| | ---
|
| | tags:
|
| | - spacy
|
| | - token-classification
|
| | language:
|
| | - en
|
| | license: mit
|
| | model-index:
|
| | - name: en_core_web_sm
|
| | 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
|
| | ---
|
| | ### Details: https://spacy.io/models/en#en_core_web_sm
|
| |
|
| | English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
|
| |
|
| | | Feature | Description |
|
| | | --- | --- |
|
| | | **Name** | `en_core_web_sm` |
|
| | | **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 (113 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`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `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 | |
