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# bert-small-juman-unigram

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4490
- Accuracy: 0.6911

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 1.9849        | 1.0   | 69472  | 1.8385          | 0.6286   |
| 1.8444        | 2.0   | 138944 | 1.6912          | 0.6513   |
| 1.7767        | 3.0   | 208416 | 1.6322          | 0.6610   |
| 1.7357        | 4.0   | 277888 | 1.5931          | 0.6676   |
| 1.709         | 5.0   | 347360 | 1.5636          | 0.6719   |
| 1.6874        | 6.0   | 416832 | 1.5405          | 0.6756   |
| 1.6707        | 7.0   | 486304 | 1.5221          | 0.6786   |
| 1.6511        | 8.0   | 555776 | 1.5061          | 0.6817   |
| 1.636         | 9.0   | 625248 | 1.4933          | 0.6837   |
| 1.6295        | 10.0  | 694720 | 1.4784          | 0.6860   |
| 1.6157        | 11.0  | 764192 | 1.4673          | 0.6879   |
| 1.6027        | 12.0  | 833664 | 1.4605          | 0.6896   |
| 1.5942        | 13.0  | 903136 | 1.4535          | 0.6904   |
| 1.5866        | 14.0  | 972608 | 1.4490          | 0.6911   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.12.0+cu116
- Datasets 2.2.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-small-juman-unigram", "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-juman-unigram
========================


This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4490
* Accuracy: 0.6911


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
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.2.2
* 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
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.2.2
* 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
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: 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
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.12.0+cu116
* Datasets 2.2.2
* Tokenizers 0.12.1