From d00fbc1e9e61005d8f0ea42385a69047e1554680 Mon Sep 17 00:00:00 2001
From: Vishnu Nandakumar <38393302+Vishnunkumar@users.noreply.github.com>
Date: Mon, 12 Jun 2023 16:33:20 +0530
Subject: [PATCH] added new model nligraphspacy and made changes on
 eng_spacysentiment

---
 website/meta/universe.json | 29 ++++++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/website/meta/universe.json b/website/meta/universe.json
index 97db9393c..e5f9eaed0 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -4121,11 +4121,38 @@
             "author": "Vishnu Nandakumar",
             "author_links": {
                 "github": "Vishnunkumar",
-                "twitter": "vishnun_uchiha"
+                "twitter": "vishnunkumar_"
             },
             "category": ["pipeline"],
             "tags": ["pipeline", "nlp", "sentiment"]
         },
+        {
+            "id": "nligraphspacy",
+            "title": "nligraphspacy",
+            "slogan": "Knowledge graph using NLP Spacy",
+            "description": "Knowledge graph pipeline for simple english sentences using pre-trained spaCy models",
+            "github": "vishnunkumar/nligraphspacy",
+            "pip": "nligraphspacy",
+            "code_example": [
+                "# Please download the model from the repository and replace the directory with your own i.e where you have kept the model",
+                "import spacy",
+                "kgmodel = spacy.load('nligraphspacy')",
+                "text = She did well in her exams",
+                "doc = nlp(text)",
+                "for tok in doc: print(tok, tok.ent_type_)",
+                "# She SOURCE-NODE did EDGE well TARGET-NODE in her exams TARGET-NODE"
+            ],
+            "thumb": "",
+            "image": "",
+            "code_language": "python",
+            "author": "Vishnu Nandakumar",
+            "author_links": {
+                "github": "Vishnunkumar",
+                "twitter": "vishnunkumar_"
+            },
+            "category": ["pipeline"],
+            "tags": ["pipeline", "nlp", "ner", "knowlegde graph"]
+        },
         {
             "id": "textnets",
             "slogan": "Text analysis with networks",