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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_pipeline |
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results: |
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- tasks: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9946896173 |
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- name: Recall |
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type: recall |
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value: 0.9916932907 |
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- name: F Score |
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type: f_score |
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value: 0.9931891941 |
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- tasks: |
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name: POS |
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type: token-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.0 |
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- tasks: |
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name: SENTER |
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type: token-classification |
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metrics: |
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- name: Precision |
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type: precision |
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value: 1.0 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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- name: F Score |
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type: f_score |
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value: 1.0 |
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- tasks: |
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name: UNLABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.0 |
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- tasks: |
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name: LABELED_DEPENDENCIES |
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type: token-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.0 |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_pipeline` | |
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| **Version** | `0.0.0` | |
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| **spaCy** | `>=3.1.0,<3.2.0` | |
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| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `ner`, `attribute_ruler`, `lemmatizer` | |
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| **Components** | `tok2vec`, `tagger`, `parser`, `ner`, `attribute_ruler`, `lemmatizer` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 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 (114 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`, `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`** | `ARC`, `AST`, `BOOK`, `CAUSAL`, `COMPARISON`, `DATE`, `HEM`, `HOUR`, `HYPO`, `INSTRUMENT`, `JUDGEMENT`, `LAWS`, `MODEL`, `NAME`, `Observation`, `PAR`, `PLACE`, `QUANTITY`, `REASON`, `ZOD` | |
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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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| `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` | 99.32 | |
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| `ENTS_P` | 99.47 | |
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| `ENTS_R` | 99.17 | |
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| `LEMMA_ACC` | 0.00 | |
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| `NER_LOSS` | 7790.09 | |
