
<!-- 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-vanilla-target-glue-mnli

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8100
- Accuracy: 0.6375

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0866        | 0.04  | 500   | 1.0515          | 0.4557   |
| 1.0101        | 0.08  | 1000  | 0.9526          | 0.5612   |
| 0.9599        | 0.12  | 1500  | 0.9195          | 0.5802   |
| 0.9378        | 0.16  | 2000  | 0.9018          | 0.5930   |
| 0.9229        | 0.2   | 2500  | 0.8904          | 0.5954   |
| 0.9182        | 0.24  | 3000  | 0.8802          | 0.6033   |
| 0.9019        | 0.29  | 3500  | 0.8738          | 0.6070   |
| 0.8971        | 0.33  | 4000  | 0.8613          | 0.6154   |
| 0.8788        | 0.37  | 4500  | 0.8593          | 0.6172   |
| 0.8856        | 0.41  | 5000  | 0.8508          | 0.6194   |
| 0.8751        | 0.45  | 5500  | 0.8404          | 0.6256   |
| 0.8718        | 0.49  | 6000  | 0.8445          | 0.6248   |
| 0.8739        | 0.53  | 6500  | 0.8333          | 0.6306   |
| 0.8653        | 0.57  | 7000  | 0.8363          | 0.6280   |
| 0.8588        | 0.61  | 7500  | 0.8213          | 0.6376   |
| 0.8587        | 0.65  | 8000  | 0.8215          | 0.6360   |
| 0.8544        | 0.69  | 8500  | 0.8268          | 0.6292   |
| 0.8556        | 0.73  | 9000  | 0.8045          | 0.6463   |
| 0.8445        | 0.77  | 9500  | 0.8187          | 0.6328   |
| 0.836         | 0.81  | 10000 | 0.8021          | 0.6446   |
| 0.8399        | 0.86  | 10500 | 0.8100          | 0.6375   |


### 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-vanilla-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-vanilla-target-glue-mnli
=============================


This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8100
* Accuracy: 0.6375


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