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Unverified Commit 08f534d2 authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Doc styling (#8067) 

* Important files

* Styling them all

* Revert "Styling them all"

This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

* More fixes

* Fix modeling auto and script

* Remove new line

* Fixes

* More fixes

* Fix more files

* Style

* Add FSMT

* More fixes

* More fixes

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* Make sphinx happy
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src/transformers/configuration_pegasus.py
View file @ 08f534d2
......@@ -68,12 +68,11 @@ task_specific_params = {
class PegasusConfig(BartConfig):
"""
This is the configuration class to store the configuration of a
:class:`~transformers.PegasusForConditionalGeneration`. It is used to
instantiate a Pegasus model according to the specified arguments, defining the model architecture.
:class:`~transformers.PegasusForConditionalGeneration`. It is used to instantiate a Pegasus model according to the
specified arguments, defining the model architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 96103):
......@@ -94,8 +93,8 @@ class PegasusConfig(BartConfig):
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
......@@ -105,8 +104,8 @@ class PegasusConfig(BartConfig):
classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (:obj:`int`, `optional`, defaults to 1024):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
add_bias_logits (:obj:`bool`, `optional`, defaults to :obj:`False`):
......@@ -128,11 +127,11 @@ class PegasusConfig(BartConfig):
bos_token_id (:obj:`int`, `optional`, defaults to 0)
Beginning of stream token id.
encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the encoder. See the `LayerDrop paper
<see https://arxiv.org/abs/1909.11556>`__ for more details.
The LayerDrop probability for the encoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the decoder. See the `LayerDrop paper
<see https://arxiv.org/abs/1909.11556>`__ for more details.
The LayerDrop probability for the decoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
extra_pos_embeddings: (:obj:`int`, `optional`, defaults to 2):
How many extra learned positional embeddings to use. Should be pad_token_id+1 for bart.
is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`True`):
......
src/transformers/configuration_prophetnet.py
View file @ 08f534d2
......@@ -28,22 +28,21 @@ PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class ProphetNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.ProphetNetModel`. It is used to
instantiate a ProphetNet model according to the specified arguments, defining the model architecture.
This is the configuration class to store the configuration of a :class:`~transformers.ProphetNetModel`. It is used
to instantiate a ProphetNet model according to the specified arguments, defining the model architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
activation_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.ProphetNetModel`.
Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.ProphetNetModel`.
hidden_size (:obj:`int`, `optional`, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
......@@ -63,8 +62,8 @@ class ProphetNetConfig(PretrainedConfig):
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
add_cross_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
......@@ -78,21 +77,19 @@ class ProphetNetConfig(PretrainedConfig):
eos_token_id (:obj:`int`, `optional`, defaults to 2)
End of stream token id.
ngram (:obj:`int`, `optional`, defaults to 2)
Number of future tokens to predict.
Set to 1 to be same as traditional Language model to predict next first token.
Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
token.
num_buckets (:obj:`int`, `optional`, defaults to 32)
The number of buckets to use for each attention layer.
This is for relative position calculation. See the `T5 paper
<see https://arxiv.org/abs/1910.10683>`__ for more details.
The number of buckets to use for each attention layer. This is for relative position calculation. See the
`T5 paper <see https://arxiv.org/abs/1910.10683>`__ for more details.
relative_max_distance (:obj:`int`, `optional`, defaults to 128)
Relative distances greater than this number will be put into the last same bucket.
This is for relative position calculation. See the `T5 paper
<see https://arxiv.org/abs/1910.10683>`__ for more details.
Relative distances greater than this number will be put into the last same bucket. This is for relative
position calculation. See the `T5 paper <see https://arxiv.org/abs/1910.10683>`__ for more details.
disable_ngram_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether be trained predicting only the next first token.
eps (:obj:`float`, `optional`, defaults to 0.0):
Controls the ``epsilon`` parameter value for label
smoothing in the loss calculation. If set to 0, no label smoothing is performed.
Controls the ``epsilon`` parameter value for label smoothing in the loss calculation. If set to 0, no label
smoothing is performed.
"""
model_type = "prophetnet"
......
src/transformers/configuration_rag.py
View file @ 08f534d2
......@@ -21,16 +21,17 @@ from .file_utils import add_start_docstrings
RAG_CONFIG_DOC = r"""
:class:`~transformers.RagConfig` stores the configuration of a `RagModel`.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
:class:`~transformers.RagConfig` stores the configuration of a `RagModel`. Configuration objects inherit from
:class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from
:class:`~transformers.PretrainedConfig` for more information.
Args:
title_sep (:obj:`str`, `optional`, defaults to ``" / "``):
Separator inserted between the title and the text of the retrieved document when calling :class:`~transformers.RagRetriever`.
Separator inserted between the title and the text of the retrieved document when calling
:class:`~transformers.RagRetriever`.
doc_sep (:obj:`str`, `optional`, defaults to ``" // "``):
Separator inserted between the the text of the retrieved document and the original input when calliang :class:`~transformers.RagRetriever`.
Separator inserted between the the text of the retrieved document and the original input when calliang
:class:`~transformers.RagRetriever`.
n_docs (:obj:`int`, `optional`, defaults to 5):
Number of documents to retrieve.
max_combined_length (:obj:`int`, `optional`, defaults to 300):
......@@ -41,8 +42,8 @@ RAG_CONFIG_DOC = r"""
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index excapsulated
:class:`~transformers.RagRetriever`.
dataset (:obj:`str`, `optional`, defaults to :obj:`"wiki_dpr"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and
ids using :obj:`datasets.list_datasets()`).
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using :obj:`datasets.list_datasets()`).
dataset_split (:obj:`str`, `optional`, defaults to :obj:`"train"`)
Which split of the :obj:`dataset` to load.
index_name (:obj:`str`, `optional`, defaults to :obj:`"compressed"`)
......@@ -59,13 +60,13 @@ RAG_CONFIG_DOC = r"""
Only relevant if ``return_loss`` is set to :obj:`True`. Controls the ``epsilon`` parameter value for label
smoothing in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`True`, the logits are marginalized over all documents
by making use of ``torch.nn.functional.log_softmax``.
If :obj:`True`, the logits are marginalized over all documents by making use of
``torch.nn.functional.log_softmax``.
reduce_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to reduce the NLL loss using the ``torch.Tensor.sum`` operation.
do_deduplication (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to deduplicate the generations from different context documents for a given input.
Has to be set to :obj:`False` if used while training with distributed backend.
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to :obj:`False` if used while training with distributed backend.
exclude_bos_score (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(:obj:`bool`, `optional`, defaults to :obj:`False`):
......@@ -160,7 +161,8 @@ class RagConfig(PretrainedConfig):
cls, question_encoder_config: PretrainedConfig, generator_config: PretrainedConfig, **kwargs
) -> PretrainedConfig:
r"""
Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.
Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model
configuration and decoder model configuration.
Returns:
:class:`EncoderDecoderConfig`: An instance of a configuration object
......@@ -169,7 +171,8 @@ class RagConfig(PretrainedConfig):
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default :meth:`~transformers.PretrainedConfig.to_dict`.
Serializes this instance to a Python dictionary. Override the default
:meth:`~transformers.PretrainedConfig.to_dict`.
Returns:
:obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
......
src/transformers/configuration_reformer.py
View file @ 08f534d2
......@@ -32,16 +32,15 @@ class ReformerConfig(PretrainedConfig):
This is the configuration class to store the configuration of a :class:`~transformers.ReformerModel`. It is used to
instantiate a Reformer model according to the specified arguments, defining the model architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
attention_head_size (:obj:`int`, `optional`, defaults to 64):
Dimensionality of the projected key, query and value vectors
attn_layers (:obj:`List[str]`, `optional`, defaults to :obj:`["local", "lsh", "local", "lsh", "local", "lsh"]`):
List of attention layer types in ascending order. It can be chosen between a
LSHSelfAttention layer (:obj:`"lsh"`) and a LocalSelfAttention layer (:obj:`"local"`).
List of attention layer types in ascending order. It can be chosen between a LSHSelfAttention layer
(:obj:`"lsh"`) and a LocalSelfAttention layer (:obj:`"local"`).
For more information on LSHSelfAttention layer, see `LSH Self Attention
<reformer.html#lsh-self-attention>`__. For more information on LocalSelfAttention layer, see `Local Self
......@@ -65,9 +64,9 @@ class ReformerConfig(PretrainedConfig):
For more information on how axial position embeddings work, see `Axial Position Encodings
<reformer.html#axial-positional-encodings>`__.
chunk_size_lm_head (:obj:`int`, `optional`, defaults to 0):
The chunk size of the final language model feed forward head layer.
A chunk size of 0 means that the feed forward layer is not chunked.
A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time.
The chunk size of the final language model feed forward head layer. A chunk size of 0 means that the feed
forward layer is not chunked. A chunk size of n means that the feed forward layer processes n <
sequence_length embeddings at a time.
For more information on feed forward chunking, see `How does Feed Forward Chunking work?
<../glossary.html#feed-forward-chunking>`__.
......@@ -81,8 +80,7 @@ class ReformerConfig(PretrainedConfig):
:obj:`None` to ensure fully random rotations in local sensitive hashing scheme.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"relu"`):
The non-linear activation function (function or string) in the feed forward layer in the residual attention
block.
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
block. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.05):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
hidden_size (:obj:`int`, `optional`, defaults to 256):
......@@ -97,8 +95,8 @@ class ReformerConfig(PretrainedConfig):
The epsilon used by the layer normalization layers.
local_chunk_length (:obj:`int`, `optional`, defaults to 64):
Length of chunk which attends to itself in :obj:`LocalSelfAttention`. Chunking reduces memory complexity
from sequence length x sequence length (self attention) to
chunk length x chunk length x sequence length / chunk length (chunked self attention).
from sequence length x sequence length (self attention) to chunk length x chunk length x sequence length /
chunk length (chunked self attention).
local_num_chunks_before (:obj:`int`, `optional`, defaults to 1):
Number of previous neighbouring chunks to attend to in :obj:`LocalSelfAttention` layer to itself.
local_num_chunks_after (:obj:`int`, `optional`, defaults to 0):
......@@ -108,8 +106,8 @@ class ReformerConfig(PretrainedConfig):
The dropout ratio for the attention probabilities in :obj:`LocalSelfAttention`.
lsh_attn_chunk_length (:obj:`int`, `optional`, defaults to 64):
Length of chunk which attends to itself in :obj:`LSHSelfAttention`. Chunking reduces memory complexity from
sequence length x sequence length (self attention) to
chunk length x chunk length x sequence length / chunk length (chunked self attention).
sequence length x sequence length (self attention) to chunk length x chunk length x sequence length / chunk
length (chunked self attention).
lsh_num_chunks_before (:obj:`int`, `optional`, defaults to 1):
Number of previous neighbouring chunks to attend to in :obj:`LSHSelfAttention` layer to itself.
lsh_num_chunks_after (:obj:`int`, `optional`, defaults to 0):
......@@ -117,23 +115,22 @@ class ReformerConfig(PretrainedConfig):
lsh_attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities in :obj:`LSHSelfAttention`.
max_position_embeddings (:obj:`int`, `optional`, defaults to 4096):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_buckets (:obj:`int` or :obj:`List[int]`, `optional`):
Number of buckets, the key query vectors can be "hashed into" using the locality sensitive hashing scheme.
Each query key vector is hashed into a hash in :obj:`1, ..., num_buckets`.
The number of buckets can also be factorized into a list for improved memory complexity. In this case, each
query key vector is hashed into a hash in
:obj:`1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if :obj:`num_buckets` is
factorized into two factors.
The number of buckets (or the product the factors) should approximately equal
sequence length / lsh_chunk_length. If :obj:`num_buckets` not set, a good value is calculated on the fly.
Each query key vector is hashed into a hash in :obj:`1, ..., num_buckets`. The number of buckets can also
be factorized into a list for improved memory complexity. In this case, each query key vector is hashed
into a hash in :obj:`1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if
:obj:`num_buckets` is factorized into two factors. The number of buckets (or the product the factors)
should approximately equal sequence length / lsh_chunk_length. If :obj:`num_buckets` not set, a good value
is calculated on the fly.
num_hashes (:obj:`int`, `optional`, defaults to 1):
Number of hashing rounds (e.g., number of random rotations) in Local Sensitive Hashing scheme.
The higher :obj:`num_hashes`, the more accurate the :obj:`LSHSelfAttention` becomes, but also the more
memory and time intensive the hashing becomes.
Number of hashing rounds (e.g., number of random rotations) in Local Sensitive Hashing scheme. The higher
:obj:`num_hashes`, the more accurate the :obj:`LSHSelfAttention` becomes, but also the more memory and time
intensive the hashing becomes.
pad_token_id (:obj:`int`, `optional`, defaults to 0):
The token id for the padding token.
vocab_size (:obj:`int`, `optional`, defaults to 320):\
......
src/transformers/configuration_retribert.py
View file @ 08f534d2
......@@ -28,13 +28,11 @@ RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class RetriBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.RetriBertModel`.
It is used to instantiate a RetriBertModel model according to the specified arguments, defining the model
architecture.
This is the configuration class to store the configuration of a :class:`~transformers.RetriBertModel`. It is used
to instantiate a RetriBertModel model according to the specified arguments, defining the model architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
......@@ -50,15 +48,15 @@ class RetriBertConfig(PretrainedConfig):
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
......
src/transformers/configuration_roberta.py
View file @ 08f534d2
......@@ -38,12 +38,11 @@ class RobertaConfig(BertConfig):
arguments, defining the model architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
The :class:`~transformers.RobertaConfig` class directly inherits :class:`~transformers.BertConfig`.
It reuses the same defaults. Please check the parent class for more information.
The :class:`~transformers.RobertaConfig` class directly inherits :class:`~transformers.BertConfig`. It reuses the
same defaults. Please check the parent class for more information.
Examples::
......
src/transformers/configuration_squeezebert.py
View file @ 08f534d2
......@@ -29,19 +29,17 @@ SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class SqueezeBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.SqueezeBertModel`.
It is used to instantiate a SqueezeBERT model according to the specified arguments, defining the model
architecture.
This is the configuration class to store the configuration of a :class:`~transformers.SqueezeBertModel`. It is used
to instantiate a SqueezeBERT model according to the specified arguments, defining the model architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be
represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.SqueezeBertModel`.
Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.SqueezeBertModel`.
hidden_size (:obj:`int`, `optional`, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
......@@ -51,15 +49,15 @@ class SqueezeBertConfig(PretrainedConfig):
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or
:class:`~transformers.TFBertModel`.
......@@ -85,7 +83,7 @@ class SqueezeBertConfig(PretrainedConfig):
output_groups (:obj:`int`, `optional`, defaults to 4):
The number of groups in the third feed forward network layer.
Example:
Examples::
>>> from transformers import SqueezeBertModel, SqueezeBertConfig
......@@ -98,9 +96,8 @@ class SqueezeBertConfig(PretrainedConfig):
>>> # Accessing the model configuration
>>> configuration = model.config
Attributes:
pretrained_config_archive_map (Dict[str, str]):
A dictionary containing all the available pre-trained checkpoints.
Attributes: pretrained_config_archive_map (Dict[str, str]): A dictionary containing all the available pre-trained
checkpoints.
"""
pretrained_config_archive_map = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "squeezebert"
......
src/transformers/configuration_t5.py
View file @ 08f534d2
......@@ -32,36 +32,34 @@ T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class T5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.T5Model` or a
:class:`~transformers.TFT5Model`. It is used to instantiate a T5 model according to the specified
arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
configuration to that of the T5 `t5-small <https://huggingface.co/t5-small>`__ architecture.
:class:`~transformers.TFT5Model`. It is used to instantiate a T5 model according to the specified arguments,
defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration
to that of the T5 `t5-small <https://huggingface.co/t5-small>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Arguments:
vocab_size (:obj:`int`, `optional`, defaults to 32128):
Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.T5Model` or
:class:`~transformers.TFT5Model`.
:obj:`inputs_ids` passed when calling :class:`~transformers.T5Model` or :class:`~transformers.TFT5Model`.
n_positions (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
d_model (:obj:`int`, `optional`, defaults to 512):
Size of the encoder layers and the pooler layer.
d_kv (:obj:`int`, `optional`, defaults to 64):
Size of the key, query, value projections per attention head. :obj:`d_kv` has to be equal to
:obj:`d_model // num_heads`.
Size of the key, query, value projections per attention head. :obj:`d_kv` has to be equal to :obj:`d_model
// num_heads`.
d_ff (:obj:`int`, `optional`, defaults to 2048):
Size of the intermediate feed forward layer in each :obj:`T5Block`.
num_layers (:obj:`int`, `optional`, defaults to 6):
Number of hidden layers in the Transformer encoder.
num_decoder_layers (:obj:`int`, `optional`):
Number of hidden layers in the Transformer decoder. Will use the same value as :obj:`num_layers` if not set.
Number of hidden layers in the Transformer decoder. Will use the same value as :obj:`num_layers` if not
set.
num_heads (:obj:`int`, `optional`, defaults to 8):
Number of attention heads for each attention layer in
the Transformer encoder.
Number of attention heads for each attention layer in the Transformer encoder.
relative_attention_num_buckets (:obj:`int`, `optional`, defaults to 32):
The number of buckets to use for each attention layer.
dropout_rate (:obj:`float`, `optional`, defaults to 0.1):
......
src/transformers/configuration_transfo_xl.py
View file @ 08f534d2
......@@ -32,13 +32,12 @@ TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class TransfoXLConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.TransfoXLModel` or a
:class:`~transformers.TFTransfoXLModel`. It is used to instantiate a Transformer-XL model according to the specified
arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
configuration to that of the `Transformer XL <https://huggingface.co/transfo-xl-wt103>`__ architecture.
:class:`~transformers.TFTransfoXLModel`. It is used to instantiate a Transformer-XL model according to the
specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a
similar configuration to that of the `Transformer XL <https://huggingface.co/transfo-xl-wt103>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 267735):
......
src/transformers/configuration_utils.py
View file @ 08f534d2
......@@ -29,27 +29,26 @@ logger = logging.get_logger(__name__)
class PretrainedConfig(object):
r"""Base class for all configuration classes.
Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving
configurations.
r"""
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
Note:
A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** load the model weights.
It only affects the model's configuration.
Note: A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** load the model weights. It only affects the model's configuration.
Class attributes (overridden by derived classes)
- **model_type** (:obj:`str`): An identifier for the model type, serialized into the JSON file, and used to
recreate the correct object in :class:`~transformers.AutoConfig`.
- **is_composition** (:obj:`bool`): Whether the config class is composed of multiple
sub-configs. In this case the config has to be initialized from two or more configs of
type :class:`~transformers.PretrainedConfig` like: :class:`~transformers.EncoderDecoderConfig` or
:class:`~RagConfig`.
- **is_composition** (:obj:`bool`): Whether the config class is composed of multiple sub-configs. In this case
the config has to be initialized from two or more configs of type :class:`~transformers.PretrainedConfig`
like: :class:`~transformers.EncoderDecoderConfig` or :class:`~RagConfig`.
Args:
name_or_path (:obj:`str`, `optional`, defaults to :obj:`""`):
Store the string that was passed to :func:`~transformers.PreTrainedModel.from_pretrained` or :func:`~transformers.TFPreTrainedModel.from_pretrained`
as ``pretrained_model_name_or_path`` if the configuration was created with such a method.
Store the string that was passed to :func:`~transformers.PreTrainedModel.from_pretrained` or
:func:`~transformers.TFPreTrainedModel.from_pretrained` as ``pretrained_model_name_or_path`` if the
configuration was created with such a method.
output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should return all hidden-states.
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
......@@ -57,68 +56,72 @@ class PretrainedConfig(object):
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models).
return_dict (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should return a :class:`~transformers.file_utils.ModelOutput` instead of a
plain tuple.
Whether or not the model should return a :class:`~transformers.file_utils.ModelOutput` instead of a plain
tuple.
is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether the model is used as an encoder/decoder or not.
is_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether the model is used as decoder or not (in which case it's used as an encoder).
add_cross_attention (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether cross-attention layers should be added to the model. Note, this option is only relevant for models that can be used as decoder models within the `:class:~transformers.EncoderDecoderModel` class, which consists of all models in ``AUTO_MODELS_FOR_CAUSAL_LM``.
Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
that can be used as decoder models within the `:class:~transformers.EncoderDecoderModel` class, which
consists of all models in ``AUTO_MODELS_FOR_CAUSAL_LM``.
tie_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`)
Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder and decoder model to have the exact same parameter names.
Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
and decoder model to have the exact same parameter names.
prune_heads (:obj:`Dict[int, List[int]]`, `optional`, defaults to :obj:`{}`):
Pruned heads of the model. The keys are the selected layer indices and the associated values, the list
of heads to prune in said layer.
Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
heads to prune in said layer.
For instance ``{1: [0, 2], 2: [2, 3]}`` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer
2.
For instance ``{1: [0, 2], 2: [2, 3]}`` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
xla_device (:obj:`bool`, `optional`):
A flag to indicate if TPU are available or not.
chunk_size_feed_forward (:obj:`int`, `optional`, defaults to :obj:`0`):
The chunk size of all feed forward layers in the residual attention blocks.
A chunk size of :obj:`0` means that the feed forward layer is not chunked.
A chunk size of n means that the feed forward layer processes :obj:`n` < sequence_length embeddings at a time.
For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
The chunk size of all feed forward layers in the residual attention blocks. A chunk size of :obj:`0` means
that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes
:obj:`n` < sequence_length embeddings at a time. For more information on feed forward chunking, see `How
does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
Parameters for sequence generation
- **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by
default in the :obj:`generate` method of the model.
- **min_length** (:obj:`int`, `optional`, defaults to 10) -- Minimum length that will be used by
default in the :obj:`generate` method of the model.
- **do_sample** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by default in
the :obj:`generate` method of the model. Whether or not to use sampling ; use greedy decoding otherwise.
- **early_stopping** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by
default in the :obj:`generate` method of the model. Whether to stop the beam search when at least
``num_beams`` sentences are finished per batch or not.
- **num_beams** (:obj:`int`, `optional`, defaults to 1) -- Number of beams for beam search that will be
used by default in the :obj:`generate` method of the model. 1 means no beam search.
- **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by default in the
:obj:`generate` method of the model.
- **min_length** (:obj:`int`, `optional`, defaults to 10) -- Minimum length that will be used by default in the
:obj:`generate` method of the model.
- **do_sample** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by default in the
:obj:`generate` method of the model. Whether or not to use sampling ; use greedy decoding otherwise.
- **early_stopping** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by default
in the :obj:`generate` method of the model. Whether to stop the beam search when at least ``num_beams``
sentences are finished per batch or not.
- **num_beams** (:obj:`int`, `optional`, defaults to 1) -- Number of beams for beam search that will be used by
default in the :obj:`generate` method of the model. 1 means no beam search.
- **temperature** (:obj:`float`, `optional`, defaults to 1) -- The value used to module the next token
probabilities that will be used by default in the :obj:`generate` method of the model. Must be strictly
positive.
- **top_k** (:obj:`int`, `optional`, defaults to 50) -- Number of highest probability vocabulary tokens to
keep for top-k-filtering that will be used by default in the :obj:`generate` method of the model.
- **top_p** (:obj:`float`, `optional`, defaults to 1) -- Value that will be used by default in the
:obj:`generate` method of the model for ``top_p``. If set to float < 1, only the most probable tokens
with probabilities that add up to ``top_p`` or higher are kept for generation.
- **repetition_penalty** (:obj:`float`, `optional`, defaults to 1) -- Parameter for repetition penalty
that will be used by default in the :obj:`generate` method of the model. 1.0 means no penalty.
- **length_penalty** (:obj:`float`, `optional`, defaults to 1) -- Exponential penalty to the length that
will be used by default in the :obj:`generate` method of the model.
- **no_repeat_ngram_size** (:obj:`int`, `optional`, defaults to 0) -- Value that will be used by default
in the :obj:`generate` method of the model for ``no_repeat_ngram_size``. If set to int > 0, all ngrams of
that size can only occur once.
- **bad_words_ids** (:obj:`List[int]`, `optional`) -- List of token ids that are not allowed to be
generated that will be used by default in the :obj:`generate` method of the model. In order to get the
tokens of the words that should not appear in the generated text, use
:obj:`tokenizer.encode(bad_word, add_prefix_space=True)`.
- **num_return_sequences** (:obj:`int`, `optional`, defaults to 1) -- Number of independently computed
returned sequences for each element in the batch that will be used by default in the :obj:`generate`
method of the model.
- **top_k** (:obj:`int`, `optional`, defaults to 50) -- Number of highest probability vocabulary tokens to keep
for top-k-filtering that will be used by default in the :obj:`generate` method of the model.
- **top_p** (:obj:`float`, `optional`, defaults to 1) -- Value that will be used by default in the
:obj:`generate` method of the model for ``top_p``. If set to float < 1, only the most probable tokens with
probabilities that add up to ``top_p`` or higher are kept for generation.
- **repetition_penalty** (:obj:`float`, `optional`, defaults to 1) -- Parameter for repetition penalty that
will be used by default in the :obj:`generate` method of the model. 1.0 means no penalty.
- **length_penalty** (:obj:`float`, `optional`, defaults to 1) -- Exponential penalty to the length that will
be used by default in the :obj:`generate` method of the model.
- **no_repeat_ngram_size** (:obj:`int`, `optional`, defaults to 0) -- Value that will be used by default in the
:obj:`generate` method of the model for ``no_repeat_ngram_size``. If set to int > 0, all ngrams of that size
can only occur once.
- **bad_words_ids** (:obj:`List[int]`, `optional`) -- List of token ids that are not allowed to be generated
that will be used by default in the :obj:`generate` method of the model. In order to get the tokens of the
words that should not appear in the generated text, use :obj:`tokenizer.encode(bad_word,
add_prefix_space=True)`.
- **num_return_sequences** (:obj:`int`, `optional`, defaults to 1) -- Number of independently computed returned
sequences for each element in the batch that will be used by default in the :obj:`generate` method of the
model.
Parameters for fine-tuning tasks
- **architectures** (:obj:`List[str]`, `optional`) -- Model architectures that can be used with the
model pretrained weights.
- **architectures** (:obj:`List[str]`, `optional`) -- Model architectures that can be used with the model
pretrained weights.
- **finetuning_task** (:obj:`str`, `optional`) -- Name of the task used to fine-tune the model. This can be
used when converting from an original (TensorFlow or PyTorch) checkpoint.
- **id2label** (:obj:`Dict[int, str]`, `optional`) -- A map from index (for instance prediction index, or
......@@ -126,27 +129,32 @@ class PretrainedConfig(object):
- **label2id** (:obj:`Dict[str, int]`, `optional`) -- A map from label to index for the model.
- **num_labels** (:obj:`int`, `optional`) -- Number of labels to use in the last layer added to the model,
typically for a classification task.
- **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for
the current task.
- **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for the
current task.
Parameters linked to the tokenizer
- **prefix** (:obj:`str`, `optional`) -- A specific prompt that should be added at the beginning of each
text before calling the model.
- **prefix** (:obj:`str`, `optional`) -- A specific prompt that should be added at the beginning of each text
before calling the model.
- **bos_token_id** (:obj:`int`, `optional`)) -- The id of the `beginning-of-stream` token.
- **pad_token_id** (:obj:`int`, `optional`)) -- The id of the `padding` token.
- **eos_token_id** (:obj:`int`, `optional`)) -- The id of the `end-of-stream` token.
- **decoder_start_token_id** (:obj:`int`, `optional`)) -- If an encoder-decoder model starts decoding with
a different token than `bos`, the id of that token.
- **decoder_start_token_id** (:obj:`int`, `optional`)) -- If an encoder-decoder model starts decoding with a
different token than `bos`, the id of that token.
- **sep_token_id** (:obj:`int`, `optional`)) -- The id of the `separation` token.
PyTorch specific parameters
- **torchscript** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should be
used with Torchscript.
- **tie_word_embeddings** (:obj:`bool`, `optional`, defaults to :obj:`True`) -- Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has a output word embedding layer.
- **tie_word_embeddings** (:obj:`bool`, `optional`, defaults to :obj:`True`) -- Whether the model's input and
output word embeddings should be tied. Note that this is only relevant if the model has a output word
embedding layer.
TensorFlow specific parameters
- **use_bfloat16** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should
use BFloat16 scalars (only used by some TensorFlow models).
- **use_bfloat16** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should use
BFloat16 scalars (only used by some TensorFlow models).
"""
model_type: str = ""
is_composition: bool = False
......@@ -293,15 +301,14 @@ class PretrainedConfig(object):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.
proxies (:obj:`Dict[str, str]`, `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g.,
:obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.`
The proxies are used on each request.
A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`False`, then this function returns just the final configuration object.
......@@ -310,8 +317,8 @@ class PretrainedConfig(object):
the part of ``kwargs`` which has not been used to update ``config`` and is otherwise ignored.
kwargs (:obj:`Dict[str, Any]`, `optional`):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is
controlled by the ``return_unused_kwargs`` keyword parameter.
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
by the ``return_unused_kwargs`` keyword parameter.
Returns:
:class:`PretrainedConfig`: The configuration object instantiated from this pretrained model.
......@@ -337,8 +344,8 @@ class PretrainedConfig(object):
@classmethod
def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used
for instantiating a :class:`~transformers.PretrainedConfig` using ``from_dict``.
From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a
:class:`~transformers.PretrainedConfig` using ``from_dict``.
Parameters:
pretrained_model_name_or_path (:obj:`str`):
......@@ -469,9 +476,8 @@ class PretrainedConfig(object):
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default
config attributes for better readability and serializes to a Python
dictionary.
Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
Returns:
:obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
......
src/transformers/configuration_xlm.py
View file @ 08f534d2
......@@ -37,19 +37,17 @@ XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLMConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.XLMModel` or a
:class:`~transformers.TFXLMModel`. It is used to instantiate a XLM model according to the specified
arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
configuration to that of the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
:class:`~transformers.TFXLMModel`. It is used to instantiate a XLM model according to the specified arguments,
defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration
to that of the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 30145):
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.XLMModel` or
:class:`~transformers.TFXLMModel`.
:obj:`inputs_ids` passed when calling :class:`~transformers.XLMModel` or :class:`~transformers.TFXLMModel`.
emb_dim (:obj:`int`, `optional`, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
n_layer (:obj:`int`, `optional`, defaults to 12):
......@@ -57,8 +55,7 @@ class XLMConfig(PretrainedConfig):
n_head (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected
layers in the embeddings, encoder, and pooler.
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for the attention mechanism
gelu_activation (:obj:`bool`, `optional`, defaults to :obj:`True`):
......@@ -66,28 +63,25 @@ class XLMConfig(PretrainedConfig):
sinusoidal_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
causal (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should behave in a causal manner.
Causal models use a triangular attention mask in order to only attend to the left-side context instead
if a bidirectional context.
Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in
order to only attend to the left-side context instead if a bidirectional context.
asm (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
layer.
n_langs (:obj:`int`, `optional`, defaults to 1):
The number of languages the model handles. Set to 1 for monolingual models.
use_lang_emb (:obj:`bool`, `optional`, defaults to :obj:`True`)
Whether to use language embeddings. Some models use additional language embeddings, see
`the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
for information on how to use them.
Whether to use language embeddings. Some models use additional language embeddings, see `the multilingual
models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__ for
information on how to use them.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
embed_init_std (:obj:`float`, `optional`, defaults to 2048^-0.5):
The standard deviation of the truncated_normal_initializer for
initializing the embedding matrices.
The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
init_std (:obj:`int`, `optional`, defaults to 50257):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices except the embedding matrices.
The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the
embedding matrices.
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
The epsilon used by the layer normalization layers.
bos_index (:obj:`int`, `optional`, defaults to 0):
......@@ -135,8 +129,7 @@ class XLMConfig(PretrainedConfig):
mask_token_id (:obj:`int`, `optional`, defaults to 0):
Model agnostic parameter to identify masked tokens when generating text in an MLM context.
lang_id (:obj:`int`, `optional`, defaults to 1):
The ID of the language used by the model. This parameter is used when generating
text in a given language.
The ID of the language used by the model. This parameter is used when generating text in a given language.
Examples::
......
src/transformers/configuration_xlm_prophetnet.py
View file @ 08f534d2
......@@ -28,8 +28,8 @@ XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLMProphetNetConfig(ProphetNetConfig):
"""
This class overrides :class:`~transformers.ProphetNetConfig`. Please check the
superclass for the appropriate documentation alongside usage examples.
This class overrides :class:`~transformers.ProphetNetConfig`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
model_type = "xlm-prophetnet"
src/transformers/configuration_xlm_roberta.py
View file @ 08f534d2
......@@ -33,8 +33,8 @@ XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLMRobertaConfig(RobertaConfig):
"""
This class overrides :class:`~transformers.RobertaConfig`. Please check the
superclass for the appropriate documentation alongside usage examples.
This class overrides :class:`~transformers.RobertaConfig`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
model_type = "xlm-roberta"
src/transformers/configuration_xlnet.py
View file @ 08f534d2
......@@ -32,13 +32,12 @@ XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLNetConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a :class:`~transformers.XLNetModel` or a
:class:`~transformers.TFXLNetModel`. It is used to instantiate a XLNet model according to the specified
arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
configuration to that of the `xlnet-large-cased <https://huggingface.co/xlnet-large-cased>`__ architecture.
:class:`~transformers.TFXLNetModel`. It is used to instantiate a XLNet model according to the specified arguments,
defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration
to that of the `xlnet-large-cased <https://huggingface.co/xlnet-large-cased>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 32000):
......@@ -54,8 +53,8 @@ class XLNetConfig(PretrainedConfig):
d_inner (:obj:`int`, `optional`, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
ff_activation (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
The non-linear activation function (function or string) in the If string, :obj:`"gelu"`, :obj:`"relu"`,
:obj:`"swish"` and :obj:`"gelu_new"` are supported.
untie_r (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to untie relative position biases
attn_type (:obj:`str`, `optional`, defaults to :obj:`"bi"`):
......@@ -67,18 +66,16 @@ class XLNetConfig(PretrainedConfig):
dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
mem_len (:obj:`int` or :obj:`None`, `optional`):
The number of tokens to cache. The key/value pairs that have already been pre-computed
in a previous forward pass won't be re-computed. See the
`quickstart <https://huggingface.co/transformers/quickstart.html#using-the-past>`__
for more information.
The number of tokens to cache. The key/value pairs that have already been pre-computed in a previous
forward pass won't be re-computed. See the `quickstart
<https://huggingface.co/transformers/quickstart.html#using-the-past>`__ for more information.
reuse_len (:obj:`int`, `optional`):
The number of tokens in the current batch to be cached and reused in the future.
bi_data (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use bidirectional input pipeline. Usually set to :obj:`True` during
pretraining and :obj:`False` during finetuning.
Whether or not to use bidirectional input pipeline. Usually set to :obj:`True` during pretraining and
:obj:`False` during finetuning.
clamp_len (:obj:`int`, `optional`, defaults to -1):
Clamp all relative distances larger than clamp_len.
Setting this attribute to -1 means no clamping.
Clamp all relative distances larger than clamp_len. Setting this attribute to -1 means no clamping.
same_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the same attention length for each token.
summary_type (:obj:`str`, `optional`, defaults to "last"):
......
src/transformers/convert_bert_original_tf2_checkpoint_to_pytorch.py
View file @ 08f534d2
"""
This script can be used to convert a head-less TF2.x Bert model to PyTorch,
as published on the official GitHub: https://github.com/tensorflow/models/tree/master/official/nlp/bert
This script can be used to convert a head-less TF2.x Bert model to PyTorch, as published on the official GitHub:
https://github.com/tensorflow/models/tree/master/official/nlp/bert
TF2.x uses different variable names from the original BERT (TF 1.4) implementation.
The script re-maps the TF2.x Bert weight names to the original names, so the model can be imported with Huggingface/transformer.
TF2.x uses different variable names from the original BERT (TF 1.4) implementation. The script re-maps the TF2.x Bert
weight names to the original names, so the model can be imported with Huggingface/transformer.
You may adapt this script to include classification/MLM/NSP/etc. heads.
"""
......
src/transformers/convert_bert_pytorch_checkpoint_to_original_tf.py
View file @ 08f534d2
......@@ -28,12 +28,13 @@ from transformers import BertModel
def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name: str):
"""
Args
Args:
model: BertModel Pytorch model instance to be converted
ckpt_dir: Tensorflow model directory
model_name: model name
Currently supported HF models:
- Y BertModel
- N BertForMaskedLM
- N BertForPreTraining
......
src/transformers/convert_graph_to_onnx.py
View file @ 08f534d2
......@@ -77,7 +77,8 @@ class OnnxConverterArgumentParser(ArgumentParser):
def generate_identified_filename(filename: Path, identifier: str) -> Path:
"""
Append a string-identifier at the end (before the extension, if any) to the provided filepath.
Append a string-identifier at the end (before the extension, if any) to the provided filepath
Args:
filename: pathlib.Path The actual path object we would like to add an identifier suffix
identifier: The suffix to add
......@@ -89,7 +90,8 @@ def generate_identified_filename(filename: Path, identifier: str) -> Path:
def check_onnxruntime_requirements(minimum_version: Version):
"""
Check onnxruntime is installed and if the installed version match is recent enough.
Check onnxruntime is installed and if the installed version match is recent enough
Raises:
ImportError: If onnxruntime is not installed or too old version is found
"""
......@@ -117,7 +119,8 @@ def check_onnxruntime_requirements(minimum_version: Version):
def ensure_valid_input(model, tokens, input_names):
"""
Ensure input are presented in the correct order, without any None
Ensure input are presented in the correct order, without any Non
Args:
model: The model used to forward the input data
tokens: BatchEncoding holding the input data
......@@ -144,12 +147,14 @@ def ensure_valid_input(model, tokens, input_names):
def infer_shapes(nlp: Pipeline, framework: str) -> Tuple[List[str], List[str], Dict, BatchEncoding]:
"""
Attempt to infer the static vs dynamic axes for each input and output tensors for a specific model.
Attempt to infer the static vs dynamic axes for each input and output tensors for a specific model
Args:
nlp: The pipeline object holding the model to be exported
framework: The framework identifier to dispatch to the correct inference scheme (pt/tf)
Returns:
- List of the inferred input variable names
- List of the inferred output variable names
- Dictionary with input/output variables names as key and shape tensor as value
......@@ -206,7 +211,8 @@ def infer_shapes(nlp: Pipeline, framework: str) -> Tuple[List[str], List[str], D
def load_graph_from_args(pipeline_name: str, framework: str, model: str, tokenizer: Optional[str] = None) -> Pipeline:
"""
Convert the set of arguments provided through the CLI to an actual pipeline reference (tokenizer + model)
Convert the set of arguments provided through the CLI to an actual pipeline reference (tokenizer + model
Args:
pipeline_name: The kind of pipeline to use (ner, question-answering, etc.)
framework: The actual model to convert the pipeline from ("pt" or "tf")
......@@ -234,7 +240,8 @@ def load_graph_from_args(pipeline_name: str, framework: str, model: str, tokeniz
def convert_pytorch(nlp: Pipeline, opset: int, output: Path, use_external_format: bool):
"""
Export a PyTorch backed pipeline to ONNX Intermediate Representation (IR)
Export a PyTorch backed pipeline to ONNX Intermediate Representation (IR
Args:
nlp: The pipeline to be exported
opset: The actual version of the ONNX operator set to use
......@@ -272,7 +279,8 @@ def convert_pytorch(nlp: Pipeline, opset: int, output: Path, use_external_format
def convert_tensorflow(nlp: Pipeline, opset: int, output: Path):
"""
Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR)
Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR
Args:
nlp: The pipeline to be exported
opset: The actual version of the ONNX operator set to use
......@@ -316,7 +324,8 @@ def convert(
pipeline_name: str = "feature-extraction",
):
"""
Convert the pipeline object to the ONNX Intermediate Representation (IR) format.
Convert the pipeline object to the ONNX Intermediate Representation (IR) format
Args:
framework: The framework the pipeline is backed by ("pt" or "tf")
model: The name of the model to load for the pipeline
......@@ -349,8 +358,9 @@ def convert(
def optimize(onnx_model_path: Path) -> Path:
"""
Load the model at the specified path and let onnxruntime look at transformations on the graph
to enable all the optimizations possible
Load the model at the specified path and let onnxruntime look at transformations on the graph to enable all the
optimizations possibl
Args:
onnx_model_path: filepath where the model binary description is stored
......@@ -373,7 +383,8 @@ def optimize(onnx_model_path: Path) -> Path:
def quantize(onnx_model_path: Path) -> Path:
"""
Quantize the weights of the model from float32 to in8 to allow very efficient inference on modern CPU.
Quantize the weights of the model from float32 to in8 to allow very efficient inference on modern CPU
Args:
onnx_model_path: Path to location the exported ONNX model is stored
......
src/transformers/convert_marian_tatoeba_to_pytorch.py
View file @ 08f534d2
......@@ -27,14 +27,16 @@ LANG_CODE_PATH = "lang_code_data/language-codes-3b2.csv"
class TatoebaConverter:
"""Convert Tatoeba-Challenge models to huggingface format.
"""
Convert Tatoeba-Challenge models to huggingface format.
Steps:
1. convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
2. rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique one existes.
e.g. aav-eng -> aav-en, heb-eng -> he-en
3. write a model card containing the original Tatoeba-Challenge/README.md and extra info about alpha3 group members.
2. rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
one existes. e.g. aav-eng -> aav-en, heb-eng -> he-en
3. write a model card containing the original Tatoeba-Challenge/README.md and extra info about alpha3 group
members.
"""
def __init__(self, save_dir="marian_converted"):
......@@ -148,8 +150,9 @@ class TatoebaConverter:
repo_root=DEFAULT_REPO,
dry_run=False,
) -> str:
"""Copy the most recent model's readme section from opus, and add metadata.
upload command: aws s3 sync model_card_dir s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync
model_card_dir s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
short_pair = remove_prefix(hf_model_id, "opus-mt-")
extra_metadata = self.metadata.loc[short_pair].drop("2m")
......
src/transformers/convert_marian_to_pytorch.py
View file @ 08f534d2
......@@ -152,8 +152,9 @@ def convert_opus_name_to_hf_name(x):
def convert_hf_name_to_opus_name(hf_model_name):
"""Relies on the assumption that there are no language codes like pt_br in models that are not in
GROUP_TO_OPUS_NAME."""
"""
Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME.
"""
hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
if hf_model_name in GROUP_TO_OPUS_NAME:
opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
......@@ -173,6 +174,7 @@ def get_system_metadata(repo_root):
)
# docstyle-ignore
FRONT_MATTER_TEMPLATE = """---
language:
{}
......@@ -181,7 +183,6 @@ tags:
license: apache-2.0
---
"""
DEFAULT_REPO = "Tatoeba-Challenge"
DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")
......@@ -194,8 +195,9 @@ def write_model_card(
dry_run=False,
extra_metadata={},
) -> str:
"""Copy the most recent model's readme section from opus, and add metadata.
upload command: aws s3 sync model_card_dir s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
import pandas as pd
......
src/transformers/convert_slow_tokenizer.py
View file @ 08f534d2
......@@ -12,10 +12,11 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Utilities to convert slow tokenizers in their fast tokenizers counterparts.
"""
Utilities to convert slow tokenizers in their fast tokenizers counterparts.
All the conversions are grouped here to gather SentencePiece dependencies outside of
the fast tokenizers files and allow to make our dependency on SentencePiece optional.
All the conversions are grouped here to gather SentencePiece dependencies outside of the fast tokenizers files and
allow to make our dependency on SentencePiece optional.
"""
from typing import Dict, List, Tuple
......@@ -31,8 +32,7 @@ from .file_utils import requires_sentencepiece
class SentencePieceExtractor:
"""
Extractor implementation for SentencePiece trained models.
https://github.com/google/sentencepiece
Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece
"""
def __init__(self, model: str):
......@@ -602,7 +602,8 @@ SLOW_TO_FAST_CONVERTERS = {
def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer:
"""Utilities to convert a slow tokenizer instance in a fast tokenizer instance.
"""
Utilities to convert a slow tokenizer instance in a fast tokenizer instance.
Args:
transformer_tokenizer (:class:`~transformers.tokenization_utils_base.PreTrainedTokenizer`):
......
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