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# tiny-mlm-snli-custom-tokenizer

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: 4.8264

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.5934        | 0.4   | 500   | 6.1299          |
| 5.5915        | 0.8   | 1000  | 6.0987          |
| 5.4106        | 1.2   | 1500  | 6.0209          |
| 5.3188        | 1.6   | 2000  | 5.9610          |
| 5.1738        | 2.0   | 2500  | 5.8186          |
| 5.0521        | 2.4   | 3000  | 5.7991          |
| 4.9494        | 2.8   | 3500  | 5.7584          |
| 4.9176        | 3.2   | 4000  | 5.6663          |
| 4.8419        | 3.6   | 4500  | 5.6272          |
| 4.6759        | 4.0   | 5000  | 5.5572          |
| 4.6021        | 4.4   | 5500  | 5.4644          |
| 4.6077        | 4.8   | 6000  | 5.4168          |
| 4.4571        | 5.2   | 6500  | 5.3577          |
| 4.4012        | 5.6   | 7000  | 5.3301          |
| 4.3231        | 6.0   | 7500  | 5.2220          |
| 4.2708        | 6.4   | 8000  | 5.2296          |
| 4.2149        | 6.8   | 8500  | 5.1176          |
| 4.1028        | 7.2   | 9000  | 5.1298          |
| 4.1042        | 7.6   | 9500  | 5.0949          |
| 4.0501        | 8.0   | 10000 | 5.0850          |
| 4.012         | 8.4   | 10500 | 5.0018          |
| 3.875         | 8.8   | 11000 | 5.0539          |
| 3.8863        | 9.2   | 11500 | 4.8985          |
| 3.8032        | 9.6   | 12000 | 4.9226          |
| 3.8501        | 10.0  | 12500 | 4.9202          |
| 3.6744        | 10.4  | 13000 | 4.8908          |
| 3.6515        | 10.8  | 13500 | 4.9305          |
| 3.6525        | 11.2  | 14000 | 4.8675          |
| 3.6416        | 11.6  | 14500 | 4.8610          |
| 3.5686        | 12.0  | 15000 | 4.6993          |
| 3.5437        | 12.4  | 15500 | 4.8085          |
| 3.4837        | 12.8  | 16000 | 4.7654          |
| 3.4553        | 13.2  | 16500 | 4.8264          |


### 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"], "model-index": [{"name": "tiny-mlm-snli-custom-tokenizer", "results": []}]}
transformers pytorch bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

tiny-mlm-snli-custom-tokenizer
==============================


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: 4.8264


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 #fill-mask #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 #fill-mask #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