FlaubertForTokenClassification (#5644)

* implement FlaubertForTokenClassification as a subclass of XLMForTokenClassification

* fix mapping order

* add the doc

* add common tests

tests/test_modeling_flaubert.py

@@ -31,6 +31,7 @@ if is_torch_available():
         FlaubertForQuestionAnswering,
         FlaubertForQuestionAnsweringSimple,
         FlaubertForSequenceClassification,
+        FlaubertForTokenClassification,
     )
     from transformers.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
 
@@ -294,6 +295,30 @@ class FlaubertModelTester(object):
         self.parent.assertListEqual(list(result["loss"].size()), [])
         self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size])
 
+    def create_and_check_flaubert_token_classif(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_lengths,
+        sequence_labels,
+        token_labels,
+        is_impossible_labels,
+        input_mask,
+    ):
+        config.num_labels = self.num_labels
+        model = FlaubertForTokenClassification(config)
+        model.to(torch_device)
+        model.eval()
+
+        loss, logits = model(input_ids, attention_mask=input_mask, labels=token_labels)
+        result = {
+            "loss": loss,
+            "logits": logits,
+        }
+        self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels])
+        self.check_loss_output(result)
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -320,6 +345,7 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
             FlaubertForQuestionAnswering,
             FlaubertForQuestionAnsweringSimple,
             FlaubertForSequenceClassification,
+            FlaubertForTokenClassification,
         )
         if is_torch_available()
         else ()
@@ -352,6 +378,10 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*config_and_inputs)
 
+    def test_flaubert_token_classif(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_flaubert_token_classif(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: