Adding [T5/MT5/UMT5]ForTokenClassification

docs/source/en/model_doc/mt5.md

@@ -101,6 +101,10 @@ See [`T5TokenizerFast`] for all details.
 
 [[autodoc]] MT5ForSequenceClassification
 
+## MT5ForTokenClassification
+
+[[autodoc]] MT5ForTokenClassification
+
 ## MT5ForQuestionAnswering
 
 [[autodoc]] MT5ForQuestionAnswering

docs/source/en/model_doc/t5.md

@@ -402,6 +402,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] T5ForSequenceClassification
     - forward
 
+## T5ForTokenClassification
+
+[[autodoc]] T5ForTokenClassification
+    - forward
+
 ## T5ForQuestionAnswering
 
 [[autodoc]] T5ForQuestionAnswering

docs/source/en/model_doc/umt5.md

@@ -100,6 +100,11 @@ Refer to [T5's documentation page](t5) for more tips, code examples and notebook
 [[autodoc]] UMT5ForSequenceClassification
     - forward
 
+## UMT5ForTokenClassification
+
+[[autodoc]] UMT5ForTokenClassification
+    - forward
+
 ## UMT5ForQuestionAnswering
 
 [[autodoc]] UMT5ForQuestionAnswering

docs/source/en/tasks/token_classification.md

@@ -32,7 +32,7 @@ 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), [BROS](../model_doc/bros), [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), [Falcon](../model_doc/falcon), [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), [GPT NeoX](../model_doc/gpt_neox), [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), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Phi](../model_doc/phi), [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), [BROS](../model_doc/bros), [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), [Falcon](../model_doc/falcon), [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), [GPT NeoX](../model_doc/gpt_neox), [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), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Phi](../model_doc/phi), [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), [T5](../model_doc/t5), [UMT5](../model_doc/umt5), [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

@@ -2731,6 +2731,7 @@
             "MT5ForConditionalGeneration",
             "MT5ForQuestionAnswering",
             "MT5ForSequenceClassification",
+            "MT5ForTokenClassification",
             "MT5Model",
             "MT5PreTrainedModel",
         ]
@@ -3299,6 +3300,7 @@
             "T5ForConditionalGeneration",
             "T5ForQuestionAnswering",
             "T5ForSequenceClassification",
+            "T5ForTokenClassification",
             "T5Model",
             "T5PreTrainedModel",
             "load_tf_weights_in_t5",
@@ -3370,6 +3372,7 @@
             "UMT5ForConditionalGeneration",
             "UMT5ForQuestionAnswering",
             "UMT5ForSequenceClassification",
+            "UMT5ForTokenClassification",
             "UMT5Model",
             "UMT5PreTrainedModel",
         ]
@@ -7223,6 +7226,7 @@
             MT5ForConditionalGeneration,
             MT5ForQuestionAnswering,
             MT5ForSequenceClassification,
+            MT5ForTokenClassification,
             MT5Model,
             MT5PreTrainedModel,
         )
@@ -7688,6 +7692,7 @@
             T5ForConditionalGeneration,
             T5ForQuestionAnswering,
             T5ForSequenceClassification,
+            T5ForTokenClassification,
             T5Model,
             T5PreTrainedModel,
             load_tf_weights_in_t5,
@@ -7743,6 +7748,7 @@
             UMT5ForConditionalGeneration,
             UMT5ForQuestionAnswering,
             UMT5ForSequenceClassification,
+            UMT5ForTokenClassification,
             UMT5Model,
             UMT5PreTrainedModel,
         )

src/transformers/models/auto/modeling_auto.py

@@ -950,6 +950,7 @@
         ("mpnet", "MPNetForTokenClassification"),
         ("mpt", "MptForTokenClassification"),
         ("mra", "MraForTokenClassification"),
+        ("mt5", "MT5ForTokenClassification"),
         ("nezha", "NezhaForTokenClassification"),
         ("nystromformer", "NystromformerForTokenClassification"),
         ("phi", "PhiForTokenClassification"),
@@ -960,6 +961,8 @@
         ("roc_bert", "RoCBertForTokenClassification"),
         ("roformer", "RoFormerForTokenClassification"),
         ("squeezebert", "SqueezeBertForTokenClassification"),
+        ("t5", "T5ForTokenClassification"),
+        ("umt5", "UMT5ForTokenClassification"),
         ("xlm", "XLMForTokenClassification"),
         ("xlm-roberta", "XLMRobertaForTokenClassification"),
         ("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"),

src/transformers/models/mt5/__init__.py

@@ -52,6 +52,7 @@
         "MT5ForConditionalGeneration",
         "MT5ForQuestionAnswering",
         "MT5ForSequenceClassification",
+        "MT5ForTokenClassification",
         "MT5Model",
         "MT5PreTrainedModel",
         "MT5Stack",
@@ -88,6 +89,7 @@
             MT5ForConditionalGeneration,
             MT5ForQuestionAnswering,
             MT5ForSequenceClassification,
+            MT5ForTokenClassification,
             MT5Model,
             MT5PreTrainedModel,
             MT5Stack,

src/transformers/models/mt5/configuration_mt5.py

@@ -71,6 +71,7 @@ class MT5Config(PretrainedConfig):
 
     model_type = "mt5"
     keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
 
     def __init__(
         self,
@@ -97,15 +98,6 @@ def __init__(
         classifier_dropout=0.0,
         **kwargs,
     ):
-        super().__init__(
-            is_encoder_decoder=is_encoder_decoder,
-            tokenizer_class=tokenizer_class,
-            tie_word_embeddings=tie_word_embeddings,
-            pad_token_id=pad_token_id,
-            eos_token_id=eos_token_id,
-            decoder_start_token_id=decoder_start_token_id,
-            **kwargs,
-        )
         self.vocab_size = vocab_size
         self.d_model = d_model
         self.d_kv = d_kv
@@ -139,17 +131,15 @@ def __init__(
         if feed_forward_proj == "gated-gelu":
             self.dense_act_fn = "gelu_new"
 
-    @property
-    def hidden_size(self):
-        return self.d_model
-
-    @property
-    def num_attention_heads(self):
-        return self.num_heads
-
-    @property
-    def num_hidden_layers(self):
-        return self.num_layers
+        super().__init__(
+            is_encoder_decoder=is_encoder_decoder,
+            tokenizer_class=tokenizer_class,
+            tie_word_embeddings=tie_word_embeddings,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            decoder_start_token_id=decoder_start_token_id,
+            **kwargs,
+        )
 
 
 class MT5OnnxConfig(OnnxSeq2SeqConfigWithPast):

src/transformers/models/mt5/modeling_mt5.py

@@ -32,6 +32,7 @@
     Seq2SeqModelOutput,
     Seq2SeqQuestionAnsweringModelOutput,
     Seq2SeqSequenceClassifierOutput,
+    TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
@@ -54,6 +55,19 @@
 _CHECKPOINT_FOR_DOC = "mt5-small"
 
 
+####################################################
+# This dict contains ids and associated url
+# for the pretrained weights provided with the models
+####################################################
+MT5_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "google/mt5-small",
+    "google/mt5-base",
+    "google/mt5-large",
+    "google/mt5-xl",
+    "google/mt5-xxl",
+    # See all mT5 models at https://huggingface.co/models?filter=mt5
+]
+
 PARALLELIZE_DOCSTRING = r"""
     This is an experimental feature and is a subject to change at a moment's notice.
 
@@ -804,6 +818,10 @@ def _init_weights(self, module):
             if hasattr(module, "qa_outputs"):
                 module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
                 module.qa_outputs.bias.data.zero_()
+        elif isinstance(module, MT5ForTokenClassification):
+            if hasattr(module, "classifier"):
+                module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
+                module.classifier.bias.data.zero_()
         elif isinstance(module, MT5ClassificationHead):
             module.dense.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
             if hasattr(module.dense, "bias") and module.dense.bias is not None:
@@ -1334,7 +1352,6 @@ class MT5Model(MT5PreTrainedModel):
 
     model_type = "mt5"
     config_class = MT5Config
-    _keys_to_ignore_on_load_missing = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
     _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
 
@@ -2158,6 +2175,80 @@ def forward(
         )
 
 
+@add_start_docstrings(
+    """
+    MT5 Encoder 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.
+    """,
+    MT5_START_DOCSTRING,
+)
+class MT5ForTokenClassification(MT5PreTrainedModel):
+    _tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.transformer = MT5EncoderModel(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(MT5_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    # Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.forward with T5->MT5
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        Returns:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits, outputs[2:-1])
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
 @add_start_docstrings(
     """
     MT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers

src/transformers/models/t5/__init__.py

@@ -58,6 +58,7 @@
         "load_tf_weights_in_t5",
         "T5ForQuestionAnswering",
         "T5ForSequenceClassification",
+        "T5ForTokenClassification",
     ]
 
 try:
@@ -119,6 +120,7 @@
             T5ForConditionalGeneration,
             T5ForQuestionAnswering,
             T5ForSequenceClassification,
+            T5ForTokenClassification,
             T5Model,
             T5PreTrainedModel,
             load_tf_weights_in_t5,