Update README.md

README.md

@@ -1,3 +1,32 @@
----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+---
+
+# What is the Bloominizer
+The bloominer is a fine-tuned version of BERT that classifies questions by the Bloom's Taxonomy level: Knowledge, Comprehension, Application, Analysis, Synthesis, Evaluation.
+
+Tests during training indicate that the Bloominizer is approximately 93% accurate in its classifications, with most misclassifications being for
+either one level below or above (for instance, it may misclassify a Comprehension question as a Knowledge question, but rately as an Evaluation question).
+
+The Bloominizer has been used for large-scale classification of questions from a corpus. For example, a useful usecase is to run all questions in a long
+multiple choice exam through the Bloominizer and compute the relative percentages of questions from the six Bloom's levels. This can give you an idea
+of the approximate cognitive level of the overall exam.
+
+# Using in transformers
+The Bloominizer is easiest to use through a pipeline. Sample code is below:
+
+```
+import transformers
+import torch
+from transformers import pipeline
+pipe = pipeline("text-classification", model="uw-vta/bloominzer-0.1")
+print(pipe("What is a goat?"))
+```
+
+If you run this code, the output should be something like:
+
+```
+[{'label': 'Knowledge', 'score': 0.9993932247161865}]
+```