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

python

from transformers import pipeline

model_name = "tsmatz/xlm-roberta-ner-japanese"
classifier = pipeline("token-classification", model=model_name)
result = classifier("鈴井は4月の陽気の良い日に、鈴をつけて北海道のトムラウシへと登った")
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