Use word_ids to get labels in run_ner (#8962)

* Use word_ids to get labels in run_ner

* Add sanity check

examples/token-classification/run_ner.py

@@ -35,6 +35,7 @@ from transformers import (
     AutoTokenizer,
     DataCollatorForTokenClassification,
     HfArgumentParser,
+    PreTrainedTokenizerFast,
     Trainer,
     TrainingArguments,
     set_seed,
@@ -250,6 +251,14 @@ def main():
         cache_dir=model_args.cache_dir,
     )
 
+    # Tokenizer check: this script requires a fast tokenizer.
+    if not isinstance(tokenizer, PreTrainedTokenizerFast):
+        raise ValueError(
+            "This example script only works for models that have a fast tokenizer. Checkout the big table of models "
+            "at https://huggingface.co/transformers/index.html#bigtable to find the model types that meet this "
+            "requirement"
+        )
+
     # Preprocessing the dataset
     # Padding strategy
     padding = "max_length" if data_args.pad_to_max_length else False
@@ -262,28 +271,25 @@ def main():
             truncation=True,
             # We use this argument because the texts in our dataset are lists of words (with a label for each word).
             is_split_into_words=True,
-            return_offsets_mapping=True,
         )
-        offset_mappings = tokenized_inputs.pop("offset_mapping")
         labels = []
-        for label, offset_mapping in zip(examples[label_column_name], offset_mappings):
-            label_index = 0
-            current_label = -100
+        for i, label in enumerate(examples[label_column_name]):
+            word_ids = tokenized_inputs.word_ids(batch_index=i)
+            previous_word_idx = None
             label_ids = []
-            for offset in offset_mapping:
-                # We set the label for the first token of each word. Special characters will have an offset of (0, 0)
-                # so the test ignores them.
-                if offset[0] == 0 and offset[1] != 0:
-                    current_label = label_to_id[label[label_index]]
-                    label_index += 1
-                    label_ids.append(current_label)
-                # For special tokens, we set the label to -100 so it's automatically ignored in the loss function.
-                elif offset[0] == 0 and offset[1] == 0:
+            for word_idx in word_ids:
+                # Special tokens have a word id that is None. We set the label to -100 so they are automatically
+                # ignored in the loss function.
+                if word_idx is None:
                     label_ids.append(-100)
+                # We set the label for the first token of each word.
+                elif word_idx != previous_word_idx:
+                    label_ids.append(label_to_id[label[word_idx]])
                 # For the other tokens in a word, we set the label to either the current label or -100, depending on
                 # the label_all_tokens flag.
                 else:
-                    label_ids.append(current_label if data_args.label_all_tokens else -100)
+                    label_ids.append(label_to_id[label[word_idx]] if data_args.label_all_tokens else -100)
+                previous_word_idx = word_idx
 
             labels.append(label_ids)
         tokenized_inputs["labels"] = labels