From 8b3eec6e62122ece5ff5188a9fece06af4f46b92 Mon Sep 17 00:00:00 2001
From: vincent d warmerdam <vincentwarmerdam@gmail.com>
Date: Thu, 1 Apr 2021 14:39:36 +0200
Subject: [PATCH] Add Tokenwiser to Projects (#7541)

* Add tokenwiser

* Update universe.json
---
 website/meta/universe.json | 43 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/website/meta/universe.json b/website/meta/universe.json
index db7657591..e651921ea 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -231,6 +231,49 @@
                 "website": "https://koaning.io"
             }
         },
+        {
+            "id": "tokenwiser",
+            "title": "tokenwiser",
+            "slogan": "Connect vowpal-wabbit & scikit-learn models to spaCy to run simple classification benchmarks. Comes with many utility functions for spaCy pipelines.",
+            "github": "koaning/tokenwiser",
+            "pip": "tokenwiser",
+            "thumb": "https://koaning.github.io/tokenwiser/token.png",
+            "image": "https://koaning.github.io/tokenwiser/logo-tokw.png",
+            "code_example": [
+                "import spacy",
+                "",
+                "from sklearn.pipeline import make_pipeline",
+                "from sklearn.feature_extraction.text import CountVectorizer",
+                "from sklearn.linear_model import LogisticRegression",
+                "",
+                "from tokenwiser.component import attach_sklearn_categoriser",
+                "",
+                "X = [",
+                "    'i really like this post',",
+                "    'thanks for that comment',",
+                "    'i enjoy this friendly forum',",
+                "    'this is a bad post',",
+                "    'i dislike this article',",
+                "    'this is not well written'",
+                "]",
+                "",
+                "y = ['pos', 'pos', 'pos', 'neg', 'neg', 'neg']",
+                "",
+                "# Note that we're training a pipeline here via a single-batch `.fit()` method",
+                "pipe = make_pipeline(CountVectorizer(), LogisticRegression()).fit(X, y)",
+                "",
+                "nlp = spacy.load('en_core_web_sm')",
+                "# This is where we attach our pre-trained model as a pipeline step.",
+                "attach_sklearn_categoriser(nlp, pipe_name='silly_sentiment', estimator=pipe)"
+            ],
+            "category": ["pipeline", "training"],
+            "author": "Vincent D. Warmerdam",
+            "author_links": {
+                "twitter": "fishnets88",
+                "github": "koaning",
+                "website": "https://koaning.io"
+            }
+        },
         {
             "id": "spacy-stanza",
             "title": "spacy-stanza",