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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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- en |
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license: mit |
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model-index: |
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- name: en_food_entity_extractor_v2 |
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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: 0.8535469108 |
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- name: NER Recall |
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type: recall |
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value: 0.8592748397 |
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- name: NER F Score |
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type: f_score |
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value: 0.8564012977 |
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- task: |
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name: TAG |
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type: token-classification |
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metrics: |
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- name: TAG (XPOS) Accuracy |
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type: accuracy |
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value: 0.9734404547 |
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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 Attachment Score (UAS) |
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type: f_score |
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value: 0.9204363007 |
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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 Attachment Score (LAS) |
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type: f_score |
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value: 0.9023174614 |
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- task: |
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name: SENTS |
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type: token-classification |
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metrics: |
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- name: Sentences F-Score |
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type: f_score |
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value: 0.90444794 |
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--- |
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English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_food_entity_extractor_v2` | |
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| **Version** | `3.4.1` | |
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| **spaCy** | `>=3.4.0,<3.5.0` | |
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| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | |
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| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | |
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| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | |
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| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) | |
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| **License** | `MIT` | |
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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 (114 labels for 3 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` | |
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| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | |
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| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `FOOD`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `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` | 99.93 | |
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| `TOKEN_P` | 99.57 | |
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| `TOKEN_R` | 99.58 | |
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| `TOKEN_F` | 99.57 | |
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| `TAG_ACC` | 97.34 | |
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| `SENTS_P` | 91.79 | |
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| `SENTS_R` | 89.14 | |
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| `SENTS_F` | 90.44 | |
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| `DEP_UAS` | 92.04 | |
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| `DEP_LAS` | 90.23 | |
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| `ENTS_P` | 85.35 | |
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| `ENTS_R` | 85.93 | |
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| `ENTS_F` | 85.64 | |
