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# tiny-mlm-glue-rte-target-glue-mnli

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: 0.7947
- Accuracy: 0.6475

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0719        | 0.04  | 500   | 1.0318          | 0.4653   |
| 1.0131        | 0.08  | 1000  | 0.9779          | 0.5247   |
| 0.9748        | 0.12  | 1500  | 0.9293          | 0.5769   |
| 0.9415        | 0.16  | 2000  | 0.9073          | 0.5893   |
| 0.9255        | 0.2   | 2500  | 0.8888          | 0.6011   |
| 0.9168        | 0.24  | 3000  | 0.8789          | 0.6042   |
| 0.8998        | 0.29  | 3500  | 0.8704          | 0.6077   |
| 0.8948        | 0.33  | 4000  | 0.8624          | 0.6114   |
| 0.8791        | 0.37  | 4500  | 0.8571          | 0.6176   |
| 0.8832        | 0.41  | 5000  | 0.8501          | 0.6192   |
| 0.8742        | 0.45  | 5500  | 0.8423          | 0.6247   |
| 0.87          | 0.49  | 6000  | 0.8410          | 0.6280   |
| 0.874         | 0.53  | 6500  | 0.8322          | 0.6328   |
| 0.8623        | 0.57  | 7000  | 0.8342          | 0.6296   |
| 0.8563        | 0.61  | 7500  | 0.8192          | 0.6394   |
| 0.8562        | 0.65  | 8000  | 0.8194          | 0.6367   |
| 0.8504        | 0.69  | 8500  | 0.8284          | 0.6327   |
| 0.8519        | 0.73  | 9000  | 0.8044          | 0.6424   |
| 0.8436        | 0.77  | 9500  | 0.8175          | 0.6354   |
| 0.8349        | 0.81  | 10000 | 0.8015          | 0.6438   |
| 0.8372        | 0.86  | 10500 | 0.8094          | 0.6368   |
| 0.835         | 0.9   | 11000 | 0.7958          | 0.6469   |
| 0.8291        | 0.94  | 11500 | 0.7922          | 0.6479   |
| 0.8274        | 0.98  | 12000 | 0.7938          | 0.6449   |
| 0.8158        | 1.02  | 12500 | 0.7971          | 0.6450   |
| 0.8111        | 1.06  | 13000 | 0.7947          | 0.6475   |


### 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-rte-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-rte-target-glue-mnli
==================================


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: 0.7947
* Accuracy: 0.6475


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