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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- ja |
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license: cc-by-sa-4.0 |
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model-index: |
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- name: ja_core_news_md |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.737704918 |
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- name: NER Recall |
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type: recall |
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value: 0.679245283 |
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- name: NER F Score |
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type: f_score |
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value: 0.7072691552 |
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- task: |
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name: POS |
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type: token-classification |
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metrics: |
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- name: POS Accuracy |
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type: accuracy |
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value: 0.9715755942 |
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- task: |
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name: SENTER |
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type: token-classification |
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metrics: |
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- name: SENTER Precision |
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type: precision |
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value: 0.9862475442 |
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- name: SENTER Recall |
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type: recall |
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value: 0.9901380671 |
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- name: SENTER F Score |
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type: f_score |
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value: 0.9881889764 |
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- task: |
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name: UNLABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Unlabeled Dependencies Accuracy |
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type: accuracy |
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value: 0.9224188392 |
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- task: |
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name: LABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Labeled Dependencies Accuracy |
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type: accuracy |
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value: 0.9224188392 |
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--- |
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### Details: https://spacy.io/models/ja#ja_core_news_md |
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Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `ja_core_news_md` | |
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| **Version** | `3.2.0` | |
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| **spaCy** | `>=3.2.0,<3.3.0` | |
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| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `ner` | |
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| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `ner` | |
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| **Vectors** | 480443 keys, 20000 unique vectors (300 dimensions) | |
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| **Sources** | [UD Japanese GSD v2.8](https://github.com/UniversalDependencies/UD_Japanese-GSD) (Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel)<br />[UD Japanese GSD v2.8 NER](https://github.com/megagonlabs/UD_Japanese-GSD) (Megagon Labs Tokyo)<br />[chiVe: Japanese Word Embedding with Sudachi & NWJC (chive-1.1-mc90-500k)](https://github.com/WorksApplications/chiVe) (Works Applications) | |
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| **License** | `CC BY-SA 4.0` | |
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| **Author** | [Explosion](https://explosion.ai) | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (66 labels for 4 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=VERB`, `POS=SCONJ`, `POS=AUX`, `POS=PUNCT`, `POS=PART`, `POS=DET`, `POS=NUM`, `POS=ADV`, `POS=PRON`, `POS=ADJ`, `POS=PROPN`, `POS=CCONJ`, `POS=SYM`, `POS=NOUN\|Polarity=Neg`, `POS=AUX\|Polarity=Neg`, `POS=INTJ`, `POS=SCONJ\|Polarity=Neg` | |
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| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` | |
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| **`senter`** | `I`, `S` | |
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| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `TOKEN_ACC` | 99.69 | |
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| `TOKEN_P` | 97.65 | |
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| `TOKEN_R` | 97.90 | |
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| `TOKEN_F` | 97.77 | |
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| `POS_ACC` | 97.13 | |
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| `MORPH_ACC` | 0.40 | |
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| `MORPH_MICRO_P` | 34.01 | |
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| `MORPH_MICRO_R` | 98.04 | |
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| `MORPH_MICRO_F` | 50.51 | |
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| `SENTS_P` | 98.62 | |
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| `SENTS_R` | 99.01 | |
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| `SENTS_F` | 98.82 | |
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| `DEP_UAS` | 92.24 | |
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| `DEP_LAS` | 90.75 | |
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| `TAG_ACC` | 97.16 | |
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| `LEMMA_ACC` | 96.59 | |
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| `ENTS_P` | 73.77 | |
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| `ENTS_R` | 67.92 | |
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| `ENTS_F` | 70.73 | |
