DataParallel for SQuAD + fix XLM

examples/run_squad.py

@@ -299,10 +299,14 @@ def evaluate(args, model, tokenizer, prefix=""):
 
     # XLNet and XLM use a more complex post-processing procedure
     if args.model_type in ['xlnet', 'xlm']:
+
+        start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top
+        end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top
+
         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,
-                        model.config.start_n_top, model.config.end_n_top,
+                        start_n_top, end_n_top,
                         args.version_2_with_negative, tokenizer, args.verbose_logging)
     else:
         predictions = compute_predictions_logits(examples, features, all_results, args.n_best_size,

transformers/data/metrics/squad_metrics.py

@@ -695,7 +695,12 @@ def compute_predictions_log_probs(
             tok_text = " ".join(tok_text.split())
             orig_text = " ".join(orig_tokens)
 
-            final_text = get_final_text(tok_text, orig_text, tokenizer.do_lower_case,
+            if hasattr(tokenizer, "do_lower_case"):
+                do_lower_case = tokenizer.do_lower_case
+            else:
+                do_lower_case = tokenizer.do_lowercase_and_remove_accent
+
+            final_text = get_final_text(tok_text, orig_text, do_lower_case,
                                         verbose_logging)
 
             if final_text in seen_predictions:

transformers/tokenization_xlm.py

@@ -549,6 +549,10 @@ class XLMTokenizer(PreTrainedTokenizer):
                                            additional_special_tokens=additional_special_tokens,
                                            **kwargs)
 
+
+        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens
+        self.max_len_sentences_pair = self.max_len - 3  # take into account special tokens 
+
         # cache of sm.MosesPunctNormalizer instance
         self.cache_moses_punct_normalizer = dict()
         # cache of sm.MosesTokenizer instance