Data programing bake-off details

data_programming.ipynb

@@ -45,7 +45,8 @@
     "  0. [Applying the SST labeling functions](#Applying-the-SST-labeling-functions)\n",
     "  0. [Fitting the SST generative model](#Fitting-the-SST-generative-model)\n",
     "  0. [Direct assessment of the inferred labels against the gold ones](#Direct-assessment-of-the-inferred-labels-against-the-gold-ones)\n",
-    "  0. [Fitting a discriminative model on the noisy labels](#Fitting-a-discriminative-model-on-the-noisy-labels)"
+    "  0. [Fitting a discriminative model on the noisy labels](#Fitting-a-discriminative-model-on-the-noisy-labels)\n",
+    "0. [Extra-credit bake-off](#Extra-credit-bake-off)"
    ]
   },
   {
@@ -1403,6 +1404,28 @@
    "source": [
     "At this point, we might [return to writing more labeling functions](#Other-SST-labeling-function-ideas), in the hope of improving our dev-set results. We got this far with only two simple lexicon-based feature functions, so there is reason to be optimistic that we can train effective models without showing our models any gold labels!"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extra-credit bake-off\n",
+    "\n",
+    "This is a fast, optional bake-off intended to be done in class on May 2:\n",
+    "    \n",
+    "__Question__: How good an F1 score can you get with the function call in [Direct assessment of the inferred labels against the gold ones](#Direct-assessment-of-the-inferred-labels-against-the-gold-ones) above? This just compares the actual gold labels in the train set against the ones you're creating with data programming.\n",
+    "\n",
+    "__To submit__:\n",
+    "\n",
+    "1. Your average F1 score from this assessment.\n",
+    "1. A description of the labeling functions you wrote to get this score.\n",
+    "\n",
+    "To get full credit, you just need to write at least one new labeling function and try it out.\n",
+    "\n",
+    "Submission URL: https://goo.gl/forms/MtyQHoWDHmU5oEyt1\n",
+    "\n",
+    "The close-time for this is May 2, 11:59 pm."
+   ]
   }
  ],
  "metadata": {