ba8c4d0ac0
* splitting fast and slow tokenizers [WIP] * [WIP] splitting sentencepiece and tokenizers dependencies * update dummy objects * add name_or_path to models and tokenizers * prefix added to file names * prefix * styling + quality * spliting all the tokenizer files - sorting sentencepiece based ones * update tokenizer version up to 0.9.0 * remove hard dependency on sentencepiece 🎉 * and removed hard dependency on tokenizers 🎉 * update conversion script * update missing models * fixing tests * move test_tokenization_fast to main tokenization tests - fix bugs * bump up tokenizers * fix bert_generation * update ad fix several tokenizers * keep sentencepiece in deps for now * fix funnel and deberta tests * fix fsmt * fix marian tests * fix layoutlm * fix squeezebert and gpt2 * fix T5 tokenization * fix xlnet tests * style * fix mbart * bump up tokenizers to 0.9.2 * fix model tests * fix tf models * fix seq2seq examples * fix tests without sentencepiece * fix slow => fast conversion without sentencepiece * update auto and bert generation tests * fix mbart tests * fix auto and common test without tokenizers * fix tests without tokenizers * clean up tests lighten up when tokenizers + sentencepiece are both off * style quality and tests fixing * add sentencepiece to doc/examples reqs * leave sentencepiece on for now * style quality split hebert and fix pegasus * WIP Herbert fast * add sample_text_no_unicode and fix hebert tokenization * skip FSMT example test for now * fix style * fix fsmt in example tests * update following Lysandre and Sylvain's comments * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
1891 lines
40 KiB
Python
1891 lines
40 KiB
Python
# This file is autogenerated by the command `make fix-copies`, do not edit.
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from ..file_utils import requires_pytorch
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class PyTorchBenchmark:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class PyTorchBenchmarkArguments:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class DataCollator:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class DataCollatorForLanguageModeling:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class DataCollatorForNextSentencePrediction:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class DataCollatorForPermutationLanguageModeling:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class DataCollatorForSOP:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class DataCollatorWithPadding:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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def default_data_collator(*args, **kwargs):
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requires_pytorch(default_data_collator)
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class GlueDataset:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class GlueDataTrainingArguments:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class LineByLineTextDataset:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class LineByLineWithSOPTextDataset:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class SquadDataset:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class SquadDataTrainingArguments:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class TextDataset:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class TextDatasetForNextSentencePrediction:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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def top_k_top_p_filtering(*args, **kwargs):
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requires_pytorch(top_k_top_p_filtering)
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ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class AlbertForMaskedLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertForMultipleChoice:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertForPreTraining:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertForQuestionAnswering:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertForTokenClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AlbertPreTrainedModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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def load_tf_weights_in_albert(*args, **kwargs):
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requires_pytorch(load_tf_weights_in_albert)
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MODEL_FOR_CAUSAL_LM_MAPPING = None
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MODEL_FOR_MASKED_LM_MAPPING = None
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MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None
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MODEL_FOR_PRETRAINING_MAPPING = None
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MODEL_FOR_QUESTION_ANSWERING_MAPPING = None
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MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None
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MODEL_MAPPING = None
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MODEL_WITH_LM_HEAD_MAPPING = None
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class AutoModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForCausalLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForMaskedLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForMultipleChoice:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForPreTraining:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForQuestionAnswering:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForSeq2SeqLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelForTokenClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class AutoModelWithLMHead:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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BART_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class BartForConditionalGeneration:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BartForQuestionAnswering:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BartForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BartModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class PretrainedBartModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class BertForMaskedLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertForMultipleChoice:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertForNextSentencePrediction:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class BertForPreTraining:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class BertForQuestionAnswering:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertForTokenClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertLayer:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class BertLMHeadModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class BertPreTrainedModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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def load_tf_weights_in_bert(*args, **kwargs):
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requires_pytorch(load_tf_weights_in_bert)
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class BertGenerationDecoder:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class BertGenerationEncoder:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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def load_tf_weights_in_bert_generation(*args, **kwargs):
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requires_pytorch(load_tf_weights_in_bert_generation)
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BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class BlenderbotForConditionalGeneration:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class CamembertForCausalLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class CamembertForMaskedLM:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CamembertForMultipleChoice:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CamembertForQuestionAnswering:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CamembertForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CamembertForTokenClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CamembertModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class CTRLLMHeadModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CTRLModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class CTRLPreTrainedModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
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class DebertaForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class DebertaModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DebertaPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class DistilBertForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DistilBertForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DistilBertForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DistilBertForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DistilBertForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DistilBertModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DistilBertPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DPRContextEncoder:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DPRPretrainedContextEncoder:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DPRPretrainedQuestionEncoder:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DPRPretrainedReader:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DPRQuestionEncoder:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class DPRReader:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class ElectraForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraForPreTraining:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ElectraPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_electra(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_electra)
|
|
|
|
|
|
class EncoderDecoderModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class FlaubertForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FlaubertForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FlaubertForQuestionAnsweringSimple:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FlaubertForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FlaubertForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FlaubertModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FlaubertWithLMHeadModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FSMTForConditionalGeneration:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FSMTModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class PretrainedFSMTModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class FunnelBaseModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelForPreTraining:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class FunnelModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_funnel(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_funnel)
|
|
|
|
|
|
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class GPT2DoubleHeadsModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class GPT2ForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class GPT2LMHeadModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class GPT2Model:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class GPT2PreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_gpt2(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_gpt2)
|
|
|
|
|
|
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class LayoutLMForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LayoutLMForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LayoutLMModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class LongformerForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LongformerForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LongformerForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LongformerForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LongformerForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LongformerModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LongformerSelfAttention:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertEncoder:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertForPreTraining:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertVisualFeatureEncoder:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class LxmertXLayer:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MarianMTModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MBartForConditionalGeneration:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MMBTForClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MMBTModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ModalEmbeddings:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class MobileBertForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertForNextSentencePrediction:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertForPreTraining:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertLayer:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class MobileBertPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_mobilebert(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_mobilebert)
|
|
|
|
|
|
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class OpenAIGPTDoubleHeadsModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class OpenAIGPTForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class OpenAIGPTLMHeadModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class OpenAIGPTModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class OpenAIGPTPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_openai_gpt(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_openai_gpt)
|
|
|
|
|
|
class PegasusForConditionalGeneration:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RagModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RagSequenceForGeneration:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RagTokenForGeneration:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class ReformerAttention:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ReformerForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ReformerForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ReformerForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ReformerLayer:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ReformerModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class ReformerModelWithLMHead:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class RetriBertModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RetriBertPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class RobertaForCausalLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RobertaForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RobertaForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RobertaForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RobertaForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RobertaForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class RobertaModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class SqueezeBertForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertModule:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class SqueezeBertPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
T5_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class T5ForConditionalGeneration:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class T5Model:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class T5PreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_t5(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_t5)
|
|
|
|
|
|
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class AdaptiveEmbedding:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class TransfoXLLMHeadModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class TransfoXLModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class TransfoXLPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def load_tf_weights_in_transfo_xl(*args, **kwargs):
|
|
requires_pytorch(load_tf_weights_in_transfo_xl)
|
|
|
|
|
|
class Conv1D:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class PreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
def apply_chunking_to_forward(*args, **kwargs):
|
|
requires_pytorch(apply_chunking_to_forward)
|
|
|
|
|
|
def prune_layer(*args, **kwargs):
|
|
requires_pytorch(prune_layer)
|
|
|
|
|
|
XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class XLMForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMForQuestionAnsweringSimple:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMPreTrainedModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMWithLMHeadModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class XLMRobertaForCausalLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMRobertaForMaskedLM:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMRobertaForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMRobertaForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMRobertaForSequenceClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMRobertaForTokenClassification:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLMRobertaModel:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
|
|
|
|
|
class XLNetForMultipleChoice:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLNetForQuestionAnswering:
|
|
def __init__(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
@classmethod
|
|
def from_pretrained(self, *args, **kwargs):
|
|
requires_pytorch(self)
|
|
|
|
|
|
class XLNetForQuestionAnsweringSimple:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class XLNetForSequenceClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class XLNetForTokenClassification:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class XLNetLMHeadModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class XLNetModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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class XLNetPreTrainedModel:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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@classmethod
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def from_pretrained(self, *args, **kwargs):
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requires_pytorch(self)
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def load_tf_weights_in_xlnet(*args, **kwargs):
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requires_pytorch(load_tf_weights_in_xlnet)
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class Adafactor:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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class AdamW:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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def get_constant_schedule(*args, **kwargs):
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requires_pytorch(get_constant_schedule)
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def get_constant_schedule_with_warmup(*args, **kwargs):
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requires_pytorch(get_constant_schedule_with_warmup)
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def get_cosine_schedule_with_warmup(*args, **kwargs):
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requires_pytorch(get_cosine_schedule_with_warmup)
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def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
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requires_pytorch(get_cosine_with_hard_restarts_schedule_with_warmup)
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def get_linear_schedule_with_warmup(*args, **kwargs):
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requires_pytorch(get_linear_schedule_with_warmup)
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def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
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requires_pytorch(get_polynomial_decay_schedule_with_warmup)
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class Trainer:
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def __init__(self, *args, **kwargs):
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requires_pytorch(self)
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def torch_distributed_zero_first(*args, **kwargs):
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requires_pytorch(torch_distributed_zero_first)
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