Naming update + XLNet/XLM evaluation

examples/run_squad.py

@@ -17,7 +17,7 @@
 
 from __future__ import absolute_import, division, print_function
 from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor, SquadResult
-from transformers.data.metrics.squad_metrics import compute_predictions, compute_predictions_extended, squad_evaluate
+from transformers.data.metrics.squad_metrics import compute_predictions_logits, compute_predictions_log_probs, squad_evaluate
 
 import argparse
 import logging
@@ -264,13 +264,13 @@ def evaluate(args, model, tokenizer, prefix=""):
 
     if args.model_type in ['xlnet', 'xlm']:
         # XLNet uses a more complex post-processing procedure
-        predictions = compute_predictions_extended(examples, features, all_results, args.n_best_size,
+        predictions = compute_predictions_log_probs(examples, features, all_results, args.n_best_size,
                         args.max_answer_length, output_prediction_file,
                         output_nbest_file, output_null_log_odds_file, args.predict_file,
                         model.config.start_n_top, model.config.end_n_top,
                         args.version_2_with_negative, tokenizer, args.verbose_logging)
     else:
-        predictions = compute_predictions(examples, features, all_results, args.n_best_size,
+        predictions = compute_predictions_logits(examples, features, all_results, args.n_best_size,
                         args.max_answer_length, args.do_lower_case, output_prediction_file,
                         output_nbest_file, output_null_log_odds_file, args.verbose_logging,
                         args.version_2_with_negative, args.null_score_diff_threshold)

transformers/data/metrics/squad_metrics.py

@@ -125,6 +125,53 @@ def merge_eval(main_eval, new_eval, prefix):
         main_eval['%s_%s' % (prefix, k)] = new_eval[k]
 
 
+def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
+    num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
+    cur_score = num_no_ans
+    best_score = cur_score
+    best_thresh = 0.0
+    qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+    for i, qid in enumerate(qid_list):
+        if qid not in scores:
+            continue
+        if qid_to_has_ans[qid]:
+            diff = scores[qid]
+        else:
+            if preds[qid]:
+                diff = -1
+            else:
+                diff = 0
+        cur_score += diff
+        if cur_score > best_score:
+            best_score = cur_score
+            best_thresh = na_probs[qid]
+
+    has_ans_score, has_ans_cnt = 0, 0
+    for qid in qid_list:
+        if not qid_to_has_ans[qid]:
+            continue
+        has_ans_cnt += 1
+
+        if qid not in scores:
+            continue
+        has_ans_score += scores[qid]
+
+    return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
+
+
+def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
+    best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(
+        preds, exact_raw, na_probs, qid_to_has_ans)
+    best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(
+        preds, f1_raw, na_probs, qid_to_has_ans)
+    main_eval['best_exact'] = best_exact
+    main_eval['best_exact_thresh'] = exact_thresh
+    main_eval['best_f1'] = best_f1
+    main_eval['best_f1_thresh'] = f1_thresh
+    main_eval['has_ans_exact'] = has_ans_exact
+    main_eval['has_ans_f1'] = has_ans_f1
+
+
 def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
     num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
     cur_score = num_no_ans
@@ -318,10 +365,20 @@ def _compute_softmax(scores):
     return probs
 
 
-def compute_predictions(all_examples, all_features, all_results, n_best_size,
-                      max_answer_length, do_lower_case, output_prediction_file,
-                      output_nbest_file, output_null_log_odds_file, verbose_logging,
-                      version_2_with_negative, null_score_diff_threshold):
+def compute_predictions_logits(
+    all_examples,
+    all_features,
+    all_results,
+    n_best_size,
+    max_answer_length,
+    do_lower_case,
+    output_prediction_file,
+    output_nbest_file,
+    output_null_log_odds_file,
+    verbose_logging,
+    version_2_with_negative,
+    null_score_diff_threshold
+):
     """Write final predictions to the json file and log-odds of null if needed."""
     logger.info("Writing predictions to: %s" % (output_prediction_file))
     logger.info("Writing nbest to: %s" % (output_nbest_file))
@@ -453,7 +510,7 @@ def compute_predictions(all_examples, all_features, all_results, n_best_size,
 
             # In very rare edge cases we could only have single null prediction.
             # So we just create a nonce prediction in this case to avoid failure.
-            if len(nbest)==1:
+            if len(nbest) == 1:
                 nbest.insert(0,
                              _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
 
@@ -512,12 +569,22 @@ def compute_predictions(all_examples, all_features, all_results, n_best_size,
     return all_predictions
 
 
-def compute_predictions_extended(all_examples, all_features, all_results, n_best_size,
-                                max_answer_length, output_prediction_file,
+def compute_predictions_log_probs(
+    all_examples,
+    all_features,
+    all_results,
+    n_best_size,
+    max_answer_length,
+    output_prediction_file,
     output_nbest_file,
-                                output_null_log_odds_file, orig_data_file,
-                                start_n_top, end_n_top, version_2_with_negative,
-                                tokenizer, verbose_logging):
+    output_null_log_odds_file,
+    orig_data_file,
+    start_n_top,
+    end_n_top,
+    version_2_with_negative,
+    tokenizer,
+    verbose_logging
+):
     """ XLNet write prediction logic (more complex than Bert's).
         Write final predictions to the json file and log-odds of null if needed.