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Update some model loading in Universe

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Paul O'Leary McCann 2022-12-06 14:14:34 +09:00
parent d9c939c7b1
commit 8987ef59b3
1 changed files with 6 additions and 6 deletions
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website/meta/universe.json
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@ -1037,7 +1037,7 @@
"author_links": {
"github": "mholtzscher"
},
"category": ["pipeline"]
"category": ["v2", "pipeline"]
},
{
"id": "spacy-sentence-segmenter",
@ -1061,7 +1061,7 @@
{
"id": "spacy_cld",
"title": "spaCy-CLD",
"slogan": "Add language detection to your spaCy pipeline using CLD2",
"slogan": "Add language detection to your spaCy v2 pipeline using CLD2",
"description": "spaCy-CLD operates on `Doc` and `Span` spaCy objects. When called on a `Doc` or `Span`, the object is given two attributes: `languages` (a list of up to 3 language codes) and `language_scores` (a dictionary mapping language codes to confidence scores between 0 and 1).\n\nspacy-cld is a little extension that wraps the [PYCLD2](https://github.com/aboSamoor/pycld2) Python library, which in turn wraps the [Compact Language Detector 2](https://github.com/CLD2Owners/cld2) C library originally built at Google for the Chromium project. CLD2 uses character n-grams as features and a Naive Bayes classifier to identify 80+ languages from Unicode text strings (or XML/HTML). It can detect up to 3 different languages in a given document, and reports a confidence score (reported in with each language.",
"github": "nickdavidhaynes/spacy-cld",
"pip": "spacy_cld",
@ -1081,7 +1081,7 @@
"author_links": {
"github": "nickdavidhaynes"
},
"category": ["pipeline"]
"category": ["pipeline", "v2"]
},
{
"id": "spacy-iwnlp",
@ -1349,8 +1349,8 @@
},
{
"id": "neuralcoref",
"slogan": "State-of-the-art coreference resolution based on neural nets and spaCy",
"description": "This coreference resolution module is based on the super fast [spaCy](https://spacy.io/) parser and uses the neural net scoring model described in [Deep Reinforcement Learning for Mention-Ranking Coreference Models](http://cs.stanford.edu/people/kevclark/resources/clark-manning-emnlp2016-deep.pdf) by Kevin Clark and Christopher D. Manning, EMNLP 2016. Since ✨Neuralcoref v2.0, you can train the coreference resolution system on your own dataset — e.g., another language than English! — **provided you have an annotated dataset**. Note that to use neuralcoref with spaCy > 2.1.0, you'll have to install neuralcoref from source.",
"slogan": "State-of-the-art coreference resolution based on neural nets and spaCy v2",
"description": "This coreference resolution module is based on the super fast spaCy parser and uses the neural net scoring model described in [Deep Reinforcement Learning for Mention-Ranking Coreference Models](http://cs.stanford.edu/people/kevclark/resources/clark-manning-emnlp2016-deep.pdf) by Kevin Clark and Christopher D. Manning, EMNLP 2016. Since ✨Neuralcoref v2.0, you can train the coreference resolution system on your own dataset — e.g., another language than English! — **provided you have an annotated dataset**. Note that to use neuralcoref with spaCy > 2.1.0, you'll have to install neuralcoref from source, and v3+ is not supported.",
"github": "huggingface/neuralcoref",
"thumb": "https://i.imgur.com/j6FO9O6.jpg",
"code_example": [
@ -1447,7 +1447,7 @@
"import spacy",
"import explacy",
"",
"nlp = spacy.load('en')",
"nlp = spacy.load('en_core_web_sm')",
"explacy.print_parse_info(nlp, 'The salad was surprisingly tasty.')"
],
"author": "Tyler Neylon",