readme: ---
  license: mit
  language: 
  - ja
  tags:
  - generated_from_trainer
  - ner
  - bert
  metrics:
  - f1
  model-index:
  - name: xlm-roberta-ner-ja
    results: []
  widget:
  - text: "鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った"
  - text: "中国では、中国共産党による一党統治が続く"
  ---
  
  <!-- 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. -->
  
  # xlm-roberta-ner-japanese
  
  (Japanese caption : 日本語の固有表現抽出のモデル)
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) (pre-trained cross-lingual ```RobertaModel```) trained for named entity recognition (NER) token classification.
  
  The model is fine-tuned on NER dataset provided by Stockmark Inc, in which data is collected from Japanese Wikipedia articles.<br>
  See [here](https://github.com/stockmarkteam/ner-wikipedia-dataset) for the license of this dataset.
  
  Each token is labeled by :
  
  | Label id | Tag | Tag in Widget | Description |
  |---|---|---|---|
  | 0 | O | (None) | others or nothing |
  | 1 | PER | PER | person |
  | 2 | ORG | ORG | general corporation organization |
  | 3 | ORG-P | P | political organization |
  | 4 | ORG-O | O | other organization |
  | 5 | LOC | LOC | location |
  | 6 | INS | INS | institution, facility |
  | 7 | PRD | PRD | product |
  | 8 | EVT | EVT | event |
  
  ## Intended uses
  
  ```python
  from transformers import pipeline
  
  model_name = "tsmatz/xlm-roberta-ner-japanese"
  classifier = pipeline("token-classification", model=model_name)
  result = classifier("鈴木は4月の陽気の良い日に、鈴をつけて熊本県の阿蘇山に登った")
  print(result)
  ```
  
  ## Training procedure
  
  You can download the source code for fine-tuning from [here](https://github.com/tsmatz/huggingface-finetune-japanese/blob/master/01-named-entity.ipynb).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 1.0   | 446  | 0.1510          | 0.8457 |
  | No log        | 2.0   | 892  | 0.0626          | 0.9261 |
  | No log        | 3.0   | 1338 | 0.0366          | 0.9580 |
  | No log        | 4.0   | 1784 | 0.0196          | 0.9792 |
  | No log        | 5.0   | 2230 | 0.0173          | 0.9864 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  