|
--- |
|
tags: |
|
- spacy |
|
- token-classification |
|
language: |
|
- en |
|
license: mit |
|
model-index: |
|
- name: en_pakistan_caselaw_ner |
|
results: |
|
- task: |
|
name: NER |
|
type: token-classification |
|
metrics: |
|
- name: NER Precision |
|
type: precision |
|
value: 0.8429743795 |
|
- name: NER Recall |
|
type: recall |
|
value: 0.8436498397 |
|
- name: NER F Score |
|
type: f_score |
|
value: 0.8433119744 |
|
- task: |
|
name: TAG |
|
type: token-classification |
|
metrics: |
|
- name: TAG (XPOS) Accuracy |
|
type: accuracy |
|
value: 0.972937262 |
|
- task: |
|
name: UNLABELED_DEPENDENCIES |
|
type: token-classification |
|
metrics: |
|
- name: Unlabeled Attachment Score (UAS) |
|
type: f_score |
|
value: 0.9176955214 |
|
- task: |
|
name: LABELED_DEPENDENCIES |
|
type: token-classification |
|
metrics: |
|
- name: Labeled Attachment Score (LAS) |
|
type: f_score |
|
value: 0.8991533598 |
|
- task: |
|
name: SENTS |
|
type: token-classification |
|
metrics: |
|
- name: Sentences F-Score |
|
type: f_score |
|
value: 0.9068156724 |
|
--- |
|
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer. |
|
|
|
| Feature | Description | |
|
| --- | --- | |
|
| **Name** | `en_pakistan_caselaw_ner` | |
|
| **Version** | `3.8.0` | |
|
| **spaCy** | `>=3.8.0,<3.9.0` | |
|
| **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** | [Kevin Cole](https://kevin-cole.com) | |
|
|
|
### Label Scheme |
|
|
|
<details> |
|
|
|
<summary>View label scheme (116 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`, `CASE_NUMBER`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PETITIONER`, `PRODUCT`, `QUANTITY`, `RESPONDENT`, `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` | 97.29 | |
|
| `SENTS_P` | 92.01 | |
|
| `SENTS_R` | 89.39 | |
|
| `SENTS_F` | 90.68 | |
|
| `DEP_UAS` | 91.77 | |
|
| `DEP_LAS` | 89.92 | |
|
| `ENTS_P` | 84.30 | |
|
| `ENTS_R` | 84.36 | |
|
| `ENTS_F` | 84.33 | |
