added Concise Concepts to spaCy universe (#10499)

* Update universe.json

added classy-classification to Spacy universe

* Update universe.json

added classy-classification to the spacy universe resources

* Update universe.json

corrected a small typo in json

* Update website/meta/universe.json

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update website/meta/universe.json

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update website/meta/universe.json

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update universe.json

processed merge feedback

* Update universe.json

* updated information for Classy Classificaiton 

Made a more comprehensible and easy description for Classy Classification based on feedback of Philip Vollet to prepare for sharing.

* added note about examples

* corrected for wrong formatting changes

* Update website/meta/universe.json with small typo correction

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

* resolved another typo

* Update website/meta/universe.json

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* added Concise Concepts package to spaCy universe.

* updated example code Concise Concepts

* updated description for Concise Concepts

* updated PR with more visually appealing examples

SO to koaning for the suggestions.

* corrected for small json typo's in concise concepts

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

website/meta/universe.json

@@ -2606,6 +2606,7 @@
             "description": "Have you ever struggled with needing a [spaCy TextCategorizer](https://spacy.io/api/textcategorizer) but didn't have the time to train one from scratch? Classy Classification is the way to go! For few-shot classification using [sentence-transformers](https://github.com/UKPLab/sentence-transformers) or [spaCy models](https://spacy.io/usage/models), provide a dictionary with labels and examples, or just provide a list of labels for zero shot-classification with [Huggingface zero-shot classifiers](https://huggingface.co/models?pipeline_tag=zero-shot-classification).",
             "github": "davidberenstein1957/classy-classification",
             "pip": "classy-classification",
+            "thumb": "https://raw.githubusercontent.com/Pandora-Intelligence/classy-classification/master/logo.png",
             "code_example": [
                 "import spacy",
                 "import classy_classification",
@@ -2651,6 +2652,59 @@
             ],
             "spacy_version": 3
         },
+        {
+            "id": "conciseconcepts",
+            "title": "Concise Concepts",
+            "slogan": "Concise Concepts uses few-shot NER based on word embedding similarity to get you going with easy!",
+            "description": "When wanting to apply NER to concise concepts, it is really easy to come up with examples, but it takes some effort to train an entire pipeline. Concise Concepts uses few-shot NER based on word embedding similarity to get you going with easy!",
+            "github": "pandora-intelligence/concise-concepts",
+            "pip": "concise-concepts",
+            "thumb": "https://raw.githubusercontent.com/Pandora-Intelligence/concise-concepts/master/img/logo.png",
+            "image": "https://raw.githubusercontent.com/Pandora-Intelligence/concise-concepts/master/img/example.png",
+            "code_example": [
+                "import spacy",
+                "from spacy import displacy",
+                "import concise_concepts",
+                "",
+                "data = {",
+                "    \"fruit\": [\"apple\", \"pear\", \"orange\"],",
+                "    \"vegetable\": [\"broccoli\", \"spinach\", \"tomato\"],",
+                "    \"meat\": [\"beef\", \"pork\", \"fish\", \"lamb\"]",
+                "}",
+                "",
+                "text = \"\"\"",
+                "    Heat the oil in a large pan and add the Onion, celery and carrots.",
+                "    Then, cook over a medium–low heat for 10 minutes, or until softened.",
+                "    Add the courgette, garlic, red peppers and oregano and cook for 2–3 minutes.",
+                "    Later, add some oranges and chickens.\"\"\"",
+                "",
+                "# use any model that has internal spacy embeddings", 
+                "nlp = spacy.load('en_core_web_lg')",
+                "nlp.add_pipe(\"concise_concepts\", ",
+                "    config={\"data\": data}",
+                ")",
+                "doc = nlp(text)",
+                "",
+                "options = {\"colors\": {\"fruit\": \"darkorange\", \"vegetable\": \"limegreen\", \"meat\": \"salmon\"},",
+                "           \"ents\": [\"fruit\", \"vegetable\", \"meat\"]}",
+                "",
+                "displacy.render(doc, style=\"ent\", options=options)"
+            ],
+            "author": "David Berenstein",
+            "author_links": {
+                "github": "davidberenstein1957",
+                "website": "https://www.linkedin.com/in/david-berenstein-1bab11105/"
+            },
+            "category": [
+                "pipeline"
+            ],
+            "tags": [
+                "ner",
+                "few-shot",
+                "gensim"
+            ],
+            "spacy_version": 3
+        },
         {
             "id": "blackstone",
             "title": "Blackstone",