Add multi-class, multi-label and regression to transformers (#11012)

* add to  bert

* review comments

* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* self.config.problem_type

* fix style

* fix

* fin

* fix

* update doc

* fix

* test

* Test more problem types

* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix

* remove

* fix

* quality

* make fix-copies

* remove test
Co-authored-by: abhishek thakur <abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

tests/test_modeling_distilbert.py

@@ -211,6 +211,7 @@ class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
     test_pruning = True
     test_torchscript = True
     test_resize_embeddings = True
+    test_sequence_classification_problem_types = True
 
     def setUp(self):
         self.model_tester = DistilBertModelTester(self)

tests/test_modeling_electra.py

@@ -287,6 +287,7 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    test_sequence_classification_problem_types = True
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/test_modeling_funnel.py

@@ -360,6 +360,7 @@ class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    test_sequence_classification_problem_types = True
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/test_modeling_longformer.py

@@ -274,6 +274,7 @@ class LongformerModelTester:
 class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
     test_pruning = False  # pruning is not supported
     test_torchscript = False
+    test_sequence_classification_problem_types = True
 
     all_model_classes = (
         (

tests/test_modeling_mobilebert.py

@@ -267,6 +267,7 @@ class MobileBertModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    test_sequence_classification_problem_types = True
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/test_modeling_reformer.py

@@ -590,6 +590,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, Mod
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
+    test_sequence_classification_problem_types = True
 
     def prepare_kwargs(self):
         return {

tests/test_modeling_roberta.py

@@ -351,6 +351,7 @@ class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCas
         else ()
     )
     all_generative_model_classes = (RobertaForCausalLM,) if is_torch_available() else ()
+    test_sequence_classification_problem_types = True
 
     def setUp(self):
         self.model_tester = RobertaModelTester(self)

tests/test_modeling_squeezebert.py

@@ -231,6 +231,7 @@ class SqueezeBertModelTest(ModelTesterMixin, unittest.TestCase):
     test_torchscript = True
     test_resize_embeddings = True
     test_head_masking = False
+    test_sequence_classification_problem_types = True
 
     def setUp(self):
         self.model_tester = SqueezeBertModelTester(self)

tests/test_modeling_xlm.py

@@ -349,6 +349,7 @@ class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
     all_generative_model_classes = (
         (XLMWithLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
+    test_sequence_classification_problem_types = True
 
     # XLM has 2 QA models -> need to manually set the correct labels for one of them here
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

tests/test_modeling_xlnet.py

@@ -526,6 +526,7 @@ class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase)
         (XLNetLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     test_pruning = False
+    test_sequence_classification_problem_types = True
 
     # XLNet has 2 QA models -> need to manually set the correct labels for one of them here
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):