mirror of
https://github.com/huggingface/transformers.git
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Added multiple AutoModel classes: AutoModelWithLMHead, AutoModelForQuestionAnswering and AutoModelForSequenceClassification
This commit is contained in:
LysandreJik
parent
df9d6effae
commit
cb60ce59dd
@ -10,7 +10,8 @@ from .tokenization_roberta import RobertaTokenizer
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from .tokenization_utils import (PreTrainedTokenizer)
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from .modeling_auto import (AutoConfig, AutoModel)
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from .modeling_auto import (AutoConfig, AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
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AutoModelWithLMHead)
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from .modeling_bert import (BertConfig, BertPreTrainedModel, BertModel, BertForPreTraining,
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BertForMaskedLM, BertForNextSentencePrediction,
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@ -23,13 +23,13 @@ import torch.nn as nn
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from torch.nn import CrossEntropyLoss, MSELoss
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from torch.nn.parameter import Parameter
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from .modeling_bert import BertConfig, BertModel
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from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel
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from .modeling_gpt2 import GPT2Config, GPT2Model
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from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel
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from .modeling_xlnet import XLNetConfig, XLNetModel
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from .modeling_xlm import XLMConfig, XLMModel
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from .modeling_roberta import RobertaConfig, RobertaModel
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from .modeling_bert import BertConfig, BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
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from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel, OpenAIGPTLMHeadModel
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from .modeling_gpt2 import GPT2Config, GPT2Model, GPT2LMHeadModel
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from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
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from .modeling_xlnet import XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
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from .modeling_xlm import XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
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from .modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
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from .modeling_utils import PreTrainedModel, SequenceSummary
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@ -137,20 +137,20 @@ class AutoModel(object):
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when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method take care of returning the correct model class instance
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The base model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `roberta`: RobertaModel (RoBERTa model)
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- contains `bert`: BertModel (Bert model)
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- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
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- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
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- contains `xlnet`: XLNetModel (XLNet model)
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- contains `xlm`: XLMModel (XLM model)
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- contains `roberta`: RobertaModel (RoBERTa model)
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This class cannot be instantiated using `__init__()` (throw an error).
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModel is designed to be instantiated "
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@ -158,18 +158,18 @@ class AutoModel(object):
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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r""" Instantiate a one of the base model classes of the library
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r""" Instantiates one of the base model classes of the library
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from a pre-trained model configuration.
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The base model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `roberta`: RobertaModel (RoBERTa model)
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- contains `bert`: BertModel (Bert model)
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- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
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- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
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- contains `xlnet`: XLNetModel (XLNet model)
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- contains `xlm`: XLMModel (XLM model)
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- contains `roberta`: RobertaModel (RoBERTa model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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@ -186,12 +186,12 @@ class AutoModel(object):
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checkpoint in a PyTorch model using the provided conversion scripts and loading
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the PyTorch model afterwards.
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**model_args**: (`optional`) Sequence:
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All remaning positional arguments will be passed to the underlying model's __init__ function
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**config**: an optional configuration for the model to use instead of an automatically loaded configuation.
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All remaining positional arguments will be passed to the underlying model's __init__ function
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**config**: an optional configuration for the model to use instead of an automatically loaded configuration.
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Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with a `shortcut name` of a pre-trained model), or
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- the model was saved using the `save_pretrained(save_directory)` (loaded by suppling the save directory).
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**state_dict**: an optional state dictionnary for the model to use instead of a state dictionary loaded
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- the model was saved using the `save_pretrained(save_directory)` (loaded by supplying the save directory).
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**state_dict**: an optional state dictionary for the model to use instead of a state dictionary loaded
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from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
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In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
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@ -200,7 +200,7 @@ class AutoModel(object):
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Path to a directory in which a downloaded pre-trained model
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configuration should be cached if the standard cache should not be used.
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**output_loading_info**: (`optional`) boolean:
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Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
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Set to ``True`` to also return a dictionary containing missing keys, unexpected keys and error messages.
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**kwargs**: (`optional`) dict:
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Dictionary of key, values to update the configuration object after loading.
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Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
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@ -243,3 +243,328 @@ class AutoModel(object):
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raise ValueError("Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm', 'roberta'".format(pretrained_model_name_or_path))
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class AutoModelWithLMHead(object):
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r"""
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:class:`~pytorch_transformers.AutoModelWithLMHead` is a generic model class
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that will be instantiated as one of the language modeling model classes of the library
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when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
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- contains `bert`: BertForMaskedLM (Bert model)
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- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
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- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
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- contains `xlnet`: XLNetLMHeadModel (XLNet model)
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- contains `xlm`: XLMWithLMHeadModel (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
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"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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r""" Instantiates one of the language modeling model classes of the library
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from a pre-trained model configuration.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
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- contains `bert`: BertForMaskedLM (Bert model)
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- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
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- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
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- contains `xlnet`: XLNetLMHeadModel (XLNet model)
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- contains `xlm`: XLMWithLMHeadModel (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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Params:
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**pretrained_model_name_or_path**: either:
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- a string with the `shortcut name` of a pre-trained model to load from cache
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or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
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- a path to a `directory` containing a configuration file saved
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using the `save_pretrained(save_directory)` method.
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- a path or url to a tensorflow index checkpoint `file` (e.g. `./tf_model/model.ckpt.index`).
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In this case, ``from_tf`` should be set to True and a configuration object should be
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provided as `config` argument. This loading option is slower than converting the TensorFlow
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checkpoint in a PyTorch model using the provided conversion scripts and loading
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the PyTorch model afterwards.
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**model_args**: (`optional`) Sequence:
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All remaining positional arguments will be passed to the underlying model's __init__ function
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**config**: an optional configuration for the model to use instead of an automatically loaded configuration.
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Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with a `shortcut name` of a pre-trained model), or
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- the model was saved using the `save_pretrained(save_directory)` (loaded by supplying the save directory).
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**state_dict**: an optional state dictionary for the model to use instead of a state dictionary loaded
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from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
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In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
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a simpler option.
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**cache_dir**: (`optional`) string:
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Path to a directory in which a downloaded pre-trained model
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configuration should be cached if the standard cache should not be used.
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**output_loading_info**: (`optional`) boolean:
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Set to ``True`` to also return a dictionary containing missing keys, unexpected keys and error messages.
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**kwargs**: (`optional`) dict:
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Dictionary of key, values to update the configuration object after loading.
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Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
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- If a configuration is provided with `config`, **kwargs will be directly passed
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to the underlying model's __init__ method.
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- If a configuration is not provided, **kwargs will be first passed to the pretrained
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model configuration class loading function (`PretrainedConfig.from_pretrained`).
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Each key of **kwargs that corresponds to a configuration attribute
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will be used to override said attribute with the supplied **kwargs value.
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Remaining keys that do not correspond to any configuration attribute will
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be passed to the underlying model's __init__ function.
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Examples::
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model = AutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = AutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = AutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
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assert model.config.output_attention == True
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# Loading from a TF checkpoint file instead of a PyTorch model (slower)
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config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
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model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'roberta' in pretrained_model_name_or_path:
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return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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return BertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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return OpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMWithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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raise ValueError("Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm', 'roberta'".format(pretrained_model_name_or_path))
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class AutoModelForSequenceClassification(object):
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r"""
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:class:`~pytorch_transformers.AutoModelForSequenceClassification` is a generic model class
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that will be instantiated as one of the sequence classification model classes of the library
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when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
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- contains `bert`: BertForSequenceClassification (Bert model)
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- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
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- contains `xlm`: XLMForSequenceClassification (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
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"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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r""" Instantiates one of the sequence classification model classes of the library
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from a pre-trained model configuration.
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The `from_pretrained()` method takes care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
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- contains `bert`: BertForSequenceClassification (Bert model)
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- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
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- contains `xlm`: XLMForSequenceClassification (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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Params:
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**pretrained_model_name_or_path**: either:
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- a string with the `shortcut name` of a pre-trained model to load from cache
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or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
|
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- a path to a `directory` containing a configuration file saved
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using the `save_pretrained(save_directory)` method.
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- a path or url to a tensorflow index checkpoint `file` (e.g. `./tf_model/model.ckpt.index`).
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In this case, ``from_tf`` should be set to True and a configuration object should be
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provided as `config` argument. This loading option is slower than converting the TensorFlow
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checkpoint in a PyTorch model using the provided conversion scripts and loading
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the PyTorch model afterwards.
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**model_args**: (`optional`) Sequence:
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All remaining positional arguments will be passed to the underlying model's __init__ function
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**config**: an optional configuration for the model to use instead of an automatically loaded configuration.
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Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with a `shortcut name` of a pre-trained model), or
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- the model was saved using the `save_pretrained(save_directory)` (loaded by supplying the save directory).
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**state_dict**: an optional state dictionary for the model to use instead of a state dictionary loaded
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from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
|
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In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
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a simpler option.
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**cache_dir**: (`optional`) string:
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Path to a directory in which a downloaded pre-trained model
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configuration should be cached if the standard cache should not be used.
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**output_loading_info**: (`optional`) boolean:
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Set to ``True`` to also return a dictionary containing missing keys, unexpected keys and error messages.
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**kwargs**: (`optional`) dict:
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Dictionary of key, values to update the configuration object after loading.
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Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
|
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- If a configuration is provided with `config`, **kwargs will be directly passed
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to the underlying model's __init__ method.
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- If a configuration is not provided, **kwargs will be first passed to the pretrained
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model configuration class loading function (`PretrainedConfig.from_pretrained`).
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Each key of **kwargs that corresponds to a configuration attribute
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will be used to override said attribute with the supplied **kwargs value.
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Remaining keys that do not correspond to any configuration attribute will
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be passed to the underlying model's __init__ function.
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Examples::
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model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
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assert model.config.output_attention == True
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# Loading from a TF checkpoint file instead of a PyTorch model (slower)
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config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
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model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'roberta' in pretrained_model_name_or_path:
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return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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return BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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raise ValueError("Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path))
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class AutoModelForQuestionAnswering(object):
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r"""
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:class:`~pytorch_transformers.AutoModelForQuestionAnswering` is a generic model class
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that will be instantiated as one of the question answering model classes of the library
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when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
|
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class method.
|
||||
|
||||
The `from_pretrained()` method takes care of returning the correct model class instance
|
||||
using pattern matching on the `pretrained_model_name_or_path` string.
|
||||
|
||||
The model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `bert`: BertForQuestionAnswering (Bert model)
|
||||
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
||||
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
||||
|
||||
This class cannot be instantiated using `__init__()` (throws an error).
|
||||
"""
|
||||
def __init__(self):
|
||||
raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
|
||||
"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
r""" Instantiates one of the question answering model classes of the library
|
||||
from a pre-trained model configuration.
|
||||
|
||||
The `from_pretrained()` method takes care of returning the correct model class instance
|
||||
using pattern matching on the `pretrained_model_name_or_path` string.
|
||||
|
||||
The model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `bert`: BertForQuestionAnswering (Bert model)
|
||||
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
||||
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
||||
|
||||
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
|
||||
To train the model, you should first set it back in training mode with `model.train()`
|
||||
|
||||
Params:
|
||||
**pretrained_model_name_or_path**: either:
|
||||
- a string with the `shortcut name` of a pre-trained model to load from cache
|
||||
or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
|
||||
- a path to a `directory` containing a configuration file saved
|
||||
using the `save_pretrained(save_directory)` method.
|
||||
- a path or url to a tensorflow index checkpoint `file` (e.g. `./tf_model/model.ckpt.index`).
|
||||
In this case, ``from_tf`` should be set to True and a configuration object should be
|
||||
provided as `config` argument. This loading option is slower than converting the TensorFlow
|
||||
checkpoint in a PyTorch model using the provided conversion scripts and loading
|
||||
the PyTorch model afterwards.
|
||||
**model_args**: (`optional`) Sequence:
|
||||
All remaining positional arguments will be passed to the underlying model's __init__ function
|
||||
**config**: an optional configuration for the model to use instead of an automatically loaded configuration.
|
||||
Configuration can be automatically loaded when:
|
||||
- the model is a model provided by the library (loaded with a `shortcut name` of a pre-trained model), or
|
||||
- the model was saved using the `save_pretrained(save_directory)` (loaded by supplying the save directory).
|
||||
**state_dict**: an optional state dictionary for the model to use instead of a state dictionary loaded
|
||||
from saved weights file.
|
||||
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
|
||||
In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
|
||||
a simpler option.
|
||||
**cache_dir**: (`optional`) string:
|
||||
Path to a directory in which a downloaded pre-trained model
|
||||
configuration should be cached if the standard cache should not be used.
|
||||
**output_loading_info**: (`optional`) boolean:
|
||||
Set to ``True`` to also return a dictionary containing missing keys, unexpected keys and error messages.
|
||||
**kwargs**: (`optional`) dict:
|
||||
Dictionary of key, values to update the configuration object after loading.
|
||||
Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
|
||||
|
||||
- If a configuration is provided with `config`, **kwargs will be directly passed
|
||||
to the underlying model's __init__ method.
|
||||
- If a configuration is not provided, **kwargs will be first passed to the pretrained
|
||||
model configuration class loading function (`PretrainedConfig.from_pretrained`).
|
||||
Each key of **kwargs that corresponds to a configuration attribute
|
||||
will be used to override said attribute with the supplied **kwargs value.
|
||||
Remaining keys that do not correspond to any configuration attribute will
|
||||
be passed to the underlying model's __init__ function.
|
||||
|
||||
Examples::
|
||||
|
||||
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
|
||||
model = AutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
||||
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
|
||||
assert model.config.output_attention == True
|
||||
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
|
||||
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
|
||||
model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
||||
|
||||
"""
|
||||
if 'bert' in pretrained_model_name_or_path:
|
||||
return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
return XLNetForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlm' in pretrained_model_name_or_path:
|
||||
return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user