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Commit 31c23bd5 authored by thomwolf's avatar thomwolf
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[BIG] pytorch-transformers => transformers

parent 2f071fcb
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Showing with 142 additions and 142 deletions
pytorch_transformers/modeling_openai.py → transformers/modeling_openai.py
View file @ 31c23bd5
......@@ -294,9 +294,9 @@ OPENAI_GPT_START_DOCSTRING = r""" OpenAI GPT model was proposed in
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~pytorch_transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
......@@ -304,9 +304,9 @@ OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
Indices of input sequence tokens in the vocabulary.
GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......
pytorch_transformers/modeling_roberta.py → transformers/modeling_roberta.py
View file @ 31c23bd5
......@@ -77,9 +77,9 @@ ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~pytorch_transformers.RobertaConfig`): Model configuration class with all the parameters of the
config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ROBERTA_INPUTS_DOCSTRING = r"""
......@@ -102,8 +102,8 @@ ROBERTA_INPUTS_DOCSTRING = r"""
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......@@ -361,9 +361,9 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
``token_type_ids: 0 0 0 0 0 0 0``
Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.BertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
......
pytorch_transformers/modeling_tf_auto.py → transformers/modeling_tf_auto.py
View file @ 31c23bd5
......@@ -34,7 +34,7 @@ logger = logging.getLogger(__name__)
class TFAutoModel(object):
r"""
:class:`~pytorch_transformers.TFAutoModel` is a generic model class
:class:`~transformers.TFAutoModel` is a generic model class
that will be instantiated as one of the base model classes of the library
when created with the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)`
class method.
......@@ -79,7 +79,7 @@ class TFAutoModel(object):
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 path to a `directory` containing model weights saved using :func:`~pytorch_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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
......@@ -88,17 +88,17 @@ class TFAutoModel(object):
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~pytorch_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:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
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.
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_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:
Path to a directory in which a downloaded pre-trained model
......@@ -118,7 +118,7 @@ class TFAutoModel(object):
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 not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_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.
- 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::
......@@ -155,7 +155,7 @@ class TFAutoModel(object):
class TFAutoModelWithLMHead(object):
r"""
:class:`~pytorch_transformers.TFAutoModelWithLMHead` is a generic model class
:class:`~transformers.TFAutoModelWithLMHead` is a generic model class
that will be instantiated as one of the language modeling model classes of the library
when created with the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
class method.
......@@ -203,7 +203,7 @@ class TFAutoModelWithLMHead(object):
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 path to a `directory` containing model weights saved using :func:`~pytorch_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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
......@@ -212,17 +212,17 @@ class TFAutoModelWithLMHead(object):
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~pytorch_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:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
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.
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_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:
Path to a directory in which a downloaded pre-trained model
......@@ -242,7 +242,7 @@ class TFAutoModelWithLMHead(object):
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 not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_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.
- 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::
......@@ -279,7 +279,7 @@ class TFAutoModelWithLMHead(object):
class TFAutoModelForSequenceClassification(object):
r"""
:class:`~pytorch_transformers.TFAutoModelForSequenceClassification` is a generic model class
:class:`~transformers.TFAutoModelForSequenceClassification` is a generic model class
that will be instantiated as one of the sequence classification model classes of the library
when created with the `TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
class method.
......@@ -324,7 +324,7 @@ class TFAutoModelForSequenceClassification(object):
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 path to a `directory` containing model weights saved using :func:`~pytorch_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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
......@@ -333,17 +333,17 @@ class TFAutoModelForSequenceClassification(object):
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~pytorch_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:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
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.
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_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:
Path to a directory in which a downloaded pre-trained model
......@@ -363,7 +363,7 @@ class TFAutoModelForSequenceClassification(object):
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 not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_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.
- 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::
......@@ -393,7 +393,7 @@ class TFAutoModelForSequenceClassification(object):
class TFAutoModelForQuestionAnswering(object):
r"""
:class:`~pytorch_transformers.TFAutoModelForQuestionAnswering` is a generic model class
:class:`~transformers.TFAutoModelForQuestionAnswering` is a generic model class
that will be instantiated as one of the question answering model classes of the library
when created with the `TFAutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
class method.
......@@ -436,7 +436,7 @@ class TFAutoModelForQuestionAnswering(object):
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 path to a `directory` containing model weights saved using :func:`~pytorch_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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
......@@ -445,17 +445,17 @@ class TFAutoModelForQuestionAnswering(object):
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~pytorch_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:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
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.
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_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:
Path to a directory in which a downloaded pre-trained model
......@@ -475,7 +475,7 @@ class TFAutoModelForQuestionAnswering(object):
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 not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_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.
- 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::
......
pytorch_transformers/modeling_tf_bert.py → transformers/modeling_tf_bert.py
View file @ 31c23bd5
......@@ -581,9 +581,9 @@ BERT_START_DOCSTRING = r""" The BERT model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.BertConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
BERT_INPUTS_DOCSTRING = r"""
......@@ -607,9 +607,9 @@ BERT_INPUTS_DOCSTRING = r"""
Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.BertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......@@ -653,7 +653,7 @@ class TFBertModel(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertModel
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained('bert-base-uncased')
......@@ -692,7 +692,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForPreTraining
from transformers import BertTokenizer, TFBertForPreTraining
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
......@@ -738,7 +738,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForMaskedLM
from transformers import BertTokenizer, TFBertForMaskedLM
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForMaskedLM.from_pretrained('bert-base-uncased')
......@@ -782,7 +782,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForNextSentencePrediction
from transformers import BertTokenizer, TFBertForNextSentencePrediction
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
......@@ -827,7 +827,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForSequenceClassification
from transformers import BertTokenizer, TFBertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
......@@ -879,7 +879,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForMultipleChoice
from transformers import BertTokenizer, TFBertForMultipleChoice
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForMultipleChoice.from_pretrained('bert-base-uncased')
......@@ -958,7 +958,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForTokenClassification
from transformers import BertTokenizer, TFBertForTokenClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForTokenClassification.from_pretrained('bert-base-uncased')
......@@ -1011,7 +1011,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFBertForQuestionAnswering
from transformers import BertTokenizer, TFBertForQuestionAnswering
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForQuestionAnswering.from_pretrained('bert-base-uncased')
......
pytorch_transformers/modeling_tf_distilbert.py → transformers/modeling_tf_distilbert.py
View file @ 31c23bd5
......@@ -500,9 +500,9 @@ DISTILBERT_START_DOCSTRING = r"""
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
DISTILBERT_INPUTS_DOCSTRING = r"""
......@@ -540,7 +540,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import DistilBertTokenizer, TFDistilBertModel
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
......@@ -598,7 +598,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import DistilBertTokenizer, TFDistilBertForMaskedLM
from transformers import DistilBertTokenizer, TFDistilBertForMaskedLM
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
......@@ -653,7 +653,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFDistilBertForSequenceClassification
from transformers import BertTokenizer, TFDistilBertForSequenceClassification
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
......@@ -710,7 +710,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import BertTokenizer, TFDistilBertForQuestionAnswering
from transformers import BertTokenizer, TFDistilBertForQuestionAnswering
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
......
pytorch_transformers/modeling_tf_gpt2.py → transformers/modeling_tf_gpt2.py
View file @ 31c23bd5
......@@ -385,9 +385,9 @@ GPT2_START_DOCSTRING = r""" OpenAI GPT-2 model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.GPT2Config`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
GPT2_INPUTS_DOCSTRING = r""" Inputs:
......@@ -395,9 +395,9 @@ GPT2_INPUTS_DOCSTRING = r""" Inputs:
Indices of input sequence tokens in the vocabulary.
GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**past**:
list of ``Numpy array`` or ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
......@@ -441,7 +441,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import GPT2Tokenizer, TFGPT2Model
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
......@@ -481,7 +481,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import GPT2Tokenizer, TFGPT2LMHeadModel
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2LMHeadModel.from_pretrained('gpt2')
......@@ -537,7 +537,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel
from transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2DoubleHeadsModel.from_pretrained('gpt2')
......
pytorch_transformers/modeling_tf_openai.py → transformers/modeling_tf_openai.py
View file @ 31c23bd5
......@@ -371,9 +371,9 @@ OPENAI_GPT_START_DOCSTRING = r""" OpenAI GPT model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
......@@ -381,9 +381,9 @@ OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
Indices of input sequence tokens in the vocabulary.
GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......@@ -419,7 +419,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
model = TFOpenAIGPTModel.from_pretrained('openai-gpt')
......@@ -455,7 +455,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import OpenAIGPTTokenizer, TFOpenAIGPTLMHeadModel
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTLMHeadModel
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
model = TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
......@@ -506,7 +506,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import OpenAIGPTTokenizer, TFOpenAIGPTDoubleHeadsModel
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTDoubleHeadsModel
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
model = TFOpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')
......
pytorch_transformers/modeling_tf_pytorch_utils.py → transformers/modeling_tf_pytorch_utils.py
View file @ 31c23bd5
......@@ -189,14 +189,14 @@ def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs
"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.")
raise e
import pytorch_transformers
import transformers
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info("Loading TensorFlow weights from {}".format(tf_checkpoint_path))
# Instantiate and load the associated TF 2.0 model
tf_model_class_name = "TF" + pt_model.__class__.__name__ # Add "TF" at the beggining
tf_model_class = getattr(pytorch_transformers, tf_model_class_name)
tf_model_class = getattr(transformers, tf_model_class_name)
tf_model = tf_model_class(pt_model.config)
if tf_inputs is None:
......
pytorch_transformers/modeling_tf_roberta.py → transformers/modeling_tf_roberta.py
View file @ 31c23bd5
......@@ -137,9 +137,9 @@ ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.RobertaConfig`): Model configuration class with all the parameters of the
config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ROBERTA_INPUTS_DOCSTRING = r"""
......@@ -162,8 +162,8 @@ ROBERTA_INPUTS_DOCSTRING = r"""
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......@@ -209,7 +209,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import RobertaTokenizer, TFRobertaModel
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaModel.from_pretrained('roberta-base')
......@@ -286,7 +286,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import RobertaTokenizer, TFRobertaForMaskedLM
from transformers import RobertaTokenizer, TFRobertaForMaskedLM
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
......@@ -354,7 +354,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import RobertaTokenizer, TFRobertaForSequenceClassification
from transformers import RobertaTokenizer, TFRobertaForSequenceClassification
tokenizer = RoertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaForSequenceClassification.from_pretrained('roberta-base')
......
pytorch_transformers/modeling_tf_transfo_xl.py → transformers/modeling_tf_transfo_xl.py
View file @ 31c23bd5
......@@ -614,9 +614,9 @@ TRANSFO_XL_START_DOCSTRING = r""" The Transformer-XL model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.TransfoXLConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.TransfoXLConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
TRANSFO_XL_INPUTS_DOCSTRING = r"""
......@@ -625,9 +625,9 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
Indices of input sequence tokens in the vocabulary.
Transformer-XL is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`pytorch_transformers.TransfoXLTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.TransfoXLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**mems**: (`optional`)
list of ``Numpy array`` or ``tf.Tensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
......@@ -660,7 +660,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import TransfoXLTokenizer, TFTransfoXLModel
from transformers import TransfoXLTokenizer, TFTransfoXLModel
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
model = TFTransfoXLModel.from_pretrained('transfo-xl-wt103')
......@@ -702,7 +702,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import TransfoXLTokenizer, TFTransfoXLLMHeadModel
from transformers import TransfoXLTokenizer, TFTransfoXLLMHeadModel
tokenizer = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')
model = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
......
pytorch_transformers/modeling_tf_utils.py → transformers/modeling_tf_utils.py
View file @ 31c23bd5
......@@ -32,16 +32,16 @@ logger = logging.getLogger(__name__)
class TFPreTrainedModel(tf.keras.Model):
r""" Base class for all TF models.
:class:`~pytorch_transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
:class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- ``config_class``: a class derived from :class:`~pytorch_transformers.PretrainedConfig` to use as configuration class for this model architecture.
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
- ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
- ``model``: an instance of the relevant subclass of :class:`~pytorch_transformers.PreTrainedModel`,
- ``config``: an instance of the relevant subclass of :class:`~pytorch_transformers.PretrainedConfig`,
- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
- ``path``: a path (string) to the TensorFlow checkpoint.
- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
......@@ -123,7 +123,7 @@ class TFPreTrainedModel(tf.keras.Model):
def save_pretrained(self, save_directory):
""" Save a model and its configuration file to a directory, so that it
can be re-loaded using the `:func:`~pytorch_transformers.PreTrainedModel.from_pretrained`` class method.
can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
"""
assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
......@@ -151,17 +151,17 @@ class TFPreTrainedModel(tf.keras.Model):
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 path to a `directory` containing model weights saved using :func:`~pytorch_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 `PyTorch state_dict save file` (e.g. `./pt_model/pytorch_model.bin`). In this case, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~pytorch_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:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
from_pt: (`optional`) boolean, default False:
......@@ -182,7 +182,7 @@ class TFPreTrainedModel(tf.keras.Model):
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 not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_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.
- 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::
......
pytorch_transformers/modeling_tf_xlm.py → transformers/modeling_tf_xlm.py
View file @ 31c23bd5
......@@ -484,9 +484,9 @@ XLM_START_DOCSTRING = r""" The XLM model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.XLMConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.XLMConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLM_INPUTS_DOCSTRING = r"""
......@@ -497,9 +497,9 @@ XLM_INPUTS_DOCSTRING = r"""
XLM is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`pytorch_transformers.XLMTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.XLMTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......@@ -550,7 +550,7 @@ class TFXLMModel(TFXLMPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLMTokenizer, TFXLMModel
from transformers import XLMTokenizer, TFXLMModel
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMModel.from_pretrained('xlm-mlm-en-2048')
......@@ -623,7 +623,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLMTokenizer, TFXLMWithLMHeadModel
from transformers import XLMTokenizer, TFXLMWithLMHeadModel
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048')
......@@ -667,7 +667,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLMTokenizer, TFXLMForSequenceClassification
from transformers import XLMTokenizer, TFXLMForSequenceClassification
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMForSequenceClassification.from_pretrained('xlm-mlm-en-2048')
......@@ -715,7 +715,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLMTokenizer, TFXLMForQuestionAnsweringSimple
from transformers import XLMTokenizer, TFXLMForQuestionAnsweringSimple
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
model = TFXLMForQuestionAnsweringSimple.from_pretrained('xlm-mlm-en-2048')
......
pytorch_transformers/modeling_tf_xlnet.py → transformers/modeling_tf_xlnet.py
View file @ 31c23bd5
......@@ -716,9 +716,9 @@ XLNET_START_DOCSTRING = r""" The XLNet model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLNET_INPUTS_DOCSTRING = r"""
......@@ -727,9 +727,9 @@ XLNET_INPUTS_DOCSTRING = r"""
Indices of input sequence tokens in the vocabulary.
XLNet is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`pytorch_transformers.XLNetTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.XLNetTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......@@ -793,7 +793,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLNetTokenizer, TFXLNetModel
from transformers import XLNetTokenizer, TFXLNetModel
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = TFXLNetModel.from_pretrained('xlnet-large-cased')
......@@ -835,7 +835,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLNetTokenizer, TFXLNetLMHeadModel
from transformers import XLNetTokenizer, TFXLNetLMHeadModel
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = TFXLNetLMHeadModel.from_pretrained('xlnet-large-cased')
......@@ -890,7 +890,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLNetTokenizer, TFXLNetForSequenceClassification
from transformers import XLNetTokenizer, TFXLNetForSequenceClassification
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = TFXLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
......@@ -943,7 +943,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel):
Examples::
import tensorflow as tf
from pytorch_transformers import XLNetTokenizer, TFXLNetForQuestionAnsweringSimple
from transformers import XLNetTokenizer, TFXLNetForQuestionAnsweringSimple
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = TFXLNetForQuestionAnsweringSimple.from_pretrained('xlnet-base-cased')
......
pytorch_transformers/modeling_transfo_xl.py → transformers/modeling_transfo_xl.py
View file @ 31c23bd5
......@@ -531,9 +531,9 @@ TRANSFO_XL_START_DOCSTRING = r""" The Transformer-XL model was proposed in
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~pytorch_transformers.TransfoXLConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.TransfoXLConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
TRANSFO_XL_INPUTS_DOCSTRING = r"""
......@@ -542,9 +542,9 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
Indices of input sequence tokens in the vocabulary.
Transformer-XL is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`pytorch_transformers.TransfoXLTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.TransfoXLTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**mems**: (`optional`)
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
......
pytorch_transformers/modeling_utils.py → transformers/modeling_utils.py
View file @ 31c23bd5
......@@ -52,16 +52,16 @@ except ImportError:
class PreTrainedModel(nn.Module):
r""" Base class for all models.
:class:`~pytorch_transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
:class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- ``config_class``: a class derived from :class:`~pytorch_transformers.PretrainedConfig` to use as configuration class for this model architecture.
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
- ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
- ``model``: an instance of the relevant subclass of :class:`~pytorch_transformers.PreTrainedModel`,
- ``config``: an instance of the relevant subclass of :class:`~pytorch_transformers.PretrainedConfig`,
- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
- ``path``: a path (string) to the TensorFlow checkpoint.
- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
......@@ -189,7 +189,7 @@ class PreTrainedModel(nn.Module):
def save_pretrained(self, save_directory):
""" Save a model and its configuration file to a directory, so that it
can be re-loaded using the `:func:`~pytorch_transformers.PreTrainedModel.from_pretrained`` class method.
can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
"""
assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
......@@ -220,24 +220,24 @@ class PreTrainedModel(nn.Module):
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 path to a `directory` containing model weights saved using :func:`~pytorch_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.
- None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~pytorch_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:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
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.
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_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:
Path to a directory in which a downloaded pre-trained model
......@@ -257,7 +257,7 @@ class PreTrainedModel(nn.Module):
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 not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_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.
- 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::
......@@ -355,7 +355,7 @@ class PreTrainedModel(nn.Module):
else:
# Load from our TensorFlow 2.0 checkpoints
try:
from pytorch_transformers import load_tf2_checkpoint_in_pytorch_model
from transformers import load_tf2_checkpoint_in_pytorch_model
model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
except ImportError as e:
logger.error("Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
......@@ -554,7 +554,7 @@ class SQuADHead(nn.Module):
r""" A SQuAD head inspired by XLNet.
Parameters:
config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
Inputs:
**hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``
......
pytorch_transformers/modeling_xlm.py → transformers/modeling_xlm.py
View file @ 31c23bd5
......@@ -63,7 +63,7 @@ def gelu(x):
GELU activation
https://arxiv.org/abs/1606.08415
https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/model_pytorch.py#L14
https://github.com/huggingface/pytorch-transformers/blob/master/modeling.py
https://github.com/huggingface/transformers/blob/master/modeling.py
"""
# return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
......@@ -265,9 +265,9 @@ XLM_START_DOCSTRING = r""" The XLM model was proposed in
https://github.com/facebookresearch/XLM
Parameters:
config (:class:`~pytorch_transformers.XLMConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.XLMConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLM_INPUTS_DOCSTRING = r"""
......@@ -278,9 +278,9 @@ XLM_INPUTS_DOCSTRING = r"""
XLM is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`pytorch_transformers.XLMTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.XLMTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
......
pytorch_transformers/modeling_xlnet.py → transformers/modeling_xlnet.py
View file @ 31c23bd5
......@@ -488,9 +488,9 @@ XLNET_START_DOCSTRING = r""" The XLNet model was proposed in
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
XLNET_INPUTS_DOCSTRING = r"""
......@@ -499,9 +499,9 @@ XLNET_INPUTS_DOCSTRING = r"""
Indices of input sequence tokens in the vocabulary.
XLNet is a model with relative position embeddings so you can either pad the inputs on
the right or on the left.
Indices can be obtained using :class:`pytorch_transformers.XLNetTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
Indices can be obtained using :class:`transformers.XLNetTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The type indices in XLNet are NOT selected in the vocabulary, they can be arbitrary numbers and
......
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