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	adding spacy-universal-sentence-encoder (#5534)
* adding spacy-universal-sentence-encoder * update affiliation * updated code example
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					@ -99,8 +99,8 @@ mark both statements:
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					| Field                          | Entry                              |
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|------------------------------- | --------------------               |
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					|------------------------------- | --------------------               |
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| Name                           | Martino Mensio                     |
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					| Name                           | Martino Mensio                     |
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| Company name (if applicable)   | Polytechnic University of Turin    |
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					| Company name (if applicable)   | The Open University                |
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| Title or role (if applicable)  | Student                            |
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					| Title or role (if applicable)  | PhD Student                        |
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| Date                           | 17 November 2017                   |
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					| Date                           | 17 November 2017                   |
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| GitHub username                | MartinoMensio                      |
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					| GitHub username                | MartinoMensio                      |
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| Website (optional)             | https://martinomensio.github.io/   |
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					| Website (optional)             | https://martinomensio.github.io/   |
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					@ -1,5 +1,29 @@
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{
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					{
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    "resources": [
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					    "resources": [
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					        {
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					            "id": "spacy-universal-sentence-encoder",
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					            "title": "SpaCy - Universal Sentence Encoder",
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					            "slogan": "Make use of Google's Universal Sentence Encoder directly within SpaCy",
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					            "description": "This library lets you use Universal Sentence Encoder embeddings of Docs, Spans and Tokens directly from TensorFlow Hub",
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					            "github": "MartinoMensio/spacy-universal-sentence-encoder-tfhub",
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					            "code_example": [
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					                "import spacy_universal_sentence_encoder",
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					                "load one of the models: ['en_use_md', 'en_use_lg', 'xx_use_md', 'xx_use_lg']",
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					                "nlp = spacy_universal_sentence_encoder.load_model('en_use_lg')",
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					                "# get two documents",
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					                "doc_1 = nlp('Hi there, how are you?')",
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					                "doc_2 = nlp('Hello there, how are you doing today?')",
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					                "# use the similarity method that is based on the vectors, on Doc, Span or Token",
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					                "print(doc_1.similarity(doc_2[0:7]))"
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					            ],
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					            "category": ["models", "pipeline"],
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					            "author": "Martino Mensio",
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					            "author_links": {
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					                "twitter": "MartinoMensio",
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					                "github": "MartinoMensio",
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					                "website": "https://martinomensio.github.io"
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					            }
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					        },
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					        {
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            "id": "whatlies",
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					            "id": "whatlies",
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            "title": "whatlies",
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					            "title": "whatlies",
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