Fix labels stored in model config for token classification examples (#15482)

* Playing

* Properly set labels in model config for token classification example

* Port to run_ner_no_trainer

* Quality

examples/pytorch/token-classification/run_ner.py

@@ -295,12 +295,15 @@ def main():
         label_list.sort()
         return label_list
 
-    if isinstance(features[label_column_name].feature, ClassLabel):
+    # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere.
+    # Otherwise, we have to get the list of labels manually.
+    labels_are_int = isinstance(features[label_column_name].feature, ClassLabel)
+    if labels_are_int:
         label_list = features[label_column_name].feature.names
-        label_keys = list(range(len(label_list)))
+        label_to_id = {i: i for i in range(len(label_list))}
     else:
         label_list = get_label_list(raw_datasets["train"][label_column_name])
-        label_keys = label_list
+        label_to_id = {l: i for i, l in enumerate(label_list)}
 
     num_labels = len(label_list)
 
@@ -354,21 +357,26 @@ def main():
             "requirement"
         )
 
+    # Model has labels -> use them.
     if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id:
-        label_name_to_id = {k: v for k, v in model.config.label2id.items()}
+        if list(sorted(model.config.label2id.keys())) == list(sorted(label_list)):
-        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
+            # Reorganize `label_list` to match the ordering of the model.
-            label_to_id = {k: int(label_name_to_id[k]) for k in label_keys}
+            if labels_are_int:
+                label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)}
+                label_list = [model.config.id2label[i] for i in range(num_labels)]
+            else:
+                label_list = [model.config.id2label[i] for i in range(num_labels)]
+                label_to_id = {l: i for i, l in enumerate(label_list)}
         else:
             logger.warning(
                 "Your model seems to have been trained with labels, but they don't match the dataset: ",
-                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
+                f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels: {list(sorted(label_list))}."
                 "\nIgnoring the model labels as a result.",
             )
-    else:
-        label_to_id = {k: i for i, k in enumerate(label_keys)}
 
-    model.config.label2id = label_to_id
+    # Set the correspondences label/ID inside the model config
-    model.config.id2label = {i: l for l, i in label_to_id.items()}
+    model.config.label2id = {l: i for i, l in enumerate(label_list)}
+    model.config.id2label = {i: l for i, l in enumerate(label_list)}
 
     # Map that sends B-Xxx label to its I-Xxx counterpart
     b_to_i_label = []

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -320,12 +320,15 @@ def main():
         label_list.sort()
         return label_list
 
-    if isinstance(features[label_column_name].feature, ClassLabel):
+    # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere.
+    # Otherwise, we have to get the list of labels manually.
+    labels_are_int = isinstance(features[label_column_name].feature, ClassLabel)
+    if labels_are_int:
         label_list = features[label_column_name].feature.names
-        label_keys = list(range(len(label_list)))
+        label_to_id = {i: i for i in range(len(label_list))}
     else:
         label_list = get_label_list(raw_datasets["train"][label_column_name])
-        label_keys = label_list
+        label_to_id = {l: i for i, l in enumerate(label_list)}
 
     num_labels = len(label_list)
 
@@ -365,21 +368,26 @@ def main():
 
     model.resize_token_embeddings(len(tokenizer))
 
+    # Model has labels -> use them.
     if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id:
-        label_name_to_id = {k: v for k, v in model.config.label2id.items()}
+        if list(sorted(model.config.label2id.keys())) == list(sorted(label_list)):
-        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
+            # Reorganize `label_list` to match the ordering of the model.
-            label_to_id = {k: int(label_name_to_id[k]) for k in label_keys}
+            if labels_are_int:
+                label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)}
+                label_list = [model.config.id2label[i] for i in range(num_labels)]
+            else:
+                label_list = [model.config.id2label[i] for i in range(num_labels)]
+                label_to_id = {l: i for i, l in enumerate(label_list)}
         else:
             logger.warning(
                 "Your model seems to have been trained with labels, but they don't match the dataset: ",
-                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
+                f"model labels: {list(sorted(model.config.label2id.keys()))}, dataset labels: {list(sorted(label_list))}."
                 "\nIgnoring the model labels as a result.",
             )
-    else:
-        label_to_id = {k: i for i, k in enumerate(label_keys)}
 
-    model.config.label2id = label_to_id
+    # Set the correspondences label/ID inside the model config
-    model.config.id2label = {i: l for l, i in label_to_id.items()}
+    model.config.label2id = {l: i for i, l in enumerate(label_list)}
+    model.config.id2label = {i: l for i, l in enumerate(label_list)}
 
     # Map that sends B-Xxx label to its I-Xxx counterpart
     b_to_i_label = []