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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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model-index: |
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- name: en_egw_entity_extractor |
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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: 0.892504864 |
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- name: NER Recall |
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type: recall |
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value: 0.8951781257 |
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- name: NER F Score |
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type: f_score |
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value: 0.8938394961 |
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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: 0.9731235568 |
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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: 0.9383889999 |
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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: 0.9226387112 |
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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: 0.9496868166 |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_egw_entity_extractor` | |
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| **Version** | `1.0.0` | |
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| **spaCy** | `>=3.7.2,<3.8.0` | |
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| **Default Pipeline** | `transformer`, `tagger`, `parser`, `ner` | |
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| **Components** | `transformer`, `tagger`, `parser`, `ner` | |
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| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | |
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| **Sources** | n/a | |
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| **License** | n/a | |
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| **Author** | [n/a]() | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (100 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`** | `DATE`, `GPE`, `LOC`, `PERSON`, `REFERENCE` | |
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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` | 97.31 | |
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| `DEP_UAS` | 93.84 | |
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| `DEP_LAS` | 92.26 | |
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| `SENTS_P` | 95.17 | |
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| `SENTS_R` | 94.77 | |
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| `SENTS_F` | 94.97 | |
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| `ENTS_F` | 89.38 | |
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| `ENTS_P` | 89.25 | |
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| `ENTS_R` | 89.52 | |
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| `TRANSFORMER_LOSS` | 10291319.95 | |
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| `TAGGER_LOSS` | 1200598.50 | |
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| `PARSER_LOSS` | 5348475.42 | |
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| `NER_LOSS` | 297327.96 | |
