From 2f6b8132ef81c8cb6863f378c8fdead2258fae03 Mon Sep 17 00:00:00 2001
From: Martino Mensio <martino.mensio@open.ac.uk>
Date: Mon, 27 Jul 2020 09:44:33 +0200
Subject: [PATCH] Sentence transformers added to spaCy universe (#5814)

* fix details for spacy-universal-sentence-encoder

* added sentence-transformers
---
 website/meta/universe.json | 30 ++++++++++++++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

diff --git a/website/meta/universe.json b/website/meta/universe.json
index d6e55a2ef..8790cd6af 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -1,5 +1,30 @@
 {
     "resources": [
+        {
+            "id": "spacy-sentence-bert",
+            "title": "SpaCy - sentence-transformers",
+            "slogan": "Pipelines for pretrained sentence-transformers (BERT, RoBERTa, XLM-RoBERTa & Co.) directly within SpaCy",
+            "description": "This library lets you use the embeddings from [sentence-transformers](https://github.com/UKPLab/sentence-transformers) of Docs, Spans and Tokens directly from spaCy. Most models are for the english language but three of them are multilingual.",
+            "github": "MartinoMensio/spacy-sentence-bert",
+            "pip": "spacy-sentence-bert",
+            "code_example": [
+                "import spacy_sentence_bert",
+                "# load one of the models listed at https://github.com/MartinoMensio/spacy-sentence-bert/",
+                "nlp = spacy_sentence_bert.load_model('en_roberta_large_nli_stsb_mean_tokens')",
+                "# 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": "spacy-streamlit",
             "title": "spacy-streamlit",
@@ -58,10 +83,11 @@
             "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",
+            "github": "MartinoMensio/spacy-universal-sentence-encoder",
+            "pip": "spacy-universal-sentence-encoder",
             "code_example": [
                 "import spacy_universal_sentence_encoder",
-                "load one of the models: ['en_use_md', 'en_use_lg', 'xx_use_md', 'xx_use_lg']",
+                "# 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?')",