Source code for transformers.models.mt5.modeling_mt5

# coding=utf-8
# Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" PyTorch mT5 model. """

from ...utils import logging
from ..t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model
from .configuration_mt5 import MT5Config


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "T5Config"
_TOKENIZER_FOR_DOC = "T5Tokenizer"


[docs]class MT5Model(T5Model): r""" This class overrides :class:`~transformers.T5Model`. Please check the superclass for the appropriate documentation alongside usage examples. Examples:: >>> from transformers import MT5Model, T5Tokenizer >>> model = MT5Model.from_pretrained("google/mt5-small") >>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, return_tensors="pt") >>> with tokenizer.as_target_tokenizer(): ... labels = tokenizer(summary, return_tensors="pt") >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state """ model_type = "mt5" config_class = MT5Config _keys_to_ignore_on_load_missing = [ r"encoder\.embed_tokens\.weight", r"decoder\.embed_tokens\.weight", r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight", ] _keys_to_ignore_on_save = [ r"encoder\.embed_tokens\.weight", r"decoder\.embed_tokens\.weight", ]
[docs]class MT5ForConditionalGeneration(T5ForConditionalGeneration): r""" This class overrides :class:`~transformers.T5ForConditionalGeneration`. Please check the superclass for the appropriate documentation alongside usage examples. Examples:: >>> from transformers import MT5ForConditionalGeneration, T5Tokenizer >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small") >>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, return_tensors="pt") >>> with tokenizer.as_target_tokenizer(): ... labels = tokenizer(summary, return_tensors="pt") >>> outputs = model(**inputs,labels=labels["input_ids"]) >>> loss = outputs.loss """ model_type = "mt5" config_class = MT5Config _keys_to_ignore_on_load_missing = [ r"encoder\.embed_tokens\.weight", ] _keys_to_ignore_on_save = [ r"encoder\.embed_tokens\.weight", ]
[docs]class MT5EncoderModel(T5EncoderModel): r""" This class overrides :class:`~transformers.T5EncoderModel`. Please check the superclass for the appropriate documentation alongside usage examples. Examples:: >>> from transformers import MT5EncoderModel, T5Tokenizer >>> model = MT5EncoderModel.from_pretrained("google/mt5-small") >>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> input_ids = tokenizer(article, return_tensors="pt").input_ids >>> outputs = model(input_ids) >>> hidden_state = outputs.last_hidden_state """ model_type = "mt5" config_class = MT5Config _keys_to_ignore_on_load_missing = [ r"encoder\.embed_tokens\.weight", ] _keys_to_ignore_on_save = [ r"encoder\.embed_tokens\.weight", ]