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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: cc-by-sa-3.0 |
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model-index: |
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- name: en_ner_craft_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: 0.8277022815 |
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- name: NER Recall |
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type: recall |
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value: 0.7689367616 |
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- name: NER F Score |
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type: f_score |
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value: 0.7972380666 |
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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: 0.0 |
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- task: |
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name: LEMMA |
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type: token-classification |
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metrics: |
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- name: Lemma Accuracy |
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type: accuracy |
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value: 0.0 |
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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: 0.0 |
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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: 0.0 |
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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: 1.0 |
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--- |
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Spacy Models for Biomedical Text. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_ner_craft_md` | |
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| **Version** | `0.5.3` | |
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| **spaCy** | `>=3.6.1,<3.7.0` | |
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| **Default Pipeline** | `tok2vec`, `tagger`, `attribute_ruler`, `lemmatizer`, `parser`, `ner` | |
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| **Components** | `tok2vec`, `tagger`, `attribute_ruler`, `lemmatizer`, `parser`, `ner` | |
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| **Vectors** | 4087446 keys, 50000 unique vectors (200 dimensions) | |
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| **Sources** | CRAFT<br>OntoNotes 5<br>Common Crawl<br>GENIA 1.0 | |
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| **License** | `CC BY-SA 3.0` | |
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| **Author** | [Allen Institute for Artificial Intelligence](https://allenai.github.io/SciSpaCy/) | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (103 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`, ```` | |
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| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `acomp`, `advcl`, `advmod`, `amod`, `amod@nmod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `cc:preconj`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dative`, `dep`, `det`, `det:predet`, `dobj`, `expl`, `intj`, `mark`, `meta`, `mwe`, `neg`, `nmod`, `nmod:npmod`, `nmod:poss`, `nmod:tmod`, `nsubj`, `nsubjpass`, `nummod`, `parataxis`, `pcomp`, `pobj`, `preconj`, `predet`, `prep`, `punct`, `quantmod`, `xcomp` | |
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| **`ner`** | `CHEBI`, `CL`, `GGP`, `GO`, `SO`, `TAXON` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `TAG_ACC` | 0.00 | |
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| `LEMMA_ACC` | 0.00 | |
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| `DEP_UAS` | 0.00 | |
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| `DEP_LAS` | 0.00 | |
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| `DEP_LAS_PER_TYPE` | 0.00 | |
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| `SENTS_P` | 100.00 | |
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| `SENTS_R` | 100.00 | |
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| `SENTS_F` | 100.00 | |
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| `ENTS_F` | 79.72 | |
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| `ENTS_P` | 82.77 | |
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| `ENTS_R` | 76.89 | |
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| `NER_LOSS` | 507618.89 | |
