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

* adding spacy-universal-sentence-encoder * update affiliation * updated code example
explosion · Jun 8, 2020 · de00f96 · de00f96
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diff --git a/.github/contributors/MartinoMensio.md b/.github/contributors/MartinoMensio.md
@@ -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/   |
diff --git a/website/meta/universe.json b/website/meta/universe.json
@@ -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",