Tf longformer for sequence classification (#8231)

* working on LongformerForSequenceClassification

* add TFLongformerForMultipleChoice

* add TFLongformerForTokenClassification

* use add_start_docstrings_to_model_forward

* test TFLongformerForSequenceClassification

* test TFLongformerForMultipleChoice

* test TFLongformerForTokenClassification

* remove test from repo

* add test and doc for TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerForMultipleChoice

* add requested classes to modeling_tf_auto.py
update dummy_tf_objects
fix tests
fix bugs in requested classes

* pass all tests except test_inputs_embeds

* sync with master

* pass all tests except test_inputs_embeds

* pass all tests

* pass all tests

* work on test_inputs_embeds

* fix style and quality

* make multi choice work

* fix TFLongformerForTokenClassification signature

* fix TFLongformerForMultipleChoice, TFLongformerForSequenceClassification signature

* fix mult choice

* fix mc hint

* fix input embeds

* fix input embeds

* refactor input embeds

* fix copy issue

* apply sylvains changes and clean more
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/model_doc/longformer.rst

@@ -99,21 +99,41 @@ Longformer specific outputs
 .. autoclass:: transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling
     :members: 
 
-.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput
+.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput
     :members: 
 
 .. autoclass:: transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput
     :members: 
 
+.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput
+    :members: 
+
+.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput
+    :members: 
+
+.. autoclass:: transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput
+    :members: 
+
 .. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput
     :members: 
 
 .. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling
     :members: 
 
+.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput
+    :members: 
+
 .. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput
     :members: 
 
+.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput
+    :members: 
+
+.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput
+    :members: 
+
+.. autoclass:: transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput
+    :members: 
 
 LongformerModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -177,3 +197,24 @@ TFLongformerForQuestionAnswering
 .. autoclass:: transformers.TFLongformerForQuestionAnswering
     :members: call
 
+
+TFLongformerForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLongformerForSequenceClassification
+    :members: call
+
+
+TFLongformerForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLongformerForTokenClassification
+    :members: call
+
+
+TFLongformerForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLongformerForMultipleChoice
+    :members: call
+

src/transformers/__init__.py

@@ -766,7 +766,10 @@ if is_tf_available():
     from .models.longformer import (
         TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFLongformerForMaskedLM,
+        TFLongformerForMultipleChoice,
         TFLongformerForQuestionAnswering,
+        TFLongformerForSequenceClassification,
+        TFLongformerForTokenClassification,
         TFLongformerModel,
         TFLongformerSelfAttention,
     )

src/transformers/models/auto/modeling_tf_auto.py

@@ -92,7 +92,10 @@ from ..funnel.modeling_tf_funnel import (
 from ..gpt2.modeling_tf_gpt2 import TFGPT2LMHeadModel, TFGPT2Model
 from ..longformer.modeling_tf_longformer import (
     TFLongformerForMaskedLM,
+    TFLongformerForMultipleChoice,
     TFLongformerForQuestionAnswering,
+    TFLongformerForSequenceClassification,
+    TFLongformerForTokenClassification,
     TFLongformerModel,
 )
 from ..lxmert.modeling_tf_lxmert import TFLxmertForPreTraining, TFLxmertModel
@@ -314,6 +317,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (AlbertConfig, TFAlbertForSequenceClassification),
         (CamembertConfig, TFCamembertForSequenceClassification),
         (XLMRobertaConfig, TFXLMRobertaForSequenceClassification),
+        (LongformerConfig, TFLongformerForSequenceClassification),
         (RobertaConfig, TFRobertaForSequenceClassification),
         (BertConfig, TFBertForSequenceClassification),
         (XLNetConfig, TFXLNetForSequenceClassification),
@@ -353,6 +357,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
         (FlaubertConfig, TFFlaubertForTokenClassification),
         (XLMConfig, TFXLMForTokenClassification),
         (XLMRobertaConfig, TFXLMRobertaForTokenClassification),
+        (LongformerConfig, TFLongformerForTokenClassification),
         (RobertaConfig, TFRobertaForTokenClassification),
         (BertConfig, TFBertForTokenClassification),
         (MobileBertConfig, TFMobileBertForTokenClassification),
@@ -368,6 +373,7 @@ TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
         (CamembertConfig, TFCamembertForMultipleChoice),
         (XLMConfig, TFXLMForMultipleChoice),
         (XLMRobertaConfig, TFXLMRobertaForMultipleChoice),
+        (LongformerConfig, TFLongformerForMultipleChoice),
         (RobertaConfig, TFRobertaForMultipleChoice),
         (BertConfig, TFBertForMultipleChoice),
         (DistilBertConfig, TFDistilBertForMultipleChoice),

src/transformers/models/longformer/__init__.py

@@ -26,7 +26,10 @@ if is_tf_available():
     from .modeling_tf_longformer import (
         TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFLongformerForMaskedLM,
+        TFLongformerForMultipleChoice,
         TFLongformerForQuestionAnswering,
+        TFLongformerForSequenceClassification,
+        TFLongformerForTokenClassification,
         TFLongformerModel,
         TFLongformerSelfAttention,
     )

src/transformers/models/longformer/modeling_longformer.py

@@ -31,7 +31,6 @@ from ...file_utils import (
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_outputs import MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
 from ...modeling_utils import (
     PreTrainedModel,
     apply_chunking_to_forward,
@@ -151,17 +150,15 @@ class LongformerBaseModelOutputWithPooling(ModelOutput):
 
 
 @dataclass
-class LongformerMultipleChoiceModelOutput(ModelOutput):
+class LongformerMaskedLMOutput(ModelOutput):
     """
-    Base class for outputs of multiple choice Longformer models.
+    Base class for masked language models outputs.
 
     Args:
-        loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
-            Classification loss.
-        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
-            `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
-
-            Classification scores (before SoftMax).
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Masked language modeling (MLM) loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
             Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
             of shape :obj:`(batch_size, sequence_length, hidden_size)`.
@@ -249,6 +246,149 @@ class LongformerQuestionAnsweringModelOutput(ModelOutput):
     global_attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class LongformerSequenceClassifierOutput(ModelOutput):
+    """
+    Base class for outputs of sentence classification models.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, x + attention_window + 1)`, where ``x`` is the number of tokens with global attention
+            mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    global_attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+class LongformerMultipleChoiceModelOutput(ModelOutput):
+    """
+    Base class for outputs of multiple choice Longformer models.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
+            `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
+
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, x + attention_window + 1)`, where ``x`` is the number of tokens with global attention
+            mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    global_attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+class LongformerTokenClassifierOutput(ModelOutput):
+    """
+    Base class for outputs of token classification models.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
+            Classification loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, x + attention_window + 1)`, where ``x`` is the number of tokens with global attention
+            mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, x)`, where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    global_attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 def _get_question_end_index(input_ids, sep_token_id):
     """
     Computes the index of the first occurance of `sep_token_id`.
@@ -1495,7 +1635,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
         return self.lm_head.decoder
 
     @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=LongformerMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         input_ids=None,
@@ -1561,7 +1701,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
             output = (prediction_scores,) + outputs[2:]
             return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
 
-        return MaskedLMOutput(
+        return LongformerMaskedLMOutput(
             loss=masked_lm_loss,
             logits=prediction_scores,
             hidden_states=outputs.hidden_states,
@@ -1593,7 +1733,7 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-base-4096",
-        output_type=SequenceClassifierOutput,
+        output_type=LongformerSequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
     )
     def forward(
@@ -1651,7 +1791,7 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return SequenceClassifierOutput(
+        return LongformerSequenceClassifierOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,
@@ -1837,7 +1977,7 @@ class LongformerForTokenClassification(LongformerPreTrainedModel):
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-base-4096",
-        output_type=TokenClassifierOutput,
+        output_type=LongformerTokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
     )
     def forward(
@@ -1895,7 +2035,7 @@ class LongformerForTokenClassification(LongformerPreTrainedModel):
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output
 
-        return TokenClassifierOutput(
+        return LongformerTokenClassifierOutput(
             loss=loss,
             logits=logits,
             hidden_states=outputs.hidden_states,

src/transformers/models/longformer/modeling_tf_longformer.py

@@ -19,19 +19,21 @@ from typing import Optional, Tuple
 
 import tensorflow as tf
 
-from transformers.activations_tf import get_tf_activation
-
+from ...activations_tf import get_tf_activation
 from ...file_utils import (
+    MULTIPLE_CHOICE_DUMMY_INPUTS,
     ModelOutput,
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
 )
-from ...modeling_tf_outputs import TFMaskedLMOutput, TFQuestionAnsweringModelOutput
 from ...modeling_tf_utils import (
     TFMaskedLanguageModelingLoss,
+    TFMultipleChoiceLoss,
     TFPreTrainedModel,
     TFQuestionAnsweringLoss,
+    TFSequenceClassificationLoss,
+    TFTokenClassificationLoss,
     get_initializer,
     keras_serializable,
     shape_list,
@@ -147,6 +149,52 @@ class TFLongformerBaseModelOutputWithPooling(ModelOutput):
     global_attentions: Optional[Tuple[tf.Tensor]] = None
 
 
+@dataclass
+class TFLongformerMaskedLMOutput(ModelOutput):
+    """
+    Base class for masked language models outputs.
+
+    Args:
+        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Masked language modeling (MLM) loss.
+        logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x +
+            attention_window + 1)`, where ``x`` is the number of tokens with global attention mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x)`,
+            where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[tf.Tensor] = None
+    logits: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+    attentions: Optional[Tuple[tf.Tensor]] = None
+    global_attentions: Optional[Tuple[tf.Tensor]] = None
+
+
 @dataclass
 class TFLongformerQuestionAnsweringModelOutput(ModelOutput):
     """
@@ -196,6 +244,146 @@ class TFLongformerQuestionAnsweringModelOutput(ModelOutput):
     global_attentions: Optional[Tuple[tf.Tensor]] = None
 
 
+@dataclass
+class TFLongformerSequenceClassifierOutput(ModelOutput):
+    """
+    Base class for outputs of sentence classification models.
+
+    Args:
+        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x +
+            attention_window + 1)`, where ``x`` is the number of tokens with global attention mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x)`,
+            where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[tf.Tensor] = None
+    logits: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+    attentions: Optional[Tuple[tf.Tensor]] = None
+    global_attentions: Optional[Tuple[tf.Tensor]] = None
+
+
+@dataclass
+class TFLongformerMultipleChoiceModelOutput(ModelOutput):
+    """
+    Base class for outputs of multiple choice models.
+
+    Args:
+        loss (:obj:`tf.Tensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss.
+        logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
+            `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
+
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x +
+            attention_window + 1)`, where ``x`` is the number of tokens with global attention mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x)`,
+            where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[tf.Tensor] = None
+    logits: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+    attentions: Optional[Tuple[tf.Tensor]] = None
+    global_attentions: Optional[Tuple[tf.Tensor]] = None
+
+
+@dataclass
+class TFLongformerTokenClassifierOutput(ModelOutput):
+    """
+    Base class for outputs of token classification models.
+
+    Args:
+        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
+            Classification loss.
+        logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x +
+            attention_window + 1)`, where ``x`` is the number of tokens with global attention mask.
+
+            Local attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token in the sequence to every token with
+            global attention (first ``x`` values) and to every token in the attention window (remaining
+            ``attention_window + 1`` values). Note that the first ``x`` values refer to tokens with fixed positions in
+            the text, but the remaining ``attention_window + 1`` values refer to tokens with relative positions: the
+            attention weight of a token to itself is located at index ``x + attention_window / 2`` and the
+            ``attention_window / 2`` preceding (succeeding) values are the attention weights to the ``attention_window
+            / 2`` preceding (succeeding) tokens. If the attention window contains a token with global attention, the
+            attention weight at the corresponding index is set to 0; the value should be accessed from the first ``x``
+            attention weights. If a token has global attention, the attention weights to all other tokens in
+            :obj:`attentions` is set to 0, the values should be accessed from :obj:`global_attentions`.
+        global_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, x)`,
+            where ``x`` is the number of tokens with global attention mask.
+
+            Global attentions weights after the attention softmax, used to compute the weighted average in the
+            self-attention heads. Those are the attention weights from every token with global attention to every token
+            in the sequence.
+    """
+
+    loss: Optional[tf.Tensor] = None
+    logits: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+    attentions: Optional[Tuple[tf.Tensor]] = None
+    global_attentions: Optional[Tuple[tf.Tensor]] = None
+
+
 def _compute_global_attention_mask(input_ids_shape, sep_token_indices, before_sep_token=True):
     """
     Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is
@@ -249,18 +437,17 @@ class TFLongformerLMHead(tf.keras.layers.Layer):
 
         super().build(input_shape)
 
-    def call(self, features):
-        x = self.dense(features)
-        x = self.act(x)
-        x = self.layer_norm(x)
+    def call(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.layer_norm(hidden_states)
 
         # project back to size of vocabulary with bias
-        x = self.decoder(x, mode="linear") + self.bias
+        hidden_states = self.decoder(hidden_states, mode="linear") + self.bias
 
-        return x
+        return hidden_states
 
 
-# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaEmbeddings
 class TFLongformerEmbeddings(tf.keras.layers.Layer):
     """
     Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
@@ -304,17 +491,23 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
 
         super().build(input_shape)
 
-    def create_position_ids_from_input_ids(self, x):
+    def create_position_ids_from_input_ids(self, input_ids):
         """
         Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
         symbols are ignored. This is modified from fairseq's `utils.make_positions`.
 
         Args:
-            x: tf.Tensor
+            input_ids: tf.Tensor
 
         Returns: tf.Tensor
         """
-        mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
+        input_ids_shape = shape_list(input_ids)
+
+        # multiple choice has 3 dimensions
+        if len(input_ids_shape) == 3:
+            input_ids = tf.reshape(input_ids, (input_ids_shape[0] * input_ids_shape[1], input_ids_shape[2]))
+
+        mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=tf.int32)
         incremental_indices = tf.math.cumsum(mask, axis=1) * mask
 
         return incremental_indices + self.padding_idx
@@ -1783,7 +1976,7 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-base-4096",
-        output_type=TFMaskedLMOutput,
+        output_type=TFLongformerMaskedLMOutput,
         config_class=_CONFIG_FOR_DOC,
     )
     def call(
@@ -1837,11 +2030,12 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
 
             return ((loss,) + output) if loss is not None else output
 
-        return TFMaskedLMOutput(
+        return TFLongformerMaskedLMOutput(
             loss=loss,
             logits=prediction_scores,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
+            global_attentions=outputs.global_attentions,
         )
 
 
@@ -1871,7 +2065,7 @@ class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAn
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="allenai/longformer-large-4096-finetuned-triviaqa",
-        output_type=TFQuestionAnsweringModelOutput,
+        output_type=TFLongformerQuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
     )
     def call(
@@ -1969,3 +2163,357 @@ class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAn
             attentions=outputs.attentions,
             global_attentions=outputs.global_attentions,
         )
+
+
+class TFLongformerClassificationHead(tf.keras.layers.Layer):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config, **kwargs):
+        super().__init__(**kwargs)
+        self.dense = tf.keras.layers.Dense(
+            config.hidden_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            activation="tanh",
+            name="dense",
+        )
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.out_proj = tf.keras.layers.Dense(
+            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
+        )
+
+    def call(self, hidden_states, training=False):
+        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
+        hidden_states = self.dropout(hidden_states, training=training)
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states, training=training)
+        output = self.out_proj(hidden_states)
+        return output
+
+
+@add_start_docstrings(
+    """
+    Longformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+    pooled output) e.g. for GLUE tasks.
+    """,
+    LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSequenceClassificationLoss):
+
+    authorized_missing_keys = [r"pooler"]
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.num_labels = config.num_labels
+
+        self.longformer = TFLongformerMainLayer(config, name="longformer")
+        self.classifier = TFLongformerClassificationHead(config, name="classifier")
+
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="allenai/longformer-base-4096",
+        output_type=TFLongformerSequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        inputs=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        global_attention_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+        training=False,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
+            position_ids = inputs[3] if len(inputs) > 3 else position_ids
+            global_attention_mask = inputs[4] if len(inputs) > 4 else global_attention_mask
+            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            return_dict = inputs[8] if len(inputs) > 8 else return_dict
+            labels = inputs[9] if len(inputs) > 9 else labels
+            assert len(inputs) <= 10, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            global_attention_mask = inputs.get("global_attention_mask", global_attention_mask)
+            token_type_ids = inputs.get("token_type_ids", token_type_ids)
+            position_ids = inputs.get("position_ids", position_ids)
+            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
+            labels = inputs.get("labels", labels)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            return_dict = inputs.get("return_dict", return_dict)
+            assert len(inputs) <= 10, "Too many inputs."
+        else:
+            input_ids = inputs
+
+        if global_attention_mask is None and input_ids is not None:
+            logger.info("Initializing global attention on CLS token...")
+            # global attention on cls token
+            global_attention_mask = tf.zeros_like(input_ids)
+            global_attention_mask = tf.tensor_scatter_nd_update(
+                global_attention_mask,
+                [[i, 0] for i in range(input_ids.shape[0])],
+                [1 for _ in range(input_ids.shape[0])],
+            )
+
+        outputs = self.longformer(
+            input_ids,
+            attention_mask=attention_mask,
+            global_attention_mask=global_attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output)
+
+        loss = None if labels is None else self.compute_loss(labels, logits)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFLongformerSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            global_attentions=outputs.global_attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Longformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
+    a softmax) e.g. for RocStories/SWAG tasks.
+    """,
+    LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoiceLoss):
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.longformer = TFLongformerMainLayer(config, name="longformer")
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.classifier = tf.keras.layers.Dense(
+            1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+        )
+
+    @property
+    def dummy_inputs(self):
+        input_ids = tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)
+        # make sure global layers are initialized
+        global_attention_mask = tf.constant([[[0, 0, 0, 1], [0, 0, 0, 1]]] * 2)
+        return {"input_ids": input_ids, "global_attention_mask": global_attention_mask}
+
+    @add_start_docstrings_to_model_forward(
+        LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="allenai/longformer-base-4096",
+        output_type=TFLongformerMultipleChoiceModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        inputs,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        global_attention_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+        training=False,
+    ):
+        r"""
+        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
+            num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
+            :obj:`input_ids` above)
+        """
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
+            position_ids = inputs[3] if len(inputs) > 3 else position_ids
+            global_attention_mask = inputs[4] if len(inputs) > 4 else global_attention_mask
+            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            return_dict = inputs[8] if len(inputs) > 8 else return_dict
+            labels = inputs[9] if len(inputs) > 9 else labels
+            assert len(inputs) <= 10, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            global_attention_mask = inputs.get("global_attention_mask", global_attention_mask)
+            token_type_ids = inputs.get("token_type_ids", token_type_ids)
+            position_ids = inputs.get("position_ids", position_ids)
+            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
+            labels = inputs.get("labels", labels)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            return_dict = inputs.get("return_dict", return_dict)
+            assert len(inputs) <= 10, "Too many inputs."
+        else:
+            input_ids = inputs
+
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        if input_ids is not None:
+            num_choices = shape_list(input_ids)[1]
+            seq_length = shape_list(input_ids)[2]
+        else:
+            num_choices = shape_list(inputs_embeds)[1]
+            seq_length = shape_list(inputs_embeds)[2]
+
+        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
+        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+        flat_global_attention_mask = (
+            tf.reshape(global_attention_mask, (-1, global_attention_mask.shape[-1]))
+            if global_attention_mask is not None
+            else None
+        )
+        flat_inputs_embeds = (
+            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.longformer(
+            flat_input_ids,
+            position_ids=flat_position_ids,
+            token_type_ids=flat_token_type_ids,
+            attention_mask=flat_attention_mask,
+            global_attention_mask=flat_global_attention_mask,
+            inputs_embeds=flat_inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = tf.reshape(logits, (-1, num_choices))
+
+        loss = None if labels is None else self.compute_loss(labels, reshaped_logits)
+
+        if not return_dict:
+            output = (reshaped_logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFLongformerMultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            global_attentions=outputs.global_attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Longformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
+    for Named-Entity-Recognition (NER) tasks.
+    """,
+    LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenClassificationLoss):
+
+    authorized_missing_keys = [r"pooler"]
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.longformer = TFLongformerMainLayer(config=config, name="longformer")
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.classifier = tf.keras.layers.Dense(
+            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+        )
+
+    @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="allenai/longformer-base-4096",
+        output_type=TFLongformerTokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        inputs=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        global_attention_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+        training=False,
+    ):
+        r"""
+        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
+            1]``.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.longformer(
+            inputs,
+            attention_mask=attention_mask,
+            global_attention_mask=global_attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+        loss = None if labels is None else self.compute_loss(labels, logits)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFLongformerTokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            global_attentions=outputs.global_attentions,
+        )

src/transformers/models/roberta/modeling_tf_roberta.py

@@ -751,15 +751,15 @@ class TFRobertaLMHead(tf.keras.layers.Layer):
 
         super().build(input_shape)
 
-    def call(self, features):
-        x = self.dense(features)
-        x = self.act(x)
-        x = self.layer_norm(x)
+    def call(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.layer_norm(hidden_states)
 
         # project back to size of vocabulary with bias
-        x = self.decoder(x, mode="linear") + self.bias
+        hidden_states = self.decoder(hidden_states, mode="linear") + self.bias
 
-        return x
+        return hidden_states
 
 
 @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)

src/transformers/utils/dummy_tf_objects.py

@@ -812,6 +812,15 @@ class TFLongformerForMaskedLM:
         requires_tf(self)
 
 
+class TFLongformerForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
 class TFLongformerForQuestionAnswering:
     def __init__(self, *args, **kwargs):
         requires_tf(self)
@@ -821,6 +830,24 @@ class TFLongformerForQuestionAnswering:
         requires_tf(self)
 
 
+class TFLongformerForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
+class TFLongformerForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
 class TFLongformerModel:
     def __init__(self, *args, **kwargs):
         requires_tf(self)

tests/test_modeling_longformer.py

@@ -129,7 +129,7 @@ class LongformerModelTester:
         output_without_mask = model(input_ids)["last_hidden_state"]
         self.parent.assertTrue(torch.allclose(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], atol=1e-4))
 
-    def create_and_check_longformer_model(
+    def create_and_check_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = LongformerModel(config=config)
@@ -141,7 +141,7 @@ class LongformerModelTester:
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
 
-    def create_and_check_longformer_model_with_global_attention_mask(
+    def create_and_check_model_with_global_attention_mask(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = LongformerModel(config=config)
@@ -163,7 +163,7 @@ class LongformerModelTester:
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
 
-    def create_and_check_longformer_for_masked_lm(
+    def create_and_check_for_masked_lm(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = LongformerForMaskedLM(config=config)
@@ -172,7 +172,7 @@ class LongformerModelTester:
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
-    def create_and_check_longformer_for_question_answering(
+    def create_and_check_for_question_answering(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = LongformerForQuestionAnswering(config=config)
@@ -189,7 +189,7 @@ class LongformerModelTester:
         self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
 
-    def create_and_check_longformer_for_sequence_classification(
+    def create_and_check_for_sequence_classification(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.num_labels = self.num_labels
@@ -199,7 +199,7 @@ class LongformerModelTester:
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
 
-    def create_and_check_longformer_for_token_classification(
+    def create_and_check_for_token_classification(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.num_labels = self.num_labels
@@ -209,7 +209,7 @@ class LongformerModelTester:
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
 
-    def create_and_check_longformer_for_multiple_choice(
+    def create_and_check_for_multiple_choice(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.num_choices = self.num_choices
@@ -296,37 +296,37 @@ class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_longformer_model(self):
+    def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_model(*config_and_inputs)
+        self.model_tester.create_and_check_model(*config_and_inputs)
 
-    def test_longformer_model_attention_mask_determinism(self):
+    def test_model_attention_mask_determinism(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_attention_mask_determinism(*config_and_inputs)
 
-    def test_longformer_model_global_attention_mask(self):
+    def test_model_global_attention_mask(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_model_with_global_attention_mask(*config_and_inputs)
+        self.model_tester.create_and_check_model_with_global_attention_mask(*config_and_inputs)
 
-    def test_longformer_for_masked_lm(self):
+    def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_for_masked_lm(*config_and_inputs)
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
 
-    def test_longformer_for_question_answering(self):
+    def test_for_question_answering(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering()
-        self.model_tester.create_and_check_longformer_for_question_answering(*config_and_inputs)
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
 
     def test_for_sequence_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_for_sequence_classification(*config_and_inputs)
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
 
     def test_for_token_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_for_token_classification(*config_and_inputs)
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
 
     def test_for_multiple_choice(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_for_multiple_choice(*config_and_inputs)
+        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
 
 
 @require_torch
@@ -691,7 +691,7 @@ class LongformerModelIntegrationTest(unittest.TestCase):
         )  # long input
         input_ids = input_ids.to(torch_device)
 
-        loss, prediction_scores = model(input_ids, labels=input_ids)
+        loss, prediction_scores = model(input_ids, labels=input_ids).to_tuple()
 
         expected_loss = torch.tensor(0.0074, device=torch_device)
         expected_prediction_scores_sum = torch.tensor(-6.1048e08, device=torch_device)

tests/test_modeling_tf_longformer.py

@@ -29,7 +29,10 @@ if is_tf_available():
     from transformers import (
         LongformerConfig,
         TFLongformerForMaskedLM,
+        TFLongformerForMultipleChoice,
         TFLongformerForQuestionAnswering,
+        TFLongformerForSequenceClassification,
+        TFLongformerForTokenClassification,
         TFLongformerModel,
         TFLongformerSelfAttention,
     )
@@ -130,7 +133,7 @@ class TFLongformerModelTester:
         output_without_mask = model(input_ids)[0]
         tf.debugging.assert_near(output_with_mask[0, 0, :5], output_without_mask[0, 0, :5], rtol=1e-4)
 
-    def create_and_check_longformer_model(
+    def create_and_check_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.return_dict = True
@@ -144,7 +147,7 @@ class TFLongformerModelTester:
         )
         self.parent.assertListEqual(shape_list(result.pooler_output), [self.batch_size, self.hidden_size])
 
-    def create_and_check_longformer_model_with_global_attention_mask(
+    def create_and_check_model_with_global_attention_mask(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.return_dict = True
@@ -172,7 +175,7 @@ class TFLongformerModelTester:
         )
         self.parent.assertListEqual(shape_list(result.pooler_output), [self.batch_size, self.hidden_size])
 
-    def create_and_check_longformer_for_masked_lm(
+    def create_and_check_for_masked_lm(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.return_dict = True
@@ -180,7 +183,7 @@ class TFLongformerModelTester:
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertListEqual(shape_list(result.logits), [self.batch_size, self.seq_length, self.vocab_size])
 
-    def create_and_check_longformer_for_question_answering(
+    def create_and_check_for_question_answering(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.return_dict = True
@@ -196,6 +199,41 @@ class TFLongformerModelTester:
         self.parent.assertListEqual(shape_list(result.start_logits), [self.batch_size, self.seq_length])
         self.parent.assertListEqual(shape_list(result.end_logits), [self.batch_size, self.seq_length])
 
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFLongformerForSequenceClassification(config=config)
+        output = model(
+            input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
+        ).logits
+        self.parent.assertListEqual(shape_list(output), [self.batch_size, self.num_labels])
+
+    def create_and_check_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 = TFLongformerForTokenClassification(config=config)
+        output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels).logits
+        self.parent.assertListEqual(shape_list(output), [self.batch_size, self.seq_length, self.num_labels])
+
+    def create_and_check_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = TFLongformerForMultipleChoice(config=config)
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        output = model(
+            multiple_choice_inputs_ids,
+            attention_mask=multiple_choice_input_mask,
+            global_attention_mask=multiple_choice_input_mask,
+            token_type_ids=multiple_choice_token_type_ids,
+            labels=choice_labels,
+        ).logits
+        self.parent.assertListEqual(list(output.shape), [self.batch_size, self.num_choices])
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -252,6 +290,9 @@ class TFLongformerModelTest(TFModelTesterMixin, unittest.TestCase):
             TFLongformerModel,
             TFLongformerForMaskedLM,
             TFLongformerForQuestionAnswering,
+            TFLongformerForSequenceClassification,
+            TFLongformerForMultipleChoice,
+            TFLongformerForTokenClassification,
         )
         if is_tf_available()
         else ()
@@ -264,25 +305,37 @@ class TFLongformerModelTest(TFModelTesterMixin, unittest.TestCase):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_longformer_model_attention_mask_determinism(self):
+    def test_model_attention_mask_determinism(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_attention_mask_determinism(*config_and_inputs)
 
-    def test_longformer_model(self):
+    def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_model(*config_and_inputs)
+        self.model_tester.create_and_check_model(*config_and_inputs)
 
-    def test_longformer_model_global_attention_mask(self):
+    def test_model_global_attention_mask(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_model_with_global_attention_mask(*config_and_inputs)
+        self.model_tester.create_and_check_model_with_global_attention_mask(*config_and_inputs)
 
-    def test_longformer_for_masked_lm(self):
+    def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_longformer_for_masked_lm(*config_and_inputs)
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
 
-    def test_longformer_for_question_answering(self):
+    def test_for_question_answering(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering()
-        self.model_tester.create_and_check_longformer_for_question_answering(*config_and_inputs)
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
 
     @slow
     def test_saved_model_with_attentions_output(self):