NER support for Albert in run_ner.py and NerPipeline (#2983)

* * Added support for Albert when fine-tuning for NER

* Added support for Albert in NER pipeline

* Added command-line options to examples/ner/run_ner.py to better control tokenization

* Added class AlbertForTokenClassification

* Changed output for NerPipeline to use .convert_ids_to_tokens(...) instead of .decode(...) to better reflect tokens

* Added ,

* Now passes style guide enforcement

* Changes from reviews.

* Code now passes style enforcement

* Added test for AlbertForTokenClassification

* Added test for AlbertForTokenClassification

tests/test_modeling_albert.py

@@ -29,6 +29,7 @@ if is_torch_available():
         AlbertModel,
         AlbertForMaskedLM,
         AlbertForSequenceClassification,
+        AlbertForTokenClassification,
         AlbertForQuestionAnswering,
     )
     from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@@ -207,6 +208,25 @@ class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
             self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
             self.check_loss_output(result)
 
+        def create_and_check_albert_for_token_classification(
+            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+        ):
+            config.num_labels = self.num_labels
+            model = AlbertForTokenClassification(config=config)
+            model.to(torch_device)
+            model.eval()
+            loss, logits = model(
+                input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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()
             (