
# ku-accms/bert-base-japanese-ssuw
## Model description
This is a pre-trained Japanese BERT base model for super short unit words (SSUW).

## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea).

## How to use
You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='ku-accms/bert-base-japanese-ssuw')
>>> unmasker("京都 大学 で [MASK] を 専攻 する 。")
[{'sequence': '京都 大学 で 文学 を 専攻 する 。',
  'score': '0.1464807540178299',
  'token': '14603',
  'token_str': '文学'}
 {'sequence': '京都 大学 で 哲学 を 専攻 する 。',
  'score': '0.08064978569746017',
  'token': '15917',
  'token_str': '哲学'}
 {'sequence': '京都 大学 で 演劇 を 専攻 する 。',
  'score': '0.0800977498292923',
  'token': '16772',
  'token_str': '演劇'}
 {'sequence': '京都 大学 で 法学 を 専攻 する 。',
  'score': '0.04579947143793106',
  'token': '16255',
  'token_str': '法学'}
 {'sequence': '京都 大学 で 英語 を 専攻 する 。',
  'score': '0.045536939054727554',
  'token': '14592',
  'token_str': '英語'}
```

Here is how to use this model to get the features of a given text in PyTorch:
```python
import zenhan
import Mykytea
kytea_model_path = "somewhere"
kytea = Mykytea.Mykytea("-model {} -notags".format(kytea_model_path))
def preprocess(text):
    return " ".join(kytea.getWS(zenhan.h2z(text)))

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('ku-accms/bert-base-japanese-ssuw')
model = BertModel.from_pretrained("ku-accms/bert-base-japanese-ssuw")
text = "京都大学で自然言語処理を専攻する。"
encoded_input = tokenizer(preprocess(text), return_tensors='pt')
output = model(**encoded_input)
```

## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB).

## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using [transformers](https://github.com/huggingface/transformers) library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.

The following hyperparameters were used for the pre-training.

- learning_rate: 2e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000
{"language": "ja", "license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["bert", "fill-mask"], "datasets": ["wikipedia"], "mask_token": "[MASK]", "widget": [{"text": "\u4eac\u90fd \u5927\u5b66 \u3067 [MASK] \u3092 \u5c02\u653b \u3059\u308b \u3002"}, {"text": "\u6771\u4eac \u306f \u65e5\u672c \u306e [MASK] \u3060 \u3002"}, {"text": "\u30ab\u30d5\u30a7 \u3067 [MASK] \u3092 \u6ce8\u6587 \u3059\u308b \u3002"}]}
transformers pytorch bert fill-mask ja dataset:wikipedia license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# ku-accms/bert-base-japanese-ssuw
## Model description
This is a pre-trained Japanese BERT base model for super short unit words (SSUW).

## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea).

## How to use
You can use this model directly with a pipeline for masked language modeling:



Here is how to use this model to get the features of a given text in PyTorch:


## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB).

## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using transformers library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.

The following hyperparameters were used for the pre-training.

- learning_rate: 2e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000
# ku-accms/bert-base-japanese-ssuw ## Model description
This is a pre-trained Japanese BERT base model for super short unit words (SSUW). ## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea). ## How to use
You can use this model directly with a pipeline for masked language modeling:



Here is how to use this model to get the features of a given text in PyTorch: ## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB). ## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using transformers library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.

The following hyperparameters were used for the pre-training.

- learning_rate: 2e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # ku-accms/bert-base-japanese-ssuw ## Model description
This is a pre-trained Japanese BERT base model for super short unit words (SSUW). ## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea). ## How to use
You can use this model directly with a pipeline for masked language modeling:



Here is how to use this model to get the features of a given text in PyTorch: ## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB). ## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using transformers library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.

The following hyperparameters were used for the pre-training.

- learning_rate: 2e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# ku-accms/bert-base-japanese-ssuw## Model description
This is a pre-trained Japanese BERT base model for super short unit words (SSUW).## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea).## How to use
You can use this model directly with a pipeline for masked language modeling:



Here is how to use this model to get the features of a given text in PyTorch:## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB).## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using transformers library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.

The following hyperparameters were used for the pre-training.

- learning_rate: 2e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000