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