| |
| from ..file_utils import requires_pytorch |
|
|
|
|
| class PyTorchBenchmark: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class PyTorchBenchmarkArguments: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DataCollator: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DataCollatorForLanguageModeling: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DataCollatorForNextSentencePrediction: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DataCollatorForPermutationLanguageModeling: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DataCollatorForSOP: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DataCollatorWithPadding: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def default_data_collator(*args, **kwargs): |
| requires_pytorch(default_data_collator) |
|
|
|
|
| class GlueDataset: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class GlueDataTrainingArguments: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class LineByLineTextDataset: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class LineByLineWithSOPTextDataset: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class SquadDataset: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class SquadDataTrainingArguments: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class TextDataset: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class TextDatasetForNextSentencePrediction: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def top_k_top_p_filtering(*args, **kwargs): |
| requires_pytorch(top_k_top_p_filtering) |
|
|
|
|
| ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class AlbertForMaskedLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertForMultipleChoice: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertForPreTraining: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertForQuestionAnswering: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertForTokenClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AlbertPreTrainedModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def load_tf_weights_in_albert(*args, **kwargs): |
| requires_pytorch(load_tf_weights_in_albert) |
|
|
|
|
| MODEL_FOR_CAUSAL_LM_MAPPING = None |
|
|
|
|
| MODEL_FOR_MASKED_LM_MAPPING = None |
|
|
|
|
| MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None |
|
|
|
|
| MODEL_FOR_PRETRAINING_MAPPING = None |
|
|
|
|
| MODEL_FOR_QUESTION_ANSWERING_MAPPING = None |
|
|
|
|
| MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None |
|
|
|
|
| MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None |
|
|
|
|
| MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None |
|
|
|
|
| MODEL_MAPPING = None |
|
|
|
|
| MODEL_WITH_LM_HEAD_MAPPING = None |
|
|
|
|
| class AutoModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForCausalLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForMaskedLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForMultipleChoice: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForPreTraining: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForQuestionAnswering: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForSeq2SeqLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelForTokenClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AutoModelWithLMHead: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| BART_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class BartForConditionalGeneration: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BartForQuestionAnswering: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BartForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BartModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class PretrainedBartModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class BertForMaskedLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertForMultipleChoice: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertForNextSentencePrediction: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertForPreTraining: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertForQuestionAnswering: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertForTokenClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertLayer: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertLMHeadModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertPreTrainedModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def load_tf_weights_in_bert(*args, **kwargs): |
| requires_pytorch(load_tf_weights_in_bert) |
|
|
|
|
| class BertGenerationDecoder: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class BertGenerationEncoder: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def load_tf_weights_in_bert_generation(*args, **kwargs): |
| requires_pytorch(load_tf_weights_in_bert_generation) |
|
|
|
|
| BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class BlenderbotForConditionalGeneration: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class CamembertForCausalLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CamembertForMaskedLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CamembertForMultipleChoice: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CamembertForQuestionAnswering: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CamembertForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CamembertForTokenClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CamembertModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class CTRLLMHeadModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CTRLModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class CTRLPreTrainedModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class DebertaForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class DebertaModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @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) |
|
|
|
|
| PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class ProphetNetDecoder: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class ProphetNetEncoder: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class ProphetNetForCausalLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class ProphetNetForConditionalGeneration: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class ProphetNetModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class ProphetNetPreTrainedModel: |
| 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_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None |
|
|
|
|
| class XLMProphetNetDecoder: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLMProphetNetEncoder: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLMProphetNetForCausalLM: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLMProphetNetForConditionalGeneration: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLMProphetNetModel: |
| 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: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLNetForSequenceClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLNetForTokenClassification: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLNetLMHeadModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLNetModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class XLNetPreTrainedModel: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
| @classmethod |
| def from_pretrained(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def load_tf_weights_in_xlnet(*args, **kwargs): |
| requires_pytorch(load_tf_weights_in_xlnet) |
|
|
|
|
| class Adafactor: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| class AdamW: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def get_constant_schedule(*args, **kwargs): |
| requires_pytorch(get_constant_schedule) |
|
|
|
|
| def get_constant_schedule_with_warmup(*args, **kwargs): |
| requires_pytorch(get_constant_schedule_with_warmup) |
|
|
|
|
| def get_cosine_schedule_with_warmup(*args, **kwargs): |
| requires_pytorch(get_cosine_schedule_with_warmup) |
|
|
|
|
| def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs): |
| requires_pytorch(get_cosine_with_hard_restarts_schedule_with_warmup) |
|
|
|
|
| def get_linear_schedule_with_warmup(*args, **kwargs): |
| requires_pytorch(get_linear_schedule_with_warmup) |
|
|
|
|
| def get_polynomial_decay_schedule_with_warmup(*args, **kwargs): |
| requires_pytorch(get_polynomial_decay_schedule_with_warmup) |
|
|
|
|
| class Trainer: |
| def __init__(self, *args, **kwargs): |
| requires_pytorch(self) |
|
|
|
|
| def torch_distributed_zero_first(*args, **kwargs): |
| requires_pytorch(torch_distributed_zero_first) |
|
|
