | 0.3819          | 0.8182   | 0.7763 |
| 0.3952        | 1.1   | 12500 | 0.3836          | 0.8149   | 0.7771 |
| 0.3827        | 1.14  | 13000 | 0.3898          | 0.8134   | 0.7766 |
| 0.3719        | 1.19  | 13500 | 0.4155          | 0.7949   | 0.7691 |


### 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-rte-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-rte-target-glue-qqp
=================================


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


* Loss: 0.4155
* Accuracy: 0.7949
* F1: 0.7691


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