Merge pull request #80 from nicholaskross/master

Started SAT eval

lm_eval/tasks/sat.py

@@ -3,7 +3,10 @@
 import json
 import random
 import os
-from lm_eval.base import Dataset
+from lm_eval.base import Dataset, rf, mean
+from tqdm import auto as tqdm_lib
+from . common import simple_accuracy_metric
+import numpy as np
 from ..utils import sh
 
 
@@ -93,8 +96,37 @@ class SATAnalogies(Dataset):
 
         return text
 
-    # TODO: Implement evaluation code
 
-    # ***IMPORTANT***: this evaluation function needs to be written for the new framework. 
-    # For more info, check out the interface in base.py and the example BoolQ implementation in superglue.py. 
-    # Remove this comment when the evaluation code is implemented.
+    def doc_to_target(self, doc):
+        # assumes answer_key is the true-answer's letter
+        return doc['answer_key']
\ No newline at end of file
+
+    def construct_requests(self, ctx):
+        # assumes the output is the predicted-answer's letter
+        ll_a = rf.loglikelihood(ctx, ' a')
+        ll_b = rf.loglikelihood(ctx, ' b')
+        ll_c = rf.loglikelihood(ctx, ' c')
+        ll_d = rf.loglikelihood(ctx, ' d')
+        ll_e = rf.loglikelihood(ctx, ' e')
+
+        return ll_a, ll_b, ll_c, ll_d, ll_e
+
+    def process_results(self, doc, results):
+        predicted_odds = np.array(list(results))
+        gold = doc["answer_key"]
+
+        acc = 1. if np.argmax(predicted_odds) == gold else 0.
+
+        return [
+            {
+                "submetric": "acc",
+                "value": acc,
+                "higher_is_better": True,
+                "aggregation": mean
+            }
+        ]
+
+
+    def evaluate(self, docs, lm):
+        # functionality already implemented above
+        raise NotImplementedError()