add gobbli to spacy-universe 🥳

website/meta/universe.json

@@ -2000,6 +2000,44 @@
             },
             "category": ["pipeline"],
             "tags": ["syllables", "multilingual"]
+        },
+        {
+            "id": "gobbli",
+            "title": "gobbli",
+            "slogan": "Deep learning for text classification doesn't have to be scary",
+            "description": "gobbli is a Python library which wraps several modern deep learning models in a uniform interface that makes it easy to evaluate feasibility and conduct analyses. It leverages the abstractive powers of Docker to hide nearly all dependency management and functional differences between models from the user. It also contains an interactive app for exploring text data and evaluating classification models.",
+            "url": "https://github.com/rtiinternational/gobbli",
+            "github": "rtiinternational/gobbli",
+            "pip": "gobbli",
+            "thumb": "https://i.postimg.cc/NGpzhrdr/gobbli-lg.png",
+            "code_example": [
+                "from gobbli.io import PredictInput, TrainInput",
+                "from gobbli.model.bert import BERT",
+                "",
+                "train_input = TrainInput(",
+                "    X_train=['This is a training document.', 'This is another training document.'],",
+                "    y_train=['0', '1'],",
+                "    X_valid=['This is a validation sentence.', 'This is another validation sentence.'],",
+                "    y_valid=['1', '0'],",
+                ")",
+                "",
+                "clf = BERT()",
+                "",
+                "# Set up classifier resources -- Docker image, etc.",
+                "clf.build()",
+                "",
+                "# Train model",
+                "train_output = clf.train(train_input)",
+                "",
+                "predict_input = PredictInput(",
+                "    X=['Which class is this document?'],",
+                "    labels=train_output.labels,",
+                "    checkpoint=train_output.checkpoint,",
+                ")",
+                "",
+                "predict_output = clf.predict(predict_input)"
+            ],
+            "category": ["standalone"]
         }
     ],