added GPTNeoForTokenClassification

docs/source/en/model_doc/gpt_neo.mdx

@@ -74,6 +74,11 @@ The `generate()` method can be used to generate text using GPT Neo model.
 [[autodoc]] GPTNeoForSequenceClassification
     - forward
 
+## GPTNeoForTokenClassification
+
+[[autodoc]] GPTNeoForTokenClassification
+    - forward
+
 ## FlaxGPTNeoModel
 
 [[autodoc]] FlaxGPTNeoModel

docs/source/en/tasks/token_classification.mdx

@@ -28,7 +28,7 @@ The task illustrated in this tutorial is supported by the following model archit
 
 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
 
-[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
+[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
 
 <!--End of the generated tip-->
 

src/transformers/__init__.py

@@ -1687,6 +1687,7 @@
             "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
             "GPTNeoForCausalLM",
             "GPTNeoForSequenceClassification",
+            "GPTNeoForTokenClassification",
             "GPTNeoModel",
             "GPTNeoPreTrainedModel",
             "load_tf_weights_in_gpt_neo",
@@ -5221,6 +5222,7 @@
             GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoForCausalLM,
             GPTNeoForSequenceClassification,
+            GPTNeoForTokenClassification,
             GPTNeoModel,
             GPTNeoPreTrainedModel,
             load_tf_weights_in_gpt_neo,

src/transformers/models/auto/modeling_auto.py

@@ -814,6 +814,7 @@
         ("gpt-sw3", "GPT2ForTokenClassification"),
         ("gpt2", "GPT2ForTokenClassification"),
         ("gpt_bigcode", "GPTBigCodeForTokenClassification"),
+        ("gpt_neo", "GPTNeoForTokenClassification"),
         ("ibert", "IBertForTokenClassification"),
         ("layoutlm", "LayoutLMForTokenClassification"),
         ("layoutlmv2", "LayoutLMv2ForTokenClassification"),

src/transformers/models/gpt_neo/__init__.py

@@ -30,6 +30,7 @@
         "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoForCausalLM",
         "GPTNeoForSequenceClassification",
+        "GPTNeoForTokenClassification",
         "GPTNeoModel",
         "GPTNeoPreTrainedModel",
         "load_tf_weights_in_gpt_neo",
@@ -61,6 +62,7 @@
             GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoForCausalLM,
             GPTNeoForSequenceClassification,
+            GPTNeoForTokenClassification,
             GPTNeoModel,
             GPTNeoPreTrainedModel,
             load_tf_weights_in_gpt_neo,

src/transformers/models/gpt_neo/configuration_gpt_neo.py

@@ -66,6 +66,10 @@ class GPTNeoConfig(PretrainedConfig):
             The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
+        classifier_dropout (`float`, *optional*, defaults to 0.1):
+            Argument used when doing token classification, used in the model [`GPTNeoForTokenClassification`].
+
+            The dropout ratio for the hidden layer.
         max_position_embeddings (`int`, *optional*, defaults to 2048):
             The maximum sequence length that this model might ever be used with. Typically set this to something large
             just in case (e.g., 512 or 1024 or 2048).
@@ -111,6 +115,7 @@ def __init__(
         resid_dropout=0.0,
         embed_dropout=0.0,
         attention_dropout=0.0,
+        classifier_dropout=0.1,
         layer_norm_epsilon=1e-5,
         initializer_range=0.02,
         use_cache=True,
@@ -129,6 +134,7 @@ def __init__(
         self.resid_dropout = resid_dropout
         self.embed_dropout = embed_dropout
         self.attention_dropout = attention_dropout
+        self.classifier_dropout = classifier_dropout
         self.layer_norm_epsilon = layer_norm_epsilon
         self.initializer_range = initializer_range
         self.use_cache = use_cache

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -30,6 +30,7 @@
     CausalLMOutputWithCrossAttentions,
     CausalLMOutputWithPast,
     SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
 from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
@@ -926,3 +927,88 @@ def forward(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    GPT Neo 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.
+    """,
+    GPT_NEO_START_DOCSTRING,
+)
+class GPTNeoForTokenClassification(GPTNeoPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.transformer = GPTNeoModel(config)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint="EleutherAI/gpt-neo-125m",
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        expected_loss=0.25,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = transformer_outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + transformer_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -3273,6 +3273,13 @@ def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
 
+class GPTNeoForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class GPTNeoModel(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/gpt_neo/test_modeling_gpt_neo.py

@@ -35,6 +35,7 @@
         GPT2Tokenizer,
         GPTNeoForCausalLM,
         GPTNeoForSequenceClassification,
+        GPTNeoForTokenClassification,
         GPTNeoModel,
     )
 
@@ -334,6 +335,16 @@ def create_and_check_gpt_neo_for_sequence_classification(
         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_gpt_neo_for_token_classification(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = GPTNeoForTokenClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
     def create_and_check_forward_and_backwards(
         self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
     ):
@@ -374,13 +385,16 @@ def prepare_config_and_inputs_for_common(self):
 @require_torch
 class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (GPTNeoModel, GPTNeoForCausalLM, GPTNeoForSequenceClassification) if is_torch_available() else ()
+        (GPTNeoModel, GPTNeoForCausalLM, GPTNeoForSequenceClassification, GPTNeoForTokenClassification)
+        if is_torch_available()
+        else ()
     )
     all_generative_model_classes = (GPTNeoForCausalLM,) if is_torch_available() else ()
     pipeline_model_mapping = (
         {
             "feature-extraction": GPTNeoModel,
             "text-classification": GPTNeoForSequenceClassification,
+            "token-classification": GPTNeoForTokenClassification,
             "text-generation": GPTNeoForCausalLM,
             "zero-shot": GPTNeoForSequenceClassification,
         }
@@ -428,6 +442,10 @@ def test_gpt_neo_sequence_classification_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gpt_neo_for_sequence_classification(*config_and_inputs)
 
+    def test_gpt_neo_token_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_for_token_classification(*config_and_inputs)
+
     def test_gpt_neo_gradient_checkpointing(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)