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 ) 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 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…
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