Merge pull request huggingface#31 from stevezheng23/dev/zheng/coqa

update coqa ensemble runner & data processing pipeline
stevezheng23 · Nov 14, 2019 · b5188b8 · b5188b8
2 parents 161edaf + 5fd04a6
commit b5188b8
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Showing 2 changed files with 6 additions and 7 deletions.
diff --git a/examples/run_coqa_ensemble.py b/examples/run_coqa_ensemble.py
@@ -45,9 +45,9 @@
 
 from transformers import AdamW, WarmupLinearSchedule
 
-from utils_coqa_kd import (read_coqa_examples, convert_examples_to_features,
-                           RawResult, write_predictions, write_predictions_v2,
-                           RawResultExtended, write_predictions_extended, InputFeatures)
+from utils_coqa_ensemble import (read_coqa_examples, convert_examples_to_features,
+                                 RawResult, write_predictions, write_predictions_v2,
+                                 RawResultExtended, write_predictions_extended, InputFeatures)
 
 # The follwing import is the official CoQA evaluation script (2.0).
 # You can remove it from the dependencies if you are using this script outside of the library

diff --git a/examples/utils_coqa.py b/examples/utils_coqa.py
@@ -1291,17 +1291,16 @@ def write_predictions_v2(all_examples, all_features, all_results, n_best_size,
                 final_text = pred.answer_type
 
                 if final_text in ["option_a", "option_b"]:
-                    norm_question_tokens = CoQAEvaluator.normalize_answer(example.question_text).split(" ")
+                    question_text = example.question_text.split("<q>")[-1]
+                    norm_question_tokens = CoQAEvaluator.normalize_answer(question_text).split(" ")
                     if "or" in norm_question_tokens:
                         index = norm_question_tokens.index("or")
                         if index-1 >= 0 and index+1 < len(norm_question_tokens):
                             if final_text == "option_a":
                                 final_text = norm_question_tokens[index-1]
                             if final_text == "option_b":
                                 final_text = norm_question_tokens[index+1]
-
-                seen_predictions[final_text] = True
-
+
             nbest.append(
                 _NbestPrediction(
                     text=final_text,