| | --- |
| | tags: |
| | - spacy |
| | - token-classification |
| | language: |
| | - en |
| | license: mit |
| | model-index: |
| | - name: en_NER_Features |
| | results: |
| | - task: |
| | name: NER |
| | type: token-classification |
| | metrics: |
| | - name: NER Precision |
| | type: precision |
| | value: 0.8454836771 |
| | - name: NER Recall |
| | type: recall |
| | value: 0.8456530449 |
| | - name: NER F Score |
| | type: f_score |
| | value: 0.8455683525 |
| | - task: |
| | name: TAG |
| | type: token-classification |
| | metrics: |
| | - name: TAG (XPOS) Accuracy |
| | type: accuracy |
| | value: 0.97246532 |
| | - task: |
| | name: UNLABELED_DEPENDENCIES |
| | type: token-classification |
| | metrics: |
| | - name: Unlabeled Attachment Score (UAS) |
| | type: f_score |
| | value: 0.9175304332 |
| | - task: |
| | name: LABELED_DEPENDENCIES |
| | type: token-classification |
| | metrics: |
| | - name: Labeled Attachment Score (LAS) |
| | type: f_score |
| | value: 0.89874821 |
| | - task: |
| | name: SENTS |
| | type: token-classification |
| | metrics: |
| | - name: Sentences F-Score |
| | type: f_score |
| | value: 0.9059485531 |
| | --- |
| | English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.Named Entity Recognition model trained on Google Play Store app descriptions to automatically identify app features from app description. |
| | |
| | | Feature | Description | |
| | | --- | --- | |
| | | **Name** | `en_NER_Features` | |
| | | **Version** | `3.7.1` | |
| | | **spaCy** | `>=3.7.2,<3.8.0` | |
| | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | |
| | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | |
| | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
| | | **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) | |
| | | **License** | `MIT` | |
| | | **Author** | [Explosion](https://explosion.ai) | |
| |
|
| | ### Label Scheme |
| |
|
| | <details> |
| |
|
| | <summary>View label scheme (115 labels for 3 components)</summary> |
| |
|
| | | Component | Labels | |
| | | --- | --- | |
| | | **`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`, ```` | |
| | | **`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` | |
| | | **`ner`** | `AppName`, `CARDINAL`, `DATE`, `EVENT`, `FAC`, `FunctionalFeature`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | |
| | |
| | </details> |
| | |
| | ### Accuracy |
| | |
| | | Type | Score | |
| | | --- | --- | |
| | | `TOKEN_ACC` | 99.86 | |
| | | `TOKEN_P` | 99.57 | |
| | | `TOKEN_R` | 99.58 | |
| | | `TOKEN_F` | 99.57 | |
| | | `TAG_ACC` | 97.25 | |
| | | `SENTS_P` | 92.02 | |
| | | `SENTS_R` | 89.21 | |
| | | `SENTS_F` | 90.59 | |
| | | `DEP_UAS` | 91.75 | |
| | | `DEP_LAS` | 89.87 | |
| | | `ENTS_P` | 84.55 | |
| | | `ENTS_R` | 84.57 | |
| | | `ENTS_F` | 84.56 | |
