Adding [T5/MT5/UMT5]ForTokenClassification (#28443)
* Adding [T5/MT5/UMT5]ForTokenClassification * Add auto mappings for T5ForTokenClassification and variants * Adding ForTokenClassification to the list of models * Adding attention_mask param to the T5ForTokenClassification test * Remove outdated comment in test * Adding EncoderOnly and Token Classification tests for MT5 and UMT5 * Fix typo in umt5 string * Add tests for all the existing MT5 models * Fix wrong comment in dependency_versions_table * Reverting change to common test for _keys_to_ignore_on_load_missing The test is correctly picking up redundant keys in _keys_to_ignore_on_load_missing. * Removing _keys_to_ignore_on_missing from MT5 since the key is not used in the model * Add fix-copies to MT5ModelTest
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@ -101,6 +101,10 @@ See [`T5TokenizerFast`] for all details.
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[[autodoc]] MT5ForSequenceClassification
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## MT5ForTokenClassification
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[[autodoc]] MT5ForTokenClassification
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## MT5ForQuestionAnswering
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[[autodoc]] MT5ForQuestionAnswering
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@ -402,6 +402,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
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[[autodoc]] T5ForSequenceClassification
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- forward
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## T5ForTokenClassification
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[[autodoc]] T5ForTokenClassification
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- forward
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## T5ForQuestionAnswering
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[[autodoc]] T5ForQuestionAnswering
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@ -100,6 +100,11 @@ Refer to [T5's documentation page](t5) for more tips, code examples and notebook
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[[autodoc]] UMT5ForSequenceClassification
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- forward
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## UMT5ForTokenClassification
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[[autodoc]] UMT5ForTokenClassification
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- forward
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## UMT5ForQuestionAnswering
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[[autodoc]] UMT5ForQuestionAnswering
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@ -32,7 +32,7 @@ The task illustrated in this tutorial is supported by the following model archit
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[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)
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[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)
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<!--End of the generated tip-->
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@ -2731,6 +2731,7 @@ else:
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"MT5ForConditionalGeneration",
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"MT5ForQuestionAnswering",
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"MT5ForSequenceClassification",
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"MT5ForTokenClassification",
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"MT5Model",
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"MT5PreTrainedModel",
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]
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@ -3299,6 +3300,7 @@ else:
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"T5ForConditionalGeneration",
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"T5ForQuestionAnswering",
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"T5ForSequenceClassification",
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"T5ForTokenClassification",
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"T5Model",
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"T5PreTrainedModel",
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"load_tf_weights_in_t5",
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@ -3370,6 +3372,7 @@ else:
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"UMT5ForConditionalGeneration",
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"UMT5ForQuestionAnswering",
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"UMT5ForSequenceClassification",
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"UMT5ForTokenClassification",
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"UMT5Model",
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"UMT5PreTrainedModel",
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]
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@ -7223,6 +7226,7 @@ if TYPE_CHECKING:
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MT5ForConditionalGeneration,
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MT5ForQuestionAnswering,
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MT5ForSequenceClassification,
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MT5ForTokenClassification,
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MT5Model,
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MT5PreTrainedModel,
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)
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@ -7688,6 +7692,7 @@ if TYPE_CHECKING:
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T5ForConditionalGeneration,
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T5ForQuestionAnswering,
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T5ForSequenceClassification,
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T5ForTokenClassification,
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T5Model,
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T5PreTrainedModel,
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load_tf_weights_in_t5,
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@ -7743,6 +7748,7 @@ if TYPE_CHECKING:
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UMT5ForConditionalGeneration,
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UMT5ForQuestionAnswering,
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UMT5ForSequenceClassification,
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UMT5ForTokenClassification,
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UMT5Model,
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UMT5PreTrainedModel,
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)
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@ -950,6 +950,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
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("mpnet", "MPNetForTokenClassification"),
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("mpt", "MptForTokenClassification"),
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("mra", "MraForTokenClassification"),
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("mt5", "MT5ForTokenClassification"),
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("nezha", "NezhaForTokenClassification"),
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("nystromformer", "NystromformerForTokenClassification"),
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("phi", "PhiForTokenClassification"),
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@ -960,6 +961,8 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
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("roc_bert", "RoCBertForTokenClassification"),
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("roformer", "RoFormerForTokenClassification"),
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("squeezebert", "SqueezeBertForTokenClassification"),
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("t5", "T5ForTokenClassification"),
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("umt5", "UMT5ForTokenClassification"),
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("xlm", "XLMForTokenClassification"),
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("xlm-roberta", "XLMRobertaForTokenClassification"),
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("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"),
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@ -52,6 +52,7 @@ else:
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"MT5ForConditionalGeneration",
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"MT5ForQuestionAnswering",
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"MT5ForSequenceClassification",
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"MT5ForTokenClassification",
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"MT5Model",
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"MT5PreTrainedModel",
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"MT5Stack",
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@ -88,6 +89,7 @@ if TYPE_CHECKING:
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MT5ForConditionalGeneration,
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MT5ForQuestionAnswering,
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MT5ForSequenceClassification,
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MT5ForTokenClassification,
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MT5Model,
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MT5PreTrainedModel,
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MT5Stack,
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@ -71,6 +71,7 @@ class MT5Config(PretrainedConfig):
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model_type = "mt5"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
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def __init__(
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self,
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classifier_dropout=0.0,
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**kwargs,
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):
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super().__init__(
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is_encoder_decoder=is_encoder_decoder,
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tokenizer_class=tokenizer_class,
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tie_word_embeddings=tie_word_embeddings,
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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decoder_start_token_id=decoder_start_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.d_model = d_model
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self.d_kv = d_kv
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if feed_forward_proj == "gated-gelu":
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self.dense_act_fn = "gelu_new"
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@property
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def hidden_size(self):
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return self.d_model
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@property
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def num_attention_heads(self):
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return self.num_heads
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@property
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def num_hidden_layers(self):
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return self.num_layers
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super().__init__(
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is_encoder_decoder=is_encoder_decoder,
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tokenizer_class=tokenizer_class,
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tie_word_embeddings=tie_word_embeddings,
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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decoder_start_token_id=decoder_start_token_id,
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**kwargs,
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)
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class MT5OnnxConfig(OnnxSeq2SeqConfigWithPast):
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@ -32,6 +32,7 @@ from ...modeling_outputs import (
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Seq2SeqModelOutput,
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Seq2SeqQuestionAnsweringModelOutput,
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Seq2SeqSequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
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_CHECKPOINT_FOR_DOC = "mt5-small"
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####################################################
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# This dict contains ids and associated url
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# for the pretrained weights provided with the models
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####################################################
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MT5_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"google/mt5-small",
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"google/mt5-base",
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"google/mt5-large",
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"google/mt5-xl",
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"google/mt5-xxl",
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# See all mT5 models at https://huggingface.co/models?filter=mt5
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]
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PARALLELIZE_DOCSTRING = r"""
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This is an experimental feature and is a subject to change at a moment's notice.
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@ -804,6 +818,10 @@ class MT5PreTrainedModel(PreTrainedModel):
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if hasattr(module, "qa_outputs"):
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module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
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module.qa_outputs.bias.data.zero_()
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elif isinstance(module, MT5ForTokenClassification):
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if hasattr(module, "classifier"):
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module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
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module.classifier.bias.data.zero_()
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elif isinstance(module, MT5ClassificationHead):
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module.dense.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
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if hasattr(module.dense, "bias") and module.dense.bias is not None:
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@ -1334,7 +1352,6 @@ class MT5Model(MT5PreTrainedModel):
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model_type = "mt5"
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config_class = MT5Config
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_keys_to_ignore_on_load_missing = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
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_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
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_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
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@ -2158,6 +2175,80 @@ class MT5ForSequenceClassification(MT5PreTrainedModel):
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)
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@add_start_docstrings(
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"""
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MT5 Encoder Model with a token classification head on top (a linear layer on top of the hidden-states output)
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e.g. for Named-Entity-Recognition (NER) tasks.
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""",
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MT5_START_DOCSTRING,
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)
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class MT5ForTokenClassification(MT5PreTrainedModel):
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_tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
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# Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.__init__ with T5->MT5
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def __init__(self, config: MT5Config):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.transformer = MT5EncoderModel(config)
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self.dropout = nn.Dropout(config.classifier_dropout)
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self.classifier = nn.Linear(config.hidden_size, config.num_labels)
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# Initialize weights and apply final processing
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self.post_init()
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@add_start_docstrings_to_model_forward(MT5_INPUTS_DOCSTRING)
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@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
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# Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.forward with T5->MT5
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def forward(
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self,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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labels: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
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Returns:
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.transformer(
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input_ids,
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attention_mask=attention_mask,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = outputs[0]
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hidden_states = self.dropout(hidden_states)
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logits = self.classifier(hidden_states)
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loss = None
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if labels is not None:
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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if not return_dict:
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output = (logits, outputs[2:-1])
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return ((loss,) + output) if loss is not None else output
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return TokenClassifierOutput(
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loss=loss,
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logits=logits,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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@add_start_docstrings(
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"""
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MT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
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"load_tf_weights_in_t5",
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"T5ForQuestionAnswering",
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"T5ForSequenceClassification",
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"T5ForTokenClassification",
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]
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try:
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@ -119,6 +120,7 @@ if TYPE_CHECKING:
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T5ForConditionalGeneration,
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T5ForQuestionAnswering,
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T5ForSequenceClassification,
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T5ForTokenClassification,
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T5Model,
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T5PreTrainedModel,
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load_tf_weights_in_t5,
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@ -33,6 +33,7 @@ from ...modeling_outputs import (
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Seq2SeqModelOutput,
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Seq2SeqQuestionAnsweringModelOutput,
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Seq2SeqSequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer
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@ -832,6 +833,10 @@ class T5PreTrainedModel(PreTrainedModel):
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if hasattr(module, "qa_outputs"):
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module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
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module.qa_outputs.bias.data.zero_()
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elif isinstance(module, T5ForTokenClassification):
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if hasattr(module, "classifier"):
|
||||
module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
|
||||
module.classifier.bias.data.zero_()
|
||||
elif isinstance(module, T5ClassificationHead):
|
||||
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:
|
||||
|
@ -2118,6 +2123,78 @@ class T5ForSequenceClassification(T5PreTrainedModel):
|
|||
)
|
||||
|
||||
|
||||
@add_start_docstrings(
|
||||
"""
|
||||
T5 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.
|
||||
""",
|
||||
T5_START_DOCSTRING,
|
||||
)
|
||||
class T5ForTokenClassification(T5PreTrainedModel):
|
||||
_tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
|
||||
|
||||
def __init__(self, config: T5Config):
|
||||
super().__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
|
||||
self.transformer = T5EncoderModel(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(T5_INPUTS_DOCSTRING)
|
||||
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
||||
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(
|
||||
"""
|
||||
T5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
|
||||
|
|
|
@ -31,6 +31,7 @@ else:
|
|||
"UMT5ForConditionalGeneration",
|
||||
"UMT5ForQuestionAnswering",
|
||||
"UMT5ForSequenceClassification",
|
||||
"UMT5ForTokenClassification",
|
||||
"UMT5Model",
|
||||
"UMT5PreTrainedModel",
|
||||
]
|
||||
|
@ -49,6 +50,7 @@ if TYPE_CHECKING:
|
|||
UMT5ForConditionalGeneration,
|
||||
UMT5ForQuestionAnswering,
|
||||
UMT5ForSequenceClassification,
|
||||
UMT5ForTokenClassification,
|
||||
UMT5Model,
|
||||
UMT5PreTrainedModel,
|
||||
)
|
||||
|
|
|
@ -76,6 +76,7 @@ class UMT5Config(PretrainedConfig):
|
|||
|
||||
model_type = "umt5"
|
||||
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,
|
||||
|
@ -102,15 +103,6 @@ class UMT5Config(PretrainedConfig):
|
|||
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
|
||||
|
@ -143,17 +135,15 @@ class UMT5Config(PretrainedConfig):
|
|||
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 UMT5OnnxConfig(OnnxSeq2SeqConfigWithPast):
|
||||
|
|
|
@ -30,6 +30,7 @@ from ...modeling_outputs import (
|
|||
Seq2SeqModelOutput,
|
||||
Seq2SeqQuestionAnsweringModelOutput,
|
||||
Seq2SeqSequenceClassifierOutput,
|
||||
TokenClassifierOutput,
|
||||
)
|
||||
from ...modeling_utils import PreTrainedModel
|
||||
from ...utils import (
|
||||
|
@ -515,6 +516,10 @@ class UMT5PreTrainedModel(PreTrainedModel):
|
|||
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, UMT5ForTokenClassification):
|
||||
if hasattr(module, "classifier"):
|
||||
module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
|
||||
module.classifier.bias.data.zero_()
|
||||
elif isinstance(module, UMT5ClassificationHead):
|
||||
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:
|
||||
|
@ -941,7 +946,7 @@ class UMT5Model(UMT5PreTrainedModel):
|
|||
>>> hidden_states = outputs.last_hidden_state
|
||||
```"""
|
||||
|
||||
model_type = "uumt5"
|
||||
model_type = "umt5"
|
||||
config_class = UMT5Config
|
||||
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
|
||||
|
||||
|
@ -1589,6 +1594,81 @@ class UMT5ForSequenceClassification(UMT5PreTrainedModel):
|
|||
)
|
||||
|
||||
|
||||
@add_start_docstrings(
|
||||
"""
|
||||
UMT5 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.
|
||||
""",
|
||||
UMT5_START_DOCSTRING,
|
||||
)
|
||||
class UMT5ForTokenClassification(UMT5PreTrainedModel):
|
||||
_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
|
||||
_tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
|
||||
|
||||
# Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.__init__ with T5->UMT5
|
||||
def __init__(self, config: UMT5Config):
|
||||
super().__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
|
||||
self.transformer = UMT5EncoderModel(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(UMT5_INPUTS_DOCSTRING)
|
||||
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
|
||||
# Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.forward with T5->UMT5
|
||||
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(
|
||||
"""
|
||||
UMT5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
|
||||
|
|
|
@ -5724,6 +5724,13 @@ class MT5ForSequenceClassification(metaclass=DummyObject):
|
|||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class MT5ForTokenClassification(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class MT5Model(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
|
@ -7977,6 +7984,13 @@ class T5ForSequenceClassification(metaclass=DummyObject):
|
|||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class T5ForTokenClassification(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class T5Model(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
|
@ -8216,6 +8230,13 @@ class UMT5ForSequenceClassification(metaclass=DummyObject):
|
|||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class UMT5ForTokenClassification(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class UMT5Model(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
|
|
File diff suppressed because it is too large
Load Diff
|
@ -52,6 +52,7 @@ if is_torch_available():
|
|||
T5ForConditionalGeneration,
|
||||
T5ForQuestionAnswering,
|
||||
T5ForSequenceClassification,
|
||||
T5ForTokenClassification,
|
||||
T5Model,
|
||||
T5Tokenizer,
|
||||
)
|
||||
|
@ -586,9 +587,11 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
|
|||
# `QAPipelineTests` is not working well with slow tokenizers (for some models) and we don't want to touch the file
|
||||
# `src/transformers/data/processors/squad.py` (where this test fails for this model)
|
||||
def is_pipeline_test_to_skip(
|
||||
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
||||
self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, processor_name
|
||||
):
|
||||
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
|
||||
if tokenizer_name is None:
|
||||
return True
|
||||
if pipeline_test_case_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
@ -998,6 +1001,22 @@ class T5EncoderOnlyModelTester:
|
|||
output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
|
||||
self.parent.assertFalse(torch.isnan(output).any().item())
|
||||
|
||||
def create_and_check_with_token_classification_head(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
):
|
||||
labels = torch.tensor([1] * self.seq_length * self.batch_size, dtype=torch.long, device=torch_device)
|
||||
model = T5ForTokenClassification(config=config).to(torch_device).eval()
|
||||
outputs = model(
|
||||
input_ids=input_ids,
|
||||
labels=labels,
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
|
||||
self.parent.assertEqual(outputs["loss"].size(), ())
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
|
@ -1013,11 +1032,18 @@ class T5EncoderOnlyModelTester:
|
|||
return config, inputs_dict
|
||||
|
||||
|
||||
class T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (T5EncoderModel,) if is_torch_available() else ()
|
||||
class T5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (T5EncoderModel, T5ForTokenClassification) if is_torch_available() else ()
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_model_parallel = True
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"token-classification": T5ForTokenClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
all_parallelizable_model_classes = (T5EncoderModel,) if is_torch_available() else ()
|
||||
|
||||
def setUp(self):
|
||||
|
@ -1036,6 +1062,10 @@ class T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
|
|||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
|
||||
|
||||
def test_with_token_classification_head(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
|
||||
|
||||
|
||||
def use_task_specific_params(model, task):
|
||||
model.config.update(model.config.task_specific_params[task])
|
||||
|
|
|
@ -18,7 +18,7 @@ import pickle
|
|||
import tempfile
|
||||
import unittest
|
||||
|
||||
from transformers import T5Config, is_torch_available
|
||||
from transformers import UMT5Config, is_torch_available
|
||||
from transformers.models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
|
||||
from transformers.testing_utils import (
|
||||
require_sentencepiece,
|
||||
|
@ -30,6 +30,7 @@ from transformers.testing_utils import (
|
|||
from transformers.utils import is_torch_fx_available
|
||||
|
||||
from ...generation.test_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
@ -43,9 +44,11 @@ if is_torch_available():
|
|||
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
UMT5EncoderModel,
|
||||
UMT5ForConditionalGeneration,
|
||||
UMT5ForQuestionAnswering,
|
||||
UMT5ForSequenceClassification,
|
||||
UMT5ForTokenClassification,
|
||||
UMT5Model,
|
||||
)
|
||||
|
||||
|
@ -100,7 +103,7 @@ class UMT5ModelTester:
|
|||
self.decoder_layers = decoder_layers
|
||||
|
||||
def get_large_model_config(self):
|
||||
return T5Config.from_pretrained("google/umt5-base")
|
||||
return UMT5Config.from_pretrained("google/umt5-base")
|
||||
|
||||
def prepare_inputs_dict(
|
||||
self,
|
||||
|
@ -160,7 +163,7 @@ class UMT5ModelTester:
|
|||
return config, inputs_dict
|
||||
|
||||
def get_pipeline_config(self):
|
||||
return T5Config(
|
||||
return UMT5Config(
|
||||
vocab_size=166, # t5 forces 100 extra tokens
|
||||
d_model=self.hidden_size,
|
||||
d_ff=self.d_ff,
|
||||
|
@ -178,7 +181,7 @@ class UMT5ModelTester:
|
|||
)
|
||||
|
||||
def get_config(self):
|
||||
return T5Config(
|
||||
return UMT5Config(
|
||||
vocab_size=self.vocab_size,
|
||||
d_model=self.hidden_size,
|
||||
d_ff=self.d_ff,
|
||||
|
@ -556,6 +559,176 @@ class UMT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
|
|||
pass
|
||||
|
||||
|
||||
# Copied from tests.models.t5.test_modeling_t5.T5EncoderOnlyModelTester with T5->UMT5
|
||||
class UMT5EncoderOnlyModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
vocab_size=99,
|
||||
batch_size=13,
|
||||
encoder_seq_length=7,
|
||||
# For common tests
|
||||
use_attention_mask=True,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=4,
|
||||
d_ff=37,
|
||||
relative_attention_num_buckets=8,
|
||||
is_training=False,
|
||||
dropout_rate=0.1,
|
||||
initializer_factor=0.002,
|
||||
is_encoder_decoder=False,
|
||||
eos_token_id=1,
|
||||
pad_token_id=0,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.encoder_seq_length = encoder_seq_length
|
||||
# For common tests
|
||||
self.seq_length = self.encoder_seq_length
|
||||
self.use_attention_mask = use_attention_mask
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.d_ff = d_ff
|
||||
self.relative_attention_num_buckets = relative_attention_num_buckets
|
||||
self.dropout_rate = dropout_rate
|
||||
self.initializer_factor = initializer_factor
|
||||
self.eos_token_id = eos_token_id
|
||||
self.pad_token_id = pad_token_id
|
||||
self.is_encoder_decoder = is_encoder_decoder
|
||||
self.scope = None
|
||||
self.is_training = is_training
|
||||
|
||||
def get_large_model_config(self):
|
||||
return UMT5Config.from_pretrained("t5-base")
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
|
||||
|
||||
attention_mask = None
|
||||
if self.use_attention_mask:
|
||||
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
|
||||
|
||||
config = UMT5Config(
|
||||
vocab_size=self.vocab_size,
|
||||
d_model=self.hidden_size,
|
||||
d_ff=self.d_ff,
|
||||
d_kv=self.hidden_size // self.num_attention_heads,
|
||||
num_layers=self.num_hidden_layers,
|
||||
num_heads=self.num_attention_heads,
|
||||
relative_attention_num_buckets=self.relative_attention_num_buckets,
|
||||
dropout_rate=self.dropout_rate,
|
||||
initializer_factor=self.initializer_factor,
|
||||
eos_token_id=self.eos_token_id,
|
||||
bos_token_id=self.pad_token_id,
|
||||
pad_token_id=self.pad_token_id,
|
||||
is_encoder_decoder=self.is_encoder_decoder,
|
||||
)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
)
|
||||
|
||||
def create_and_check_model(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
):
|
||||
model = UMT5EncoderModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
result = model(input_ids=input_ids)
|
||||
encoder_output = result.last_hidden_state
|
||||
|
||||
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
|
||||
|
||||
def create_and_check_model_fp16_forward(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
):
|
||||
model = UMT5EncoderModel(config=config).to(torch_device).half().eval()
|
||||
output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
|
||||
self.parent.assertFalse(torch.isnan(output).any().item())
|
||||
|
||||
def create_and_check_with_token_classification_head(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
):
|
||||
labels = torch.tensor([1] * self.seq_length * self.batch_size, dtype=torch.long, device=torch_device)
|
||||
model = UMT5ForTokenClassification(config=config).to(torch_device).eval()
|
||||
outputs = model(
|
||||
input_ids=input_ids,
|
||||
labels=labels,
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
|
||||
self.parent.assertEqual(outputs["loss"].size(), ())
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
) = config_and_inputs
|
||||
|
||||
inputs_dict = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
# Copied from tests.models.t5.test_modeling_t5.T5EncoderOnlyModelTest with T5->UMT5
|
||||
class UMT5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (UMT5EncoderModel, UMT5ForTokenClassification) if is_torch_available() else ()
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_model_parallel = True
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"token-classification": UMT5ForTokenClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
all_parallelizable_model_classes = (UMT5EncoderModel,) if is_torch_available() else ()
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = UMT5EncoderOnlyModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=UMT5Config, d_model=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
|
||||
def test_model_fp16_forward(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
|
||||
|
||||
def test_with_token_classification_head(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
|
|
Loading…
Reference in New Issue