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>

src/transformers/configuration_utils.py

@@ -163,6 +163,14 @@ class PretrainedConfig(PushToHubMixin):
           typically for a classification task.
         - **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for the
           current task.
+        - **problem_type** (:obj:`str`, `optional`) -- Problem type for :obj:`XxxForSequenceClassification` models. Can
+          be one of (:obj:`"regression"`, :obj:`"single_label_classification"`, :obj:`"multi_label_classification"`).
+          Please note that this parameter is only available in the following models: `AlbertForSequenceClassification`,
+          `BertForSequenceClassification`, `BigBirdForSequenceClassification`, `ConvBertForSequenceClassification`,
+          `DistilBertForSequenceClassification`, `ElectraForSequenceClassification`, `FunnelForSequenceClassification`,
+          `LongformerForSequenceClassification`, `MobileBertForSequenceClassification`,
+          `ReformerForSequenceClassification`, `RobertaForSequenceClassification`,
+          `SqueezeBertForSequenceClassification`, `XLMForSequenceClassification` and `XLNetForSequenceClassification`.
 
     Parameters linked to the tokenizer
 
@@ -260,6 +268,15 @@ class PretrainedConfig(PushToHubMixin):
         # task specific arguments
         self.task_specific_params = kwargs.pop("task_specific_params", None)
 
+        # regression / multi-label classification
+        self.problem_type = kwargs.pop("problem_type", None)
+        allowed_problem_types = ("regression", "single_label_classification", "multi_label_classification")
+        if self.problem_type is not None and self.problem_type not in allowed_problem_types:
+            raise ValueError(
+                f"The config parameter `problem_type` wasnot understood: received {self.problem_type}"
+                "but only 'regression', 'single_label_classification' and 'multi_label_classification' are valid."
+            )
+
         # TPU arguments
         if kwargs.pop("xla_device", None) is not None:
             logger.warning(

src/transformers/models/albert/modeling_albert.py

@@ -21,7 +21,7 @@ from typing import Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -970,6 +970,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.albert = AlbertModel(config)
         self.dropout = nn.Dropout(config.classifier_dropout_prob)
@@ -1024,13 +1025,23 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/bert/modeling_bert.py

@@ -25,7 +25,7 @@ from typing import Optional, Tuple
 import torch
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -1381,7 +1381,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
-        **kwargs
+        **kwargs,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1463,6 +1463,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.bert = BertModel(config)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -1517,14 +1518,23 @@ class BertForSequenceClassification(BertPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
         if not return_dict:
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output

src/transformers/models/big_bird/modeling_big_bird.py

@@ -25,7 +25,7 @@ import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -2609,6 +2609,7 @@ class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
         self.bert = BigBirdModel(config)
         self.classifier = BigBirdClassificationHead(config)
 
@@ -2659,13 +2660,23 @@ class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/convbert/modeling_convbert.py

@@ -22,7 +22,7 @@ from operator import attrgetter
 import torch
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, get_activation
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
@@ -962,6 +962,7 @@ class ConvBertForSequenceClassification(ConvBertPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
         self.convbert = ConvBertModel(config)
         self.classifier = ConvBertClassificationHead(config)
 
@@ -1012,13 +1013,23 @@ class ConvBertForSequenceClassification(ConvBertPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[1:]

src/transformers/models/distilbert/modeling_distilbert.py

@@ -24,7 +24,7 @@ import math
 import numpy as np
 import torch
 import torch.nn as nn
-from torch.nn import CrossEntropyLoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import gelu
 from ...file_utils import (
@@ -579,6 +579,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.distilbert = DistilBertModel(config)
         self.pre_classifier = nn.Linear(config.dim, config.dim)
@@ -631,12 +632,23 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                loss_fct = nn.MSELoss()
+                    self.config.problem_type = "regression"
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
                 else:
-                loss_fct = nn.CrossEntropyLoss()
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + distilbert_output[1:]

src/transformers/models/electra/modeling_electra.py

@@ -22,7 +22,7 @@ from typing import Optional, Tuple
 import torch
 import torch.nn as nn
 import torch.utils.checkpoint
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, get_activation
 from ...file_utils import (
@@ -903,6 +903,7 @@ class ElectraForSequenceClassification(ElectraPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
         self.electra = ElectraModel(config)
         self.classifier = ElectraClassificationHead(config)
 
@@ -953,13 +954,23 @@ class ElectraForSequenceClassification(ElectraPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + discriminator_hidden_states[1:]

src/transformers/models/funnel/modeling_funnel.py

@@ -21,7 +21,7 @@ from typing import Optional, Tuple
 import numpy as np
 import torch
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
 
 from ...activations import ACT2FN
@@ -1240,6 +1240,7 @@ class FunnelForSequenceClassification(FunnelPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.funnel = FunnelBaseModel(config)
         self.classifier = FunnelClassificationHead(config, config.num_labels)
@@ -1287,13 +1288,23 @@ class FunnelForSequenceClassification(FunnelPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[1:]

src/transformers/models/longformer/modeling_longformer.py

@@ -21,7 +21,7 @@ from typing import Optional, Tuple
 import torch
 import torch.nn as nn
 import torch.utils.checkpoint
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
 
 from ...activations import ACT2FN, gelu
@@ -1803,6 +1803,7 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.longformer = LongformerModel(config, add_pooling_layer=False)
         self.classifier = LongformerClassificationHead(config)
@@ -1861,13 +1862,23 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/mobilebert/modeling_mobilebert.py

@@ -29,7 +29,7 @@ from typing import Optional, Tuple
 import torch
 import torch.nn.functional as F
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -1214,6 +1214,7 @@ class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.mobilebert = MobileBertModel(config)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -1268,14 +1269,23 @@ class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
         if not return_dict:
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output

src/transformers/models/reformer/modeling_reformer.py

@@ -26,7 +26,7 @@ import numpy as np
 import torch
 from torch import nn
 from torch.autograd.function import Function
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -366,7 +366,7 @@ class LSHSelfAttention(nn.Module, EfficientAttentionMixin):
         past_buckets_states=None,
         use_cache=False,
         output_attentions=False,
-        **kwargs
+        **kwargs,
     ):
         sequence_length = hidden_states.shape[1]
         batch_size = hidden_states.shape[0]
@@ -1045,7 +1045,7 @@ class LocalSelfAttention(nn.Module, EfficientAttentionMixin):
         past_buckets_states=None,
         use_cache=False,
         output_attentions=False,
-        **kwargs
+        **kwargs,
     ):
         sequence_length = hidden_states.shape[1]
         batch_size = hidden_states.shape[0]
@@ -2381,6 +2381,7 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.reformer = ReformerModel(config)
         self.classifier = ReformerClassificationHead(config)
@@ -2434,13 +2435,23 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/roberta/modeling_roberta.py

@@ -20,7 +20,7 @@ import math
 import torch
 import torch.nn as nn
 import torch.utils.checkpoint
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
 from ...file_utils import (
@@ -1117,6 +1117,7 @@ class RobertaForSequenceClassification(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.roberta = RobertaModel(config, add_pooling_layer=False)
         self.classifier = RobertaClassificationHead(config)
@@ -1167,13 +1168,23 @@ class RobertaForSequenceClassification(RobertaPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/squeezebert/modeling_squeezebert.py

@@ -19,7 +19,7 @@ import math
 
 import torch
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
@@ -733,6 +733,7 @@ class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.transformer = SqueezeBertModel(config)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
@@ -787,13 +788,23 @@ class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + outputs[2:]

src/transformers/models/xlm/modeling_xlm.py

@@ -24,7 +24,7 @@ from typing import Optional, Tuple
 import numpy as np
 import torch
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
 
 from ...activations import gelu
@@ -779,6 +779,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.transformer = XLMModel(config)
         self.sequence_summary = SequenceSummary(config)
@@ -836,13 +837,23 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + transformer_outputs[1:]

src/transformers/models/xlnet/modeling_xlnet.py

@@ -22,7 +22,7 @@ from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
 
 from ...activations import ACT2FN
@@ -1488,6 +1488,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
+        self.config = config
 
         self.transformer = XLNetModel(config)
         self.sequence_summary = SequenceSummary(config)
@@ -1551,13 +1552,23 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
 
         loss = None
         if labels is not None:
+            if self.config.problem_type is None:
                 if self.num_labels == 1:
-                #  We are doing regression
+                    self.config.problem_type = "regression"
-                loss_fct = MSELoss()
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                loss = loss_fct(logits.view(-1), labels.view(-1))
+                    self.config.problem_type = "single_label_classification"
                 else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels)
+            elif self.config.problem_type == "single_label_classification":
                 loss_fct = CrossEntropyLoss()
                 loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
 
         if not return_dict:
             output = (logits,) + transformer_outputs[1:]

tests/test_modeling_albert.py

@@ -230,6 +230,8 @@ class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
         else ()
     )
 
+    test_sequence_classification_problem_types = True
+
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

tests/test_modeling_bert.py

@@ -439,6 +439,7 @@ class BertModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
         else ()
     )
     all_generative_model_classes = (BertLMHeadModel,) 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_big_bird.py

@@ -433,6 +433,7 @@ class BigBirdModelTest(ModelTesterMixin, unittest.TestCase):
     # head masking & pruning is currently not supported for big bird
     test_head_masking = False
     test_pruning = False
+    test_sequence_classification_problem_types = True
 
     # torchscript should be possible, but takes prohibitively long to test.
     # Also torchscript is not an important feature to have in the beginning.

tests/test_modeling_common.py

@@ -89,6 +89,7 @@ class ModelTesterMixin:
     test_missing_keys = True
     test_model_parallel = False
     is_encoder_decoder = False
+    test_sequence_classification_problem_types = False
 
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = copy.deepcopy(inputs_dict)
@@ -1238,6 +1239,42 @@ class ModelTesterMixin:
             model.parallelize()
             model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
 
+    def test_problem_types(self):
+        if not self.test_sequence_classification_problem_types:
+            return
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        problem_types = [
+            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
+            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
+            {"title": "regression", "num_labels": 1, "dtype": torch.float},
+        ]
+
+        for model_class in self.all_model_classes:
+            if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
+                continue
+
+            for problem_type in problem_types:
+                with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
+
+                    config.problem_type = problem_type["title"]
+                    config.num_labels = problem_type["num_labels"]
+
+                    model = model_class(config)
+                    model.to(torch_device)
+                    model.train()
+
+                    inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+
+                    if problem_type["num_labels"] > 1:
+                        inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
+
+                    inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
+
+                    loss = model(**inputs).loss
+                    loss.backward()
+
 
 global_rng = random.Random()
 

tests/test_modeling_convbert.py

@@ -260,6 +260,7 @@ class ConvBertModelTest(ModelTesterMixin, unittest.TestCase):
     )
     test_pruning = False
     test_head_masking = False
+    test_sequence_classification_problem_types = True
 
     def setUp(self):
         self.model_tester = ConvBertModelTester(self)

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):