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adding spacy-universal-sentence-encoder (#5534)

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* adding spacy-universal-sentence-encoder

* update affiliation

* updated code example
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Martino Mensio 2020-06-08 19:26:30 +01:00 committed by GitHub
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2 changed files with 26 additions and 2 deletions
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4
.github/contributors/MartinoMensio.md vendored
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@ -99,8 +99,8 @@ mark both statements:
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Martino Mensio |
| Company name (if applicable) | Polytechnic University of Turin |
| Title or role (if applicable) | Student |
| Company name (if applicable) | The Open University |
| Title or role (if applicable) | PhD Student |
| Date | 17 November 2017 |
| GitHub username | MartinoMensio |
| Website (optional) | https://martinomensio.github.io/ |

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website/meta/universe.json
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@ -1,5 +1,29 @@
{
"resources": [
{
"id": "spacy-universal-sentence-encoder",
"title": "SpaCy - Universal Sentence Encoder",
"slogan": "Make use of Google's Universal Sentence Encoder directly within SpaCy",
"description": "This library lets you use Universal Sentence Encoder embeddings of Docs, Spans and Tokens directly from TensorFlow Hub",
"github": "MartinoMensio/spacy-universal-sentence-encoder-tfhub",
"code_example": [
"import spacy_universal_sentence_encoder",
"load one of the models: ['en_use_md', 'en_use_lg', 'xx_use_md', 'xx_use_lg']",
"nlp = spacy_universal_sentence_encoder.load_model('en_use_lg')",
"# get two documents",
"doc_1 = nlp('Hi there, how are you?')",
"doc_2 = nlp('Hello there, how are you doing today?')",
"# use the similarity method that is based on the vectors, on Doc, Span or Token",
"print(doc_1.similarity(doc_2[0:7]))"
],
"category": ["models", "pipeline"],
"author": "Martino Mensio",
"author_links": {
"twitter": "MartinoMensio",
"github": "MartinoMensio",
"website": "https://martinomensio.github.io"
}
},
{
"id": "whatlies",
"title": "whatlies",