Update spaCy pipeline
Browse files- LICENSES_SOURCES +4 -4
- README.md +47 -40
- accuracy.json +204 -204
- attribute_ruler/patterns +0 -0
- config.cfg +7 -11
- en_core_web_md-any-py3-none-any.whl +2 -2
- meta.json +216 -213
- ner/model +2 -2
- ner/moves +1 -1
- parser/model +0 -0
- parser/moves +2 -1
- senter/model +0 -0
- tagger/cfg +0 -2
- tagger/model +0 -0
- tok2vec/model +2 -2
- tokenizer +0 -0
- vocab/key2row +2 -2
- vocab/strings.json +0 -0
- vocab/vectors +1 -1
LICENSES_SOURCES
CHANGED
@@ -64,11 +64,11 @@ Princeton University and LICENSEE agrees to preserve same.```
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#
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* Author:
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* URL: https://
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* License:
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```
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The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and Related Rights (defined below) upon the creator and subsequent owner(s) (each and all, an "owner") of an original work of authorship and/or a database (each, a "Work").
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# GloVe Common Crawl
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* Author: Jeffrey Pennington, Richard Socher, and Christopher D. Manning
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* URL: https://nlp.stanford.edu/projects/glove/
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* License: Public Domain Dedication and License v1.0
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```
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The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and Related Rights (defined below) upon the creator and subsequent owner(s) (each and all, an "owner") of an original work of authorship and/or a database (each, a "Work").
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README.md
CHANGED
@@ -14,41 +14,41 @@ model-index:
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metrics:
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- name: NER Precision
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type: precision
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value: 0.
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- name: NER Recall
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type: recall
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value: 0.
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- name: NER F Score
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type: f_score
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value: 0.
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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.
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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.
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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.
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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.
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---
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---
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tags:
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metrics:
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- name: NER Precision
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type: precision
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value: 0.
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- name: NER Recall
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type: recall
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value: 0.
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- name: NER F Score
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type: f_score
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value: 0.
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- task:
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name:
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type: token-classification
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metrics:
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- name:
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type: accuracy
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value: 0.
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- task:
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name:
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type: token-classification
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metrics:
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- name:
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type: f_score
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value: 0.
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name:
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type: token-classification
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metrics:
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- name:
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type:
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value: 0.
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- task:
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name:
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type: token-classification
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metrics:
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- name:
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type:
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value: 0.
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---
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### Details: https://spacy.io/models/en#en_core_web_md
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| Feature | Description |
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| --- | --- |
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| **Name** | `en_core_web_md` |
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| **Version** | `3.
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| **spaCy** | `>=3.
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| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
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| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
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| **Vectors** |
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| **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)<br />[
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| **License** | `MIT` |
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| **Author** | [Explosion](https://explosion.ai) |
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@@ -122,12 +128,13 @@ English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter,
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<details>
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<summary>View label scheme (
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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`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
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</details>
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| Type | Score |
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| --- | --- |
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-
| `TOKEN_ACC` | 99.
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| `TOKEN_P` | 99.57 |
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| `TOKEN_R` | 99.58 |
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| `TOKEN_F` | 99.57 |
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| `TAG_ACC` | 97.
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-
| `SENTS_P` |
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| `SENTS_R` | 89.
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| `SENTS_F` | 90.
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| `DEP_UAS` |
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| `DEP_LAS` | 90.
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| `ENTS_P` |
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| `ENTS_R` |
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| `ENTS_F` |
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metrics:
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- name: NER Precision
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type: precision
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value: 0.8531330602
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- name: NER Recall
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type: recall
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value: 0.8448016827
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- name: NER F Score
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type: f_score
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value: 0.8489469314
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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: 0.9736958159
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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: 0.9186827918
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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: 0.9006556195
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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: 0.9029823331
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---
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---
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tags:
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metrics:
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- name: NER Precision
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type: precision
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+
value: 0.8531330602
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- name: NER Recall
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type: recall
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+
value: 0.8448016827
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- name: NER F Score
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type: f_score
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+
value: 0.8489469314
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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: 0.9736958159
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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: 0.9144345238
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- name: SENTER Recall
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+
type: recall
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+
value: 0.8918134442
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- name: SENTER F Score
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type: f_score
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+
value: 0.9029823331
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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: 0.9186827918
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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: 0.9186827918
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---
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### Details: https://spacy.io/models/en#en_core_web_md
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|
|
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| Feature | Description |
|
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| --- | --- |
|
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| **Name** | `en_core_web_md` |
|
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+
| **Version** | `3.2.0` |
|
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| **spaCy** | `>=3.2.0,<3.3.0` |
|
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| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
|
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| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
|
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+
| **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) |
|
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+
| **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)<br />[GloVe Common Crawl](https://nlp.stanford.edu/projects/glove/) (Jeffrey Pennington, Richard Socher, and Christopher D. Manning) |
|
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| **License** | `MIT` |
|
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| **Author** | [Explosion](https://explosion.ai) |
|
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|
|
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<details>
|
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+
<summary>View label scheme (114 labels for 4 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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+
| **`senter`** | `I`, `S` |
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| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
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</details>
|
|
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| Type | Score |
|
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| --- | --- |
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| `TOKEN_ACC` | 99.93 |
|
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| `TOKEN_P` | 99.57 |
|
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| `TOKEN_R` | 99.58 |
|
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| `TOKEN_F` | 99.57 |
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+
| `TAG_ACC` | 97.37 |
|
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| `SENTS_P` | 91.44 |
|
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| `SENTS_R` | 89.18 |
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| `SENTS_F` | 90.30 |
|
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| `DEP_UAS` | 91.87 |
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| `DEP_LAS` | 90.07 |
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| `ENTS_P` | 85.31 |
|
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| `ENTS_R` | 84.48 |
|
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| `ENTS_F` | 84.89 |
|
accuracy.json
CHANGED
@@ -1,109 +1,109 @@
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{
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"token_acc": 0.
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"dep_las_per_type": {
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"compound": {
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"ccomp": {
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"attr": {
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"case": {
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"mark": {
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"intj": {
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"cc": {
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"neg": {
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"p": 0.9431988042,
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"f": 0.9465
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},
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"conj": {
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"nsubjpass": {
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"auxpass": {
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Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ
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|
config.cfg
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@@ -54,8 +54,8 @@ nO = null
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|
54 |
[components.ner.model.tok2vec.embed]
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55 |
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56 |
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58 |
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60 |
|
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[components.ner.model.tok2vec.encode]
|
@@ -93,9 +93,8 @@ overwrite = false
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|
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scorer = {"@scorers":"spacy.senter_scorer.v1"}
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94 |
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96 |
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98 |
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99 |
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|
101 |
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@@ -116,14 +115,12 @@ maxout_pieces = 2
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|
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|
117 |
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|
118 |
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119 |
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|
120 |
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|
121 |
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124 |
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|
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126 |
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127 |
|
128 |
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|
129 |
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@@ -139,8 +136,8 @@ factory = "tok2vec"
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|
139 |
[components.tok2vec.model.embed]
|
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|
@@ -177,12 +174,11 @@ dropout = 0.1
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|
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|
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|
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|
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@architectures = "spacy.Tagger.v1"
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|
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|
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|
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|
115 |
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|
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@architectures = "spacy.Tagger.v1"
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|
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|
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