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# tiny-mlm-glue-sst2-target-glue-mnli

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: 0.7870
- Accuracy: 0.6519

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.076         | 0.04  | 500   | 1.0342          | 0.4657   |
| 1.0114        | 0.08  | 1000  | 0.9714          | 0.5393   |
| 0.9654        | 0.12  | 1500  | 0.9268          | 0.5736   |
| 0.9381        | 0.16  | 2000  | 0.9120          | 0.5849   |
| 0.9266        | 0.2   | 2500  | 0.8942          | 0.5953   |
| 0.9171        | 0.24  | 3000  | 0.8783          | 0.6014   |
| 0.9009        | 0.29  | 3500  | 0.8687          | 0.6085   |
| 0.8932        | 0.33  | 4000  | 0.8567          | 0.6191   |
| 0.8767        | 0.37  | 4500  | 0.8524          | 0.6171   |
| 0.8768        | 0.41  | 5000  | 0.8436          | 0.6231   |
| 0.8702        | 0.45  | 5500  | 0.8374          | 0.6220   |
| 0.8673        | 0.49  | 6000  | 0.8345          | 0.6271   |
| 0.8684        | 0.53  | 6500  | 0.8274          | 0.6274   |
| 0.8606        | 0.57  | 7000  | 0.8282          | 0.6298   |
| 0.8528        | 0.61  | 7500  | 0.8146          | 0.6363   |
| 0.8529        | 0.65  | 8000  | 0.8103          | 0.6406   |
| 0.8467        | 0.69  | 8500  | 0.8237          | 0.6320   |
| 0.8478        | 0.73  | 9000  | 0.7964          | 0.6473   |
| 0.8399        | 0.77  | 9500  | 0.8081          | 0.6391   |
| 0.8295        | 0.81  | 10000 | 0.7954          | 0.6475   |
| 0.833         | 0.86  | 10500 | 0.7994          | 0.6439   |
| 0.8316        | 0.9   | 11000 | 0.7886          | 0.6513   |
| 0.8239        | 0.94  | 11500 | 0.7847          | 0.6544   |
| 0.8247        | 0.98  | 12000 | 0.7848          | 0.6512   |
| 0.81          | 1.02  | 12500 | 0.7915          | 0.6507   |
| 0.8059        | 1.06  | 13000 | 0.7870          | 0.6519   |


### 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-sst2-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-sst2-target-glue-mnli
===================================


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: 0.7870
* Accuracy: 0.6519


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