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# bert-small-UnidicBpe2

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5693
- Accuracy: 0.6686

## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 768
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 14.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 2.0974        | 1.0   | 69473  | 1.9746          | 0.6071   |
| 1.9586        | 2.0   | 138946 | 1.8301          | 0.6284   |
| 1.889         | 3.0   | 208419 | 1.7627          | 0.6383   |
| 1.8496        | 4.0   | 277892 | 1.7236          | 0.6442   |
| 1.8188        | 5.0   | 347365 | 1.6924          | 0.6490   |
| 1.7983        | 6.0   | 416838 | 1.6650          | 0.6535   |
| 1.7788        | 7.0   | 486311 | 1.6484          | 0.6558   |
| 1.7623        | 8.0   | 555784 | 1.6328          | 0.6580   |
| 1.7497        | 9.0   | 625257 | 1.6182          | 0.6605   |
| 1.7321        | 10.0  | 694730 | 1.6064          | 0.6623   |
| 1.7225        | 11.0  | 764203 | 1.5908          | 0.6647   |
| 1.707         | 12.0  | 833676 | 1.5859          | 0.6660   |
| 1.7049        | 13.0  | 903149 | 1.5752          | 0.6672   |
| 1.6982        | 14.0  | 972622 | 1.5693          | 0.6686   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.12.0+cu116
- Datasets 2.9.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-small-UnidicBpe2", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

bert-small-UnidicBpe2
=====================


This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.5693
* Accuracy: 0.6686


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: 0.0001
* train\_batch\_size: 256
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 3
* total\_train\_batch\_size: 768
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 14.0


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.9.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 256
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 3
* total\_train\_batch\_size: 768
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 14.0 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 256
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 3
* total\_train\_batch\_size: 768
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 14.0 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 256
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 3
* total\_train\_batch\_size: 768
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.01
* num\_epochs: 14.0### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.9.0
* Tokenizers 0.12.1