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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 (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.
See here 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

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.

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
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