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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_acnl_roberta_pipeline |
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results: |
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- task: |
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name: POS |
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type: token-classification |
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metrics: |
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- name: POS Accuracy |
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type: accuracy |
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value: 0.9805220696 |
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- task: |
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name: SENTER |
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type: token-classification |
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metrics: |
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- name: SENTER Precision |
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type: precision |
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value: 0.9380376859 |
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- name: SENTER Recall |
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type: recall |
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value: 0.954223356 |
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- name: SENTER F Score |
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type: f_score |
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value: 0.946061298 |
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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 Dependencies Accuracy |
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type: accuracy |
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value: 0.9597776646 |
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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 Dependencies Accuracy |
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type: accuracy |
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value: 0.9597776646 |
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license: cc-by-4.0 |
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datasets: |
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- conll2012_ontonotesv5 |
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metrics: |
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- f1 |
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library_name: spacy |
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pipeline_tag: text-classification |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_acnl_roberta_pipeline` | |
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| **Version** | `0.0.1` | |
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| **spaCy** | `>=3.1.3,<3.2.0` | |
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| **Default Pipeline** | `transformer`, `tagger`, `parser` | |
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| **Components** | `transformer`, `tagger`, `parser` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | OntoNotes | |
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| **License** | CC BY-SA 4.0 | |
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| **Author** | Daniel Vasić | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (87 labels for 2 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`, `VERB`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` | |
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| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `dative`, `dep`, `det`, `dobj`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nummod`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | |
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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` | 98.05 | |
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| `DEP_UAS` | 95.98 | |
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| `DEP_LAS` | 94.83 | |
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| `SENTS_P` | 93.80 | |
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| `SENTS_R` | 95.42 | |
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| `SENTS_F` | 94.61 | |
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| `TRANSFORMER_LOSS` | 3784861.59 | |
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| `TAGGER_LOSS` | 698704.80 | |
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| `PARSER_LOSS` | 5540167.00 | |
