updated tokenizer loading for addressing reproducibility issues

examples/run_glue.py

@@ -448,13 +448,14 @@ def main():
 
         # Load a trained model and vocabulary that you have fine-tuned
         model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         model.to(args.device)
 
 
     # Evaluation
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
@@ -463,7 +464,6 @@ def main():
         for checkpoint in checkpoints:
             global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
             model = model_class.from_pretrained(checkpoint)
-            tokenizer = tokenizer_class.from_pretrained(checkpoint)
             model.to(args.device)
             result = evaluate(args, model, tokenizer, prefix=global_step)
             result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())