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AutoModels doc

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Lysandre
2020-01-24 16:36:11 -05:00
committed by Lysandre Debut
parent 009fcb0ec1
commit 983fef469c
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docs/source/model_doc/auto.rst
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@@ -3,7 +3,7 @@ AutoModels
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method. In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.
AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary: AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary:
Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``). Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).
@@ -15,6 +15,13 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
:members: :members:
``AutoTokenizer``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AutoTokenizer
:members:
``AutoModel`` ``AutoModel``
~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~
@@ -22,8 +29,30 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
:members: :members:
``AutoTokenizer`` ``AutoModelWithLMHead``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AutoTokenizer .. autoclass:: transformers.AutoModelWithLMHead
:members: :members:
``AutoModelForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AutoModelForSequenceClassification
:members:
``AutoModelForQuestionAnswering``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AutoModelForQuestionAnswering
:members:
``AutoModelForTokenClassification``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AutoModelForTokenClassification
:members:

302
src/transformers/modeling_auto.py
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@@ -202,26 +202,6 @@ class AutoModel(object):
when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)` when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
or the `AutoModel.from_config(config)` class methods. or the `AutoModel.from_config(config)` class methods.
The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to using pattern matching on the `pretrained_model_name_or_path` string.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: T5Model (T5 model)
- contains `distilbert`: DistilBertModel (DistilBERT model)
- contains `albert`: AlbertModel (ALBERT model)
- contains `camembert`: CamembertModel (CamemBERT model)
- contains `xlm-roberta`: XLMRobertaModel (XLM-RoBERTa model)
- contains `roberta`: RobertaModel (RoBERTa model)
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
- contains `xlnet`: XLNetModel (XLNet model)
- contains `xlm`: XLMModel (XLM model)
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
This class cannot be instantiated using `__init__()` (throws an error). This class cannot be instantiated using `__init__()` (throws an error).
""" """
@@ -237,17 +217,19 @@ class AutoModel(object):
r""" Instantiates one of the base model classes of the library r""" Instantiates one of the base model classes of the library
from a configuration. from a configuration.
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class: The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `roberta` configuration class: RobertaModel (RoBERTa model) - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
- isInstance of `bert` configuration class: BertModel (Bert model) - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModel` (RoBERTa model)
- isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model) - isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
- isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model) - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
- isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL model) - isInstance of `gpt2` configuration class: :class:`~transformers.GPT2Model` (OpenAI GPT-2 model)
- isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model) - isInstance of `ctrl` configuration class: :class:`~transformers.CTRLModel` (Salesforce CTRL model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model) - isInstance of `transfo-xl` configuration class: :class:`~transformers.TransfoXLModel` (Transformer-XL model)
- isInstance of `xlm` configuration class: XLMModel (XLM model) - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModel` (XLNet model)
- isInstance of `xlm` configuration class: :class:`~transformers.XLMModel` (XLM model)
Examples:: Examples::
@@ -269,26 +251,30 @@ class AutoModel(object):
r""" Instantiates one of the base model classes of the library r""" Instantiates one of the base model classes of the library
from a pre-trained model configuration. from a pre-trained model configuration.
The model class to instantiate is selected as the first pattern matching The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to using pattern matching on the `pretrained_model_name_or_path` string.
The base model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order): in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: T5Model (T5 model) - contains `t5`: :class:`~transformers.T5Model` (T5 model)
- contains `distilbert`: DistilBertModel (DistilBERT model) - contains `distilbert`: :class:`~transformers.DistilBertModel` (DistilBERT model)
- contains `albert`: AlbertModel (ALBERT model) - contains `albert`: :class:`~transformers.AlbertModel` (ALBERT model)
- contains `camembert`: CamembertModel (CamemBERT model) - contains `camembert`: :class:`~transformers.CamembertModel` (CamemBERT model)
- contains `xlm-roberta`: XLMRobertaModel (XLM-RoBERTa model) - contains `xlm-roberta`: :class:`~transformers.XLMRobertaModel` (XLM-RoBERTa model)
- contains `roberta`: RobertaModel (RoBERTa model) - contains `roberta`: :class:`~transformers.RobertaModel` (RoBERTa model)
- contains `bert`: BertModel (Bert model) - contains `bert`: :class:`~transformers.BertModel` (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `openai-gpt`: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - contains `gpt2`: :class:`~transformers.GPT2Model` (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `transfo-xl`: :class:`~transformers.TransfoXLModel` (Transformer-XL model)
- contains `xlnet`: XLNetModel (XLNet model) - contains `xlnet`: :class:`~transformers.XLNetModel` (XLNet model)
- contains `xlm`: XLMModel (XLM model) - contains `xlm`: :class:`~transformers.XLMModel` (XLM model)
- contains `ctrl`: CTRLModel (Salesforce CTRL model) - contains `ctrl`: :class:`~transformers.CTRLModel` (Salesforce CTRL model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) 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()` To train the model, you should first set it back in training mode with `model.train()`
Params: Args:
pretrained_model_name_or_path: either: pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
@@ -367,26 +353,6 @@ class AutoModelWithLMHead(object):
when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
class method. class method.
The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to 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 `t5`: T5ModelWithLMHead (T5 model)
- contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
- contains `albert`: AlbertForMaskedLM (ALBERT model)
- contains `camembert`: CamembertForMaskedLM (CamemBERT model)
- contains `xlm-roberta`: XLMRobertaForMaskedLM (XLM-RoBERTa model)
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
- contains `bert`: BertForMaskedLM (Bert model)
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
- contains `xlnet`: XLNetLMHeadModel (XLNet model)
- contains `xlm`: XLMWithLMHeadModel (XLM model)
- contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
This class cannot be instantiated using `__init__()` (throws an error). This class cannot be instantiated using `__init__()` (throws an error).
""" """
@@ -402,17 +368,19 @@ class AutoModelWithLMHead(object):
r""" Instantiates one of the base model classes of the library r""" Instantiates one of the base model classes of the library
from a configuration. from a configuration.
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class: The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `roberta` configuration class: RobertaModel (RoBERTa model) - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
- isInstance of `bert` configuration class: BertModel (Bert model) - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModel` (RoBERTa model)
- isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model) - isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
- isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model) - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
- isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL model) - isInstance of `gpt2` configuration class: :class:`~transformers.GPT2Model` (OpenAI GPT-2 model)
- isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model) - isInstance of `ctrl` configuration class: :class:`~transformers.CTRLModel` (Salesforce CTRL model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model) - isInstance of `transfo-xl` configuration class: :class:`~transformers.TransfoXLModel` (Transformer-XL model)
- isInstance of `xlm` configuration class: XLMModel (XLM model) - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModel` (XLNet model)
- isInstance of `xlm` configuration class: :class:`~transformers.XLMModel` (XLM model)
Examples:: Examples::
@@ -440,34 +408,33 @@ class AutoModelWithLMHead(object):
The model class to instantiate is selected as the first pattern matching The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order): in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: T5ModelWithLMHead (T5 model) - contains `t5`: :class:`~transformers.T5ModelWithLMHead` (T5 model)
- contains `distilbert`: DistilBertForMaskedLM (DistilBERT model) - contains `distilbert`: :class:`~transformers.DistilBertForMaskedLM` (DistilBERT model)
- contains `albert`: AlbertForMaskedLM (ALBERT model) - contains `albert`: :class:`~transformers.AlbertForMaskedLM` (ALBERT model)
- contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `camembert`: :class:`~transformers.CamembertForMaskedLM` (CamemBERT model)
- contains `xlm-roberta`: XLMRobertaForMaskedLM (XLM-RoBERTa model) - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForMaskedLM` (XLM-RoBERTa model)
- contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `roberta`: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
- contains `bert`: BertForMaskedLM (Bert model) - contains `bert`: :class:`~transformers.BertForMaskedLM` (Bert model)
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) - contains `openai-gpt`: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model) - contains `gpt2`: :class:`~transformers.GPT2LMHeadModel` (OpenAI GPT-2 model)
- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) - contains `transfo-xl`: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
- contains `xlnet`: XLNetLMHeadModel (XLNet model) - contains `xlnet`: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
- contains `xlm`: XLMWithLMHeadModel (XLM model) - contains `xlm`: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
- contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model) - contains `ctrl`: :class:`~transformers.CTRLLMHeadModel` (Salesforce CTRL model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) 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()` To train the model, you should first set it back in training mode with `model.train()`
Params: Args:
pretrained_model_name_or_path: either: pretrained_model_name_or_path:
Either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``. - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``. - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- 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 path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. - 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 path 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 of positional arguments: model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when: Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
@@ -479,28 +446,31 @@ class AutoModelWithLMHead(object):
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file. an optional state dictionnary 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. 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 :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option. In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string: cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used. configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False: force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists. Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False: resume_download: (`optional`) boolean, default False:
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. Do not delete incompletely received file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None: proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request. The proxies are used on each request.
output_loading_info: (`optional`) boolean: output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages. Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments: kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded: Can be used to update the configuration object (after it being loaded) and initiate the model.
(e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done) - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.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. underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.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:: Examples::
@@ -535,21 +505,6 @@ class AutoModelForSequenceClassification(object):
when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)` when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
class method. class method.
The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to 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 `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
- contains `albert`: AlbertForSequenceClassification (ALBERT model)
- contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
- contains `xlm-roberta`: XLMRobertaForSequenceClassification (XLM-RoBERTa model)
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
- contains `bert`: BertForSequenceClassification (Bert model)
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
- contains `xlm`: XLMForSequenceClassification (XLM model)
This class cannot be instantiated using `__init__()` (throws an error). This class cannot be instantiated using `__init__()` (throws an error).
""" """
@@ -565,13 +520,19 @@ class AutoModelForSequenceClassification(object):
r""" Instantiates one of the base model classes of the library r""" Instantiates one of the base model classes of the library
from a configuration. from a configuration.
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class: The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `roberta` configuration class: RobertaModel (RoBERTa model) - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
- isInstance of `bert` configuration class: BertModel (Bert model) - isInstance of `albert` configuration class: :class:`~transformers.AlbertModel` (ALBERT model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model) - isInstance of `camembert` configuration class: :class:`~transformers.CamembertModel` (CamemBERT model)
- isInstance of `xlm` configuration class: XLMModel (XLM model) - isInstance of `xlm roberta` configuration class: :class:`~transformers.XLMRobertaModel` (XLM-RoBERTa model)
- isInstance of `roberta` configuration class: :class:`~transformers.RobertaModel` (RoBERTa model)
- isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
- isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModel` (XLNet model)
- isInstance of `xlm` configuration class: :class:`~transformers.XLMModel` (XLM model)
Examples:: Examples::
@@ -601,19 +562,19 @@ class AutoModelForSequenceClassification(object):
The model class to instantiate is selected as the first pattern matching The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order): in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model) - contains `distilbert`: :class:`~transformers.DistilBertForSequenceClassification` (DistilBERT model)
- contains `albert`: AlbertForSequenceClassification (ALBERT model) - contains `albert`: :class:`~transformers.AlbertForSequenceClassification` (ALBERT model)
- contains `camembert`: CamembertForSequenceClassification (CamemBERT model) - contains `camembert`: :class:`~transformers.CamembertForSequenceClassification` (CamemBERT model)
- contains `xlm-roberta`: XLMRobertaForSequenceClassification (XLM-RoBERTa model) - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForSequenceClassification` (XLM-RoBERTa model)
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `roberta`: :class:`~transformers.RobertaForSequenceClassification` (RoBERTa model)
- contains `bert`: BertForSequenceClassification (Bert model) - contains `bert`: :class:`~transformers.BertForSequenceClassification` (Bert model)
- contains `xlnet`: XLNetForSequenceClassification (XLNet model) - contains `xlnet`: :class:`~transformers.XLNetForSequenceClassification` (XLNet model)
- contains `xlm`: XLMForSequenceClassification (XLM model) - contains `xlm`: :class:`~transformers.XLMForSequenceClassification` (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) 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()` To train the model, you should first set it back in training mode with `model.train()`
Params: Args:
pretrained_model_name_or_path: either: pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
@@ -622,7 +583,7 @@ class AutoModelForSequenceClassification(object):
- 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 path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. - 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 path 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 of positional arguments: model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method All remaining positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when: Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
@@ -694,18 +655,6 @@ class AutoModelForQuestionAnswering(object):
when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
class method. class method.
The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to 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 `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
- contains `albert`: AlbertForQuestionAnswering (ALBERT model)
- 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). This class cannot be instantiated using `__init__()` (throws an error).
""" """
@@ -721,12 +670,15 @@ class AutoModelForQuestionAnswering(object):
r""" Instantiates one of the base model classes of the library r""" Instantiates one of the base model classes of the library
from a configuration. from a configuration.
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class: The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `bert` configuration class: BertModel (Bert model) - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model) - isInstance of `albert` configuration class: :class:`~transformers.AlbertModel` (ALBERT model)
- isInstance of `xlm` configuration class: XLMModel (XLM model) - isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
- isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModel` (XLNet model)
- isInstance of `xlm` configuration class: :class:`~transformers.XLMModel` (XLM model)
Examples:: Examples::
@@ -757,16 +709,16 @@ class AutoModelForQuestionAnswering(object):
The model class to instantiate is selected as the first pattern matching The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order): in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model) - contains `distilbert`: :class:`~transformers.DistilBertForQuestionAnswering` (DistilBERT model)
- contains `albert`: AlbertForQuestionAnswering (ALBERT model) - contains `albert`: :class:`~transformers.AlbertForQuestionAnswering` (ALBERT model)
- contains `bert`: BertForQuestionAnswering (Bert model) - contains `bert`: :class:`~transformers.BertForQuestionAnswering` (Bert model)
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model) - contains `xlnet`: :class:`~transformers.XLNetForQuestionAnswering` (XLNet model)
- contains `xlm`: XLMForQuestionAnswering (XLM model) - contains `xlm`: :class:`~transformers.XLMForQuestionAnswering` (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) 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()` To train the model, you should first set it back in training mode with `model.train()`
Params: Args:
pretrained_model_name_or_path: either: pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
@@ -839,6 +791,15 @@ class AutoModelForQuestionAnswering(object):
class AutoModelForTokenClassification: class AutoModelForTokenClassification:
r"""
:class:`~transformers.AutoModelForTokenClassification` is a generic model class
that will be instantiated as one of the token classification model classes of the library
when created with the `AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)`
class method.
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self): def __init__(self):
raise EnvironmentError( raise EnvironmentError(
"AutoModelForTokenClassification is designed to be instantiated " "AutoModelForTokenClassification is designed to be instantiated "
@@ -851,13 +812,16 @@ class AutoModelForTokenClassification:
r""" Instantiates one of the base model classes of the library r""" Instantiates one of the base model classes of the library
from a configuration. from a configuration.
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class: The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `bert` configuration class: BertModel (Bert model) - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model) - isInstance of `xlm roberta` configuration class: :class:`~transformers.XLMRobertaModel` (XLMRoberta model)
- isInstance of `camembert` configuration class: CamembertModel (Camembert model) - isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
- isInstance of `roberta` configuration class: RobertaModel (Roberta model) - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModel` (XLNet model)
- isInstance of `camembert` configuration class: :class:`~transformers.CamembertModel` (Camembert model)
- isInstance of `roberta` configuration class: :class:`~transformers.RobertaModel` (Roberta model)
Examples:: Examples::
@@ -888,17 +852,19 @@ class AutoModelForTokenClassification:
The model class to instantiate is selected as the first pattern matching The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order): in the `pretrained_model_name_or_path` string (in the following order):
- contains `distilbert`: DistilBertForTokenClassification (DistilBERT model) - contains `distilbert`: :class:`~transformers.DistilBertForTokenClassification` (DistilBERT model)
- contains `camembert`: CamembertForTokenClassification (Camembert model) - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForTokenClassification` (XLM-RoBERTa?Para model)
- contains `bert`: BertForTokenClassification (Bert model) - contains `camembert`: :class:`~transformers.CamembertForTokenClassification` (Camembert model)
- contains `xlnet`: XLNetForTokenClassification (XLNet model) - contains `bert`: :class:`~transformers.BertForTokenClassification` (Bert model)
- contains `roberta`: RobertaForTokenClassification (Roberta model) - contains `xlnet`: :class:`~transformers.XLNetForTokenClassification` (XLNet model)
- contains `roberta`: :class:`~transformers.RobertaForTokenClassification` (Roberta model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) 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()` To train the model, you should first set it back in training mode with `model.train()`
Params: Args:
pretrained_model_name_or_path: either: pretrained_model_name_or_path:
Either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``. - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.