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src/transformers/configuration_pegasus.py
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...@@ -92,8 +92,8 @@ expected_alpha = { ...@@ -92,8 +92,8 @@ expected_alpha = {
@add_start_docstrings_to_callable(BART_CONFIG_ARGS_DOC) @add_start_docstrings_to_callable(BART_CONFIG_ARGS_DOC)
class PegasusConfig(BartConfig): class PegasusConfig(BartConfig):
r""" r"""
:class:`~transformers.PegasusConfig` is the configuration class to store the configuration of a :class:`~transformers.PegasusConfig` is the configuration class to store the configuration of a
`PegasusModel`. `PegasusModel`.
""" """
model_type = "pegasus" model_type = "pegasus"
# The implementation of the config object is in BartConfig # The implementation of the config object is in BartConfig
src/transformers/configuration_reformer.py
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...@@ -29,105 +29,105 @@ REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -29,105 +29,105 @@ REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class ReformerConfig(PretrainedConfig): class ReformerConfig(PretrainedConfig):
r""" r"""
This is the configuration class to store the configuration of a :class:`~transformers.ReformerModel`. This is the configuration class to store the configuration of a :class:`~transformers.ReformerModel`.
It is used to instantiate an Reformer model according to the specified arguments, defining the model It is used to instantiate an Reformer model according to the specified arguments, defining the model
architecture. architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information. for more information.
Args: Args:
attention_head_size (:obj:`int`, optional, defaults to 64): attention_head_size (:obj:`int`, optional, defaults to 64):
Dimensionality of the projected key, query and value vectors Dimensionality of the projected key, query and value vectors
attn_layers (:obj:`list(str)`, optional, defaults to ["local", "lsh", "local", "lsh", "local", "lsh"]): attn_layers (:obj:`list(str)`, optional, defaults to ["local", "lsh", "local", "lsh", "local", "lsh"]):
List of attention layer types in ascending order. It can be chosen between a List of attention layer types in ascending order. It can be chosen between a
LSHSelfAttention layer ("lsh") and a LocalSelfAttention layer ("local"). LSHSelfAttention layer ("lsh") and a LocalSelfAttention layer ("local").
For more information on LSHSelfAttention layer, see `LSH Self Attention <reformer.html#lsh-self-attention>`__ . For more information on LSHSelfAttention layer, see `LSH Self Attention <reformer.html#lsh-self-attention>`__ .
For more information on LocalSelfAttention layer, see `Local Self Attention <reformer.html#local-sensitive-hashing-self-attention>`__ . For more information on LocalSelfAttention layer, see `Local Self Attention <reformer.html#local-sensitive-hashing-self-attention>`__ .
axial_pos_embds (:obj:`bool`, optional, defaults to True): axial_pos_embds (:obj:`bool`, optional, defaults to True):
If `True` use axial position embeddings. For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__ If `True` use axial position embeddings. For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__
axial_norm_std (:obj:`float`, optional, defaluts to 1.0): axial_norm_std (:obj:`float`, optional, defaluts to 1.0):
The standard deviation of the normal_initializer for initializing the weight matrices of the axial positional encodings. The standard deviation of the normal_initializer for initializing the weight matrices of the axial positional encodings.
axial_pos_shape (:obj:`list(int)`, optional, defaults to `[64, 64]`): axial_pos_shape (:obj:`list(int)`, optional, defaults to `[64, 64]`):
The position dims of the axial position encodings. The position dims of the axial position encodings.
During training the product of the position dims has to equal the sequence length. During training the product of the position dims has to equal the sequence length.
For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__. For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__.
axial_pos_embds_dim (:obj:`list(int)`, optional, defaults to `[64, 192]`): axial_pos_embds_dim (:obj:`list(int)`, optional, defaults to `[64, 192]`):
The embedding dims of the axial position encodings. The embedding dims of the axial position encodings.
The sum of the embedding dims has to equal the hidden size. The sum of the embedding dims has to equal the hidden size.
For more information on how axial position embeddings work, see `Axial Position Encodings <reformer.html#axial-positional-encodings>`__. 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): chunk_size_lm_head (:obj:`int`, optional, defaults to 0):
The chunk size of the final language model feed forward head layer. 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 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. 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>`__ . For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
eos_token_id (:obj:`int`, optional, defaults to 2): eos_token_id (:obj:`int`, optional, defaults to 2):
The token id for the <EOS> token. The token id for the <EOS> token.
feed_forward_size (:obj:`int`, optional, defaults to 512): feed_forward_size (:obj:`int`, optional, defaults to 512):
Dimensionality of the "feed_forward" (i.e., feed-forward) layer in the residual attention block. Dimensionality of the "feed_forward" (i.e., feed-forward) layer in the residual attention block.
hash_seed (:obj:`int`, optional, defaults to `None`): hash_seed (:obj:`int`, optional, defaults to `None`):
Seed that can be used to make local sensitive hashing in LSHSelfAttention deterministic. This should only be set for testing purposed. For evaluation and training purposes `hash_seed` should be set to `None` to ensure fully random rotations in local sensitive hashing scheme. Seed that can be used to make local sensitive hashing in LSHSelfAttention deterministic. This should only be set for testing purposed. For evaluation and training purposes `hash_seed` should be set to `None` to ensure fully random rotations in local sensitive hashing scheme.
hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "relu"): hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "relu"):
The non-linear activation function (function or string) in the feed forward layer in the residual attention block. The non-linear activation function (function or string) in the feed forward layer in the residual attention block.
If string, "gelu", "relu", "swish", "gelu_new" and "gelu_fast" are supported. If string, "gelu", "relu", "swish", "gelu_new" and "gelu_fast" are supported.
hidden_dropout_prob (:obj:`float`, optional, defaults to 0.05): hidden_dropout_prob (:obj:`float`, optional, defaults to 0.05):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
hidden_size (:obj:`int`, optional, defaults to 256): hidden_size (:obj:`int`, optional, defaults to 256):
Dimensionality of the output hidden states of the residual attention blocks. Dimensionality of the output hidden states of the residual attention blocks.
initializer_range (:obj:`float`, optional, defaults to 0.02): initializer_range (:obj:`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
is_decoder (:obj:`bool`, optional, defaults to False): is_decoder (:obj:`bool`, optional, defaults to False):
If `is_decoder` is True, a causal mask is used in addition to `attention_mask`. If `is_decoder` is True, a causal mask is used in addition to `attention_mask`.
When using the Reformer for causal language modeling, `is_decoder` is set to `True`. When using the Reformer for causal language modeling, `is_decoder` is set to `True`.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers. The epsilon used by the layer normalization layers.
local_chunk_length (:obj:`int`, optional, defaults to 64): local_chunk_length (:obj:`int`, optional, defaults to 64):
Length of chunk which attends to itself in 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). Length of chunk which attends to itself in 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).
local_num_chunks_before (:obj:`int`, optional, defaults to 1): local_num_chunks_before (:obj:`int`, optional, defaults to 1):
Number of previous neighbouring chunks to attend to in LocalSelfAttention layer to itself. Number of previous neighbouring chunks to attend to in LocalSelfAttention layer to itself.
local_num_chunks_after (:obj:`int`, optional, defaults to 0): local_num_chunks_after (:obj:`int`, optional, defaults to 0):
Number of following neighbouring chunks to attend to in LocalSelfAttention layer in addition to itself. Number of following neighbouring chunks to attend to in LocalSelfAttention layer in addition to itself.
local_attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1): local_attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities in LocalSelfAttention. The dropout ratio for the attention probabilities in LocalSelfAttention.
lsh_attn_chunk_length (:obj:`int`, optional, defaults to 64): lsh_attn_chunk_length (:obj:`int`, optional, defaults to 64):
Length of chunk which attends to itself in 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). Length of chunk which attends to itself in 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).
lsh_num_chunks_before (:obj:`int`, optional, defaults to 1): lsh_num_chunks_before (:obj:`int`, optional, defaults to 1):
Number of previous neighbouring chunks to attend to in LSHSelfAttention layer to itself. Number of previous neighbouring chunks to attend to in LSHSelfAttention layer to itself.
lsh_num_chunks_after (:obj:`int`, optional, defaults to 0): lsh_num_chunks_after (:obj:`int`, optional, defaults to 0):
Number of following neighbouring chunks to attend to in LSHSelfAttention layer to itself. Number of following neighbouring chunks to attend to in LSHSelfAttention layer to itself.
lsh_attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1): lsh_attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities in LSHSelfAttention. The dropout ratio for the attention probabilities in LSHSelfAttention.
max_position_embeddings (:obj:`int`, optional, defaults to 4096): max_position_embeddings (:obj:`int`, optional, defaults to 4096):
The maximum sequence length that this model might ever be used with. 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). 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): num_attention_heads (:obj:`int`, optional, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder. Number of attention heads for each attention layer in the Transformer encoder.
num_buckets (:obj:`int` or :obj:`list(int)`, optional, defaults to `None`): num_buckets (:obj:`int` or :obj:`list(int)`, optional, defaults to `None`):
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 `1, ..., num_buckets`. 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 `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 `1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if `num_buckets` is factorized into two factors. 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 `1-1, 1-2, ..., num_buckets[0]-1, ..., num_buckets[0]-num_buckets[1]` if `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 `num_buckets` is set to `None`, a good value for `num_buckets` is calculated on the fly. The number of buckets (or the product the factors) should approximately equal sequence length / lsh_chunk_length. If `num_buckets` is set to `None`, a good value for `num_buckets` is calculated on the fly.
num_hashes (:obj:`int`, optional, defaults to 1): num_hashes (:obj:`int`, optional, defaults to 1):
Number of hashing rounds (e.g. number of random rotations) in Local Sensitive Hashing scheme. Number of hashing rounds (e.g. number of random rotations) in Local Sensitive Hashing scheme.
The higher `num_hashes`, the more accurate the `LSHSelfAttention` becomes, but also the more memory and time intensive the hashing becomes. The higher `num_hashes`, the more accurate the `LSHSelfAttention` becomes, but also the more memory and time intensive the hashing becomes.
pad_token_id (:obj:`int`, optional, defaults to 0): pad_token_id (:obj:`int`, optional, defaults to 0):
The token id for the <PAD> token. The token id for the <PAD> token.
vocab_size (:obj:`int`, optional, defaults to 320): vocab_size (:obj:`int`, optional, defaults to 320):
Vocabulary size of the Reformer model. Defines the different tokens that Vocabulary size of the Reformer model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ReformerModel`. can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ReformerModel`.
Example:: Example::
>>> from transformers import ReformerModel, ReformerConfig >>> from transformers import ReformerModel, ReformerConfig
>>> # Initializing a Reformer configuration >>> # Initializing a Reformer configuration
>>> configuration = ReformerConfig() >>> configuration = ReformerConfig()
>>> # Initializing a Reformer model >>> # Initializing a Reformer model
>>> model = ReformerModel(configuration) >>> model = ReformerModel(configuration)
>>> # Accessing the model configuration >>> # Accessing the model configuration
>>> configuration = model.config >>> configuration = model.config
""" """
model_type = "reformer" model_type = "reformer"
......
src/transformers/configuration_retribert.py
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...@@ -28,47 +28,47 @@ RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -28,47 +28,47 @@ RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class RetriBertConfig(PretrainedConfig): class RetriBertConfig(PretrainedConfig):
r""" r"""
This is the configuration class to store the configuration of a :class:`~transformers.RetriBertModel`. 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 It is used to instantiate a RetriBertModel model according to the specified arguments, defining the model
architecture. architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information. for more information.
Args: Args:
vocab_size (:obj:`int`, optional, defaults to 30522): vocab_size (:obj:`int`, optional, defaults to 30522):
Vocabulary size of the BERT model. Defines the different tokens that Vocabulary size of the BERT model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`. can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
hidden_size (:obj:`int`, optional, defaults to 768): hidden_size (:obj:`int`, optional, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, optional, defaults to 12): num_hidden_layers (:obj:`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder. Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, optional, defaults to 12): num_attention_heads (:obj:`int`, optional, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder. Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, optional, defaults to 3072): intermediate_size (:obj:`int`, optional, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"): hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the encoder and pooler. The non-linear activation function (function or string) in the encoder and pooler.
If string, "gelu", "relu", "swish" and "gelu_new" are supported. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1): hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. 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): attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities. The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, optional, defaults to 512): max_position_embeddings (:obj:`int`, optional, defaults to 512):
The maximum sequence length that this model might ever be used with. 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). 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): type_vocab_size (:obj:`int`, optional, defaults to 2):
The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`. The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
initializer_range (:obj:`float`, optional, defaults to 0.02): initializer_range (:obj:`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers. The epsilon used by the layer normalization layers.
share_encoders (:obj:`bool`, optional, defaults to True): share_encoders (:obj:`bool`, optional, defaults to True):
Whether to use the same Bert-type encoder for the queries and document Whether to use the same Bert-type encoder for the queries and document
projection_dim (:obj:`int`, optional, defaults to 128): projection_dim (:obj:`int`, optional, defaults to 128):
Final dimension of the query and document representation after projection Final dimension of the query and document representation after projection
""" """
model_type = "retribert" model_type = "retribert"
......
src/transformers/configuration_roberta.py
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...@@ -33,34 +33,33 @@ ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -33,34 +33,33 @@ ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class RobertaConfig(BertConfig): class RobertaConfig(BertConfig):
r""" r"""
This is the configuration class to store the configuration of a :class:`~transformers.RobertaModel`. This is the configuration class to store the configuration of a :class:`~transformers.RobertaModel`.
It is used to instantiate an RoBERTa model according to the specified arguments, defining the model It is used to instantiate an RoBERTa model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture. the BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information. for more information.
The :class:`~transformers.RobertaConfig` class directly inherits :class:`~transformers.BertConfig`. The :class:`~transformers.RobertaConfig` class directly inherits :class:`~transformers.BertConfig`.
It reuses the same defaults. Please check the parent class for more information. It reuses the same defaults. Please check the parent class for more information.
Example:: Example::
>>> from transformers import RobertaConfig, RobertaModel >>> from transformers import RobertaConfig, RobertaModel
>>> # Initializing a RoBERTa configuration >>> # Initializing a RoBERTa configuration
>>> configuration = RobertaConfig() >>> configuration = RobertaConfig()
>>> # Initializing a model from the configuration >>> # Initializing a model from the configuration
>>> model = RobertaModel(configuration) >>> model = RobertaModel(configuration)
>>> # Accessing the model configuration >>> # Accessing the model configuration
>>> configuration = model.config >>> configuration = model.config
""" """
model_type = "roberta" model_type = "roberta"
def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs): def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs):
"""Constructs RobertaConfig. """Constructs RobertaConfig."""
"""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
src/transformers/configuration_t5.py
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...@@ -31,33 +31,33 @@ T5_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -31,33 +31,33 @@ T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class T5Config(PretrainedConfig): class T5Config(PretrainedConfig):
r""" r"""
:class:`~transformers.T5Config` is the configuration class to store the configuration of a :class:`~transformers.T5Config` is the configuration class to store the configuration of a
`T5Model`. `T5Model`.
Arguments: Arguments:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`. vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`.
d_model: Size of the encoder layers and the pooler layer. `d_model` can also accesed via the property `hidden_size`. d_model: Size of the encoder layers and the pooler layer. `d_model` can also accesed via the property `hidden_size`.
num_layers: Number of hidden layers in the Transformer encoder. `num_layers` can also be accessed via the property `num_hidden_layers`. num_layers: Number of hidden layers in the Transformer encoder. `num_layers` can also be accessed via the property `num_hidden_layers`.
d_kv: Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`. d_kv: Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`.
d_ff: Size of the intermediate feed forward layer in each `T5Block`. d_ff: Size of the intermediate feed forward layer in each `T5Block`.
num_heads: Number of attention heads for each attention layer in num_heads: Number of attention heads for each attention layer in
the Transformer encoder. `num_heads` can also be accessed via the property `num_attention_heads`. the Transformer encoder. `num_heads` can also be accessed via the property `num_attention_heads`.
intermediate_size: The size of the "intermediate" (i.e., feed-forward) intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder. layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the hidden_act: The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported. encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
hidden_dropout_prob: The dropout probabilitiy for all fully connected hidden_dropout_prob: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler. layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention attention_probs_dropout_prob: The dropout ratio for the attention
probabilities. probabilities.
n_positions: The maximum sequence length that this model might n_positions: The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048). `n_positions` can also be accessed via the property `max_position_embeddings`. (e.g., 512 or 1024 or 2048). `n_positions` can also be accessed via the property `max_position_embeddings`.
type_vocab_size: The vocabulary size of the `token_type_ids` passed into type_vocab_size: The vocabulary size of the `token_type_ids` passed into
`T5Model`. `T5Model`.
initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing). initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing).
layer_norm_eps: The epsilon used by LayerNorm. layer_norm_eps: The epsilon used by LayerNorm.
""" """
model_type = "t5" model_type = "t5"
...@@ -80,7 +80,10 @@ class T5Config(PretrainedConfig): ...@@ -80,7 +80,10 @@ class T5Config(PretrainedConfig):
**kwargs **kwargs
): ):
super().__init__( super().__init__(
pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, **kwargs, pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
**kwargs,
) )
self.vocab_size = vocab_size self.vocab_size = vocab_size
self.n_positions = n_positions self.n_positions = n_positions
......
src/transformers/configuration_transfo_xl.py
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...@@ -31,84 +31,84 @@ TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -31,84 +31,84 @@ TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class TransfoXLConfig(PretrainedConfig): class TransfoXLConfig(PretrainedConfig):
""" """
This is the configuration class to store the configuration of a :class:`~transformers.TransfoXLModel`. This is the configuration class to store the configuration of a :class:`~transformers.TransfoXLModel`.
It is used to instantiate a Transformer XL model according to the specified arguments, defining the model 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 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. the `Transformer XL <https://huggingface.co/transfo-xl-wt103>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information. for more information.
Args: Args:
vocab_size (:obj:`int`, optional, defaults to 267735): vocab_size (:obj:`int`, optional, defaults to 267735):
Vocabulary size of the Transformer XL model. Defines the different tokens that Vocabulary size of the Transformer XL model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.TransfoXLModel`. can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.TransfoXLModel`.
cutoffs (:obj:`List[int]`, optional, defaults to :obj:`[20000, 40000, 200000]`): cutoffs (:obj:`List[int]`, optional, defaults to :obj:`[20000, 40000, 200000]`):
Cutoffs for the adaptive softmax Cutoffs for the adaptive softmax
d_model (:obj:`int`, optional, defaults to 1024): d_model (:obj:`int`, optional, defaults to 1024):
Dimensionality of the model's hidden states. Dimensionality of the model's hidden states.
d_embed (:obj:`int`, optional, defaults to 1024): d_embed (:obj:`int`, optional, defaults to 1024):
Dimensionality of the embeddings Dimensionality of the embeddings
n_head (:obj:`int`, optional, defaults to 16): n_head (:obj:`int`, optional, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder. Number of attention heads for each attention layer in the Transformer encoder.
d_head (:obj:`int`, optional, defaults to 64): d_head (:obj:`int`, optional, defaults to 64):
Dimensionality of the model's heads. Dimensionality of the model's heads.
d_inner (:obj:`int`, optional, defaults to 4096): d_inner (:obj:`int`, optional, defaults to 4096):
Inner dimension in FF Inner dimension in FF
div_val (:obj:`int`, optional, defaults to 4): div_val (:obj:`int`, optional, defaults to 4):
Divident value for adapative input and softmax Divident value for adapative input and softmax
pre_lnorm (:obj:`boolean`, optional, defaults to :obj:`False`): pre_lnorm (:obj:`boolean`, optional, defaults to :obj:`False`):
Apply LayerNorm to the input instead of the output Apply LayerNorm to the input instead of the output
n_layer (:obj:`int`, optional, defaults to 18): n_layer (:obj:`int`, optional, defaults to 18):
Number of hidden layers in the Transformer encoder. Number of hidden layers in the Transformer encoder.
tgt_len (:obj:`int`, optional, defaults to 128): tgt_len (:obj:`int`, optional, defaults to 128):
Number of tokens to predict Number of tokens to predict
ext_len (:obj:`int`, optional, defaults to 0): ext_len (:obj:`int`, optional, defaults to 0):
Length of the extended context Length of the extended context
mem_len (:obj:`int`, optional, defaults to 1600): mem_len (:obj:`int`, optional, defaults to 1600):
Length of the retained previous heads Length of the retained previous heads
clamp_len (:obj:`int`, optional, defaults to 1000): clamp_len (:obj:`int`, optional, defaults to 1000):
use the same pos embeddings after clamp_len use the same pos embeddings after clamp_len
same_length (:obj:`boolean`, optional, defaults to :obj:`True`): same_length (:obj:`boolean`, optional, defaults to :obj:`True`):
Use the same attn length for all tokens Use the same attn length for all tokens
proj_share_all_but_first (:obj:`boolean`, optional, defaults to :obj:`True`): proj_share_all_but_first (:obj:`boolean`, optional, defaults to :obj:`True`):
True to share all but first projs, False not to share. True to share all but first projs, False not to share.
attn_type (:obj:`int`, optional, defaults to 0): attn_type (:obj:`int`, optional, defaults to 0):
Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al. Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
sample_softmax (:obj:`int`, optional, defaults to -1): sample_softmax (:obj:`int`, optional, defaults to -1):
number of samples in sampled softmax number of samples in sampled softmax
adaptive (:obj:`boolean`, optional, defaults to :obj:`True`): adaptive (:obj:`boolean`, optional, defaults to :obj:`True`):
use adaptive softmax use adaptive softmax
dropout (:obj:`float`, optional, defaults to 0.1): dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
dropatt (:obj:`float`, optional, defaults to 0): dropatt (:obj:`float`, optional, defaults to 0):
The dropout ratio for the attention probabilities. The dropout ratio for the attention probabilities.
untie_r (:obj:`boolean`, optional, defaults to :obj:`True`): untie_r (:obj:`boolean`, optional, defaults to :obj:`True`):
Untie relative position biases Untie relative position biases
init (:obj:`string`, optional, defaults to `normal`): init (:obj:`string`, optional, defaults to `normal`):
Parameter initializer to use Parameter initializer to use
init_range (:obj:`float`, optional, defaults to 0.01): init_range (:obj:`float`, optional, defaults to 0.01):
Parameters initialized by U(-init_range, init_range). Parameters initialized by U(-init_range, init_range).
proj_init_std (:obj:`float`, optional, defaults to 0.01): proj_init_std (:obj:`float`, optional, defaults to 0.01):
Parameters initialized by N(0, init_std) Parameters initialized by N(0, init_std)
init_std (:obj:`float`, optional, defaults to 0.02): init_std (:obj:`float`, optional, defaults to 0.02):
Parameters initialized by N(0, init_std) Parameters initialized by N(0, init_std)
layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-5): layer_norm_epsilon (:obj:`float`, optional, defaults to 1e-5):
The epsilon to use in the layer normalization layers The epsilon to use in the layer normalization layers
Example:: Example::
>>> from transformers import TransfoXLConfig, TransfoXLModel >>> from transformers import TransfoXLConfig, TransfoXLModel
>>> # Initializing a Transformer XL configuration >>> # Initializing a Transformer XL configuration
>>> configuration = TransfoXLConfig() >>> configuration = TransfoXLConfig()
>>> # Initializing a model from the configuration >>> # Initializing a model from the configuration
>>> model = TransfoXLModel(configuration) >>> model = TransfoXLModel(configuration)
>>> # Accessing the model configuration >>> # Accessing the model configuration
>>> configuration = model.config >>> configuration = model.config
""" """
model_type = "transfo-xl" model_type = "transfo-xl"
......
src/transformers/configuration_utils.py
View file @ a75c64d8
...@@ -29,116 +29,116 @@ logger = logging.get_logger(__name__) ...@@ -29,116 +29,116 @@ logger = logging.get_logger(__name__)
class PretrainedConfig(object): class PretrainedConfig(object):
r""" Base class for all configuration classes. r"""Base class for all configuration classes.
Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving
configurations. configurations.
Note: Note:
A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to 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. initialize a model does **not** load the model weights.
It only affects the model's configuration. It only affects the model's configuration.
Class attributes (overridden by derived classes) Class attributes (overridden by derived classes)
- **model_type** (:obj:`str`): An identifier for the model type, serialized into the JSON file, and used to - **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`. recreate the correct object in :class:`~transformers.AutoConfig`.
Args: Args:
output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`): output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should return all hidden-states. Whether or not the model should return all hidden-states.
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`): output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should returns all attentions. Whether or not the model should returns all attentions.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): 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). 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`): 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 Whether or not the model should return a :class:`~transformers.file_utils.ModelOutput` instead of a
plain tuple. plain tuple.
is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`): is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether the model is used as an encoder/decoder or not. Whether the model is used as an encoder/decoder or not.
is_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`): 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). 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`): 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`) 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:`{}`): 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 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. 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 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. 2.
xla_device (:obj:`bool`, `optional`): xla_device (:obj:`bool`, `optional`):
A flag to indicate if TPU are available or not. A flag to indicate if TPU are available or not.
chunk_size_feed_forward (:obj:`int`, `optional`, defaults to :obj:`0`): chunk_size_feed_forward (:obj:`int`, `optional`, defaults to :obj:`0`):
The chunk size of all feed forward layers in the residual attention blocks. 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 :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. 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>`__ . For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
Parameters for sequence generation Parameters for sequence generation
- **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by - **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by
default in the :obj:`generate` method of the model. default in the :obj:`generate` method of the model.
- **min_length** (:obj:`int`, `optional`, defaults to 10) -- Minimum length that will be used by - **min_length** (:obj:`int`, `optional`, defaults to 10) -- Minimum length that will be used by
default in the :obj:`generate` method of the model. 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 - **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. 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 - **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 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`` sentences are finished per batch or not.
- **num_beams** (:obj:`int`, `optional`, defaults to 1) -- Number of beams for beam search that will be - **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. 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 - **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 probabilities that will be used by default in the :obj:`generate` method of the model. Must be strictly
positive. positive.
- **top_k** (:obj:`int`, `optional`, defaults to 50) -- Number of highest probability vocabulary tokens to - **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. 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 - **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 :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. 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 - **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. 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 - **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. 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 - **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 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. that size can only occur once.
- **bad_words_ids** (:obj:`List[int]`, `optional`) -- List of token ids that are not allowed to be - **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 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 tokens of the words that should not appear in the generated text, use
:obj:`tokenizer.encode(bad_word, add_prefix_space=True)`. :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`.
- **num_return_sequences** (:obj:`int`, `optional`, defaults to 1) -- Number of independently computed - **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` returned sequences for each element in the batch that will be used by default in the :obj:`generate`
method of the model. method of the model.
Parameters for fine-tuning tasks Parameters for fine-tuning tasks
- **architectures** (:obj:`List[str]`, `optional`) -- Model architectures that can be used with the - **architectures** (:obj:`List[str]`, `optional`) -- Model architectures that can be used with the
model pretrained weights. model pretrained weights.
- **finetuning_task** (:obj:`str`, `optional`) -- Name of the task used to fine-tune the model. This can be - **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. used when converting from an original (TensorFlow or PyTorch) checkpoint.
- **id2label** (:obj:`List[str]`, `optional`) -- A map from index (for instance prediction index, or target - **id2label** (:obj:`List[str]`, `optional`) -- A map from index (for instance prediction index, or target
index) to label. index) to label.
- **label2id** (:obj:`Dict[str, int]`, `optional`) -- A map from label to index for the model. - **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, - **num_labels** (:obj:`int`, `optional`) -- Number of labels to use in the last layer added to the model,
typically for a classification task. typically for a classification task.
- **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for - **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for
the current task. the current task.
Parameters linked to the tokenizer Parameters linked to the tokenizer
- **prefix** (:obj:`str`, `optional`) -- A specific prompt that should be added at the beginning of each - **prefix** (:obj:`str`, `optional`) -- A specific prompt that should be added at the beginning of each
text before calling the model. text before calling the model.
- **bos_token_id** (:obj:`int`, `optional`)) -- The id of the `beginning-of-stream` token. - **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. - **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. - **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 - **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. a different token than `bos`, the id of that token.
PyTorch specific parameters PyTorch specific parameters
- **torchscript** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should be - **torchscript** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should be
used with Torchscript. 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 TensorFlow specific parameters
- **use_bfloat16** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should - **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 scalars (only used by some TensorFlow models).
""" """
model_type: str = "" model_type: str = ""
......
src/transformers/configuration_xlm.py
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...@@ -36,120 +36,120 @@ XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -36,120 +36,120 @@ XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLMConfig(PretrainedConfig): class XLMConfig(PretrainedConfig):
""" """
This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`. This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`.
It is used to instantiate an XLM model according to the specified arguments, defining the model It is used to instantiate an 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 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. the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information. for more information.
Args: Args:
vocab_size (:obj:`int`, optional, defaults to 30145): vocab_size (:obj:`int`, optional, defaults to 30145):
Vocabulary size of the XLM model. Defines the different tokens that Vocabulary size of the XLM model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`. can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`.
emb_dim (:obj:`int`, optional, defaults to 2048): emb_dim (:obj:`int`, optional, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer. Dimensionality of the encoder layers and the pooler layer.
n_layer (:obj:`int`, optional, defaults to 12): n_layer (:obj:`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder. Number of hidden layers in the Transformer encoder.
n_head (:obj:`int`, optional, defaults to 16): n_head (:obj:`int`, optional, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder. Number of attention heads for each attention layer in the Transformer encoder.
dropout (:obj:`float`, optional, defaults to 0.1): dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probability for all fully connected The dropout probability for all fully connected
layers in the embeddings, encoder, and pooler. layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, optional, defaults to 0.1): attention_dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probability for the attention mechanism The dropout probability for the attention mechanism
gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`): gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
The non-linear activation function (function or string) in the The non-linear activation function (function or string) in the
encoder and pooler. If set to `True`, "gelu" will be used instead of "relu". encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`): sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use sinusoidal positional embeddings instead of absolute positional embeddings. Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
causal (:obj:`boolean`, optional, defaults to :obj:`False`): causal (:obj:`boolean`, optional, defaults to :obj:`False`):
Set this to `True` for the model to behave in a causal manner. Set this to `True` for the model to behave in a causal manner.
Causal models use a triangular attention mask in order to only attend to the left-side context instead Causal models use a triangular attention mask in order to only attend to the left-side context instead
if a bidirectional context. if a bidirectional context.
asm (:obj:`boolean`, optional, defaults to :obj:`False`): asm (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction
layer. layer.
n_langs (:obj:`int`, optional, defaults to 1): n_langs (:obj:`int`, optional, defaults to 1):
The number of languages the model handles. Set to 1 for monolingual models. The number of languages the model handles. Set to 1 for monolingual models.
use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`) use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
Whether to use language embeddings. Some models use additional language embeddings, see 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>`__ `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
for information on how to use them. for information on how to use them.
max_position_embeddings (:obj:`int`, optional, defaults to 512): max_position_embeddings (:obj:`int`, optional, defaults to 512):
The maximum sequence length that this model might The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048). (e.g., 512 or 1024 or 2048).
embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5): embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
The standard deviation of the truncated_normal_initializer for The standard deviation of the truncated_normal_initializer for
initializing the embedding matrices. initializing the embedding matrices.
init_std (:obj:`int`, optional, defaults to 50257): init_std (:obj:`int`, optional, defaults to 50257):
The standard deviation of the truncated_normal_initializer for The standard deviation of the truncated_normal_initializer for
initializing all weight matrices except the embedding matrices. initializing all weight matrices except the embedding matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers. The epsilon used by the layer normalization layers.
bos_index (:obj:`int`, optional, defaults to 0): bos_index (:obj:`int`, optional, defaults to 0):
The index of the beginning of sentence token in the vocabulary. The index of the beginning of sentence token in the vocabulary.
eos_index (:obj:`int`, optional, defaults to 1): eos_index (:obj:`int`, optional, defaults to 1):
The index of the end of sentence token in the vocabulary. The index of the end of sentence token in the vocabulary.
pad_index (:obj:`int`, optional, defaults to 2): pad_index (:obj:`int`, optional, defaults to 2):
The index of the padding token in the vocabulary. The index of the padding token in the vocabulary.
unk_index (:obj:`int`, optional, defaults to 3): unk_index (:obj:`int`, optional, defaults to 3):
The index of the unknown token in the vocabulary. The index of the unknown token in the vocabulary.
mask_index (:obj:`int`, optional, defaults to 5): mask_index (:obj:`int`, optional, defaults to 5):
The index of the masking token in the vocabulary. The index of the masking token in the vocabulary.
is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`): is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`):
Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
summary_type (:obj:`string`, optional, defaults to "first"): summary_type (:obj:`string`, optional, defaults to "first"):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`. :class:`~transformers.XLMForSequenceClassification`.
Is one of the following options: Is one of the following options:
- 'last' => take the last token hidden state (like XLNet) - 'last' => take the last token hidden state (like XLNet)
- 'first' => take the first token hidden state (like Bert) - 'first' => take the first token hidden state (like Bert)
- 'mean' => take the mean of all tokens hidden states - 'mean' => take the mean of all tokens hidden states
- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2) - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention - 'attn' => Not implemented now, use multi-head attention
summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`): summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`. :class:`~transformers.XLMForSequenceClassification`.
Add a projection after the vector extraction Add a projection after the vector extraction
summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`): summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`. :class:`~transformers.XLMForSequenceClassification`.
'tanh' => add a tanh activation to the output, Other => no activation. 'tanh' => add a tanh activation to the output, Other => no activation.
summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`): summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`. :class:`~transformers.XLMForSequenceClassification`.
If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
summary_first_dropout (:obj:`float`, optional, defaults to 0.1): summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`. :class:`~transformers.XLMForSequenceClassification`.
Add a dropout before the projection and activation Add a dropout before the projection and activation
start_n_top (:obj:`int`, optional, defaults to 5): start_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet. Used in the SQuAD evaluation script for XLM and XLNet.
end_n_top (:obj:`int`, optional, defaults to 5): end_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet. Used in the SQuAD evaluation script for XLM and XLNet.
mask_token_id (:obj:`int`, optional, defaults to 0): mask_token_id (:obj:`int`, optional, defaults to 0):
Model agnostic parameter to identify masked tokens when generating text in an MLM context. Model agnostic parameter to identify masked tokens when generating text in an MLM context.
lang_id (:obj:`int`, optional, defaults to 1): lang_id (:obj:`int`, optional, defaults to 1):
The ID of the language used by the model. This parameter is used when generating The ID of the language used by the model. This parameter is used when generating
text in a given language. text in a given language.
Example:: Example::
>>> from transformers import XLMConfig, XLMModel >>> from transformers import XLMConfig, XLMModel
>>> # Initializing a XLM configuration >>> # Initializing a XLM configuration
>>> configuration = XLMConfig() >>> configuration = XLMConfig()
>>> # Initializing a model from the configuration >>> # Initializing a model from the configuration
>>> model = XLMModel(configuration) >>> model = XLMModel(configuration)
>>> # Accessing the model configuration >>> # Accessing the model configuration
>>> configuration = model.config >>> configuration = model.config
""" """
model_type = "xlm" model_type = "xlm"
...@@ -191,8 +191,7 @@ class XLMConfig(PretrainedConfig): ...@@ -191,8 +191,7 @@ class XLMConfig(PretrainedConfig):
bos_token_id=0, bos_token_id=0,
**kwargs **kwargs
): ):
"""Constructs XLMConfig. """Constructs XLMConfig."""
"""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs) super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
self.vocab_size = vocab_size self.vocab_size = vocab_size
self.emb_dim = emb_dim self.emb_dim = emb_dim
......
src/transformers/configuration_xlnet.py
View file @ a75c64d8
...@@ -31,104 +31,104 @@ XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { ...@@ -31,104 +31,104 @@ XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
class XLNetConfig(PretrainedConfig): class XLNetConfig(PretrainedConfig):
""" """
This is the configuration class to store the configuration of a :class:`~transformers.XLNetModel`. This is the configuration class to store the configuration of a :class:`~transformers.XLNetModel`.
It is used to instantiate an XLNet model according to the specified arguments, defining the model It is used to instantiate an 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 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. the `xlnet-large-cased <https://huggingface.co/xlnet-large-cased>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information. for more information.
Args: Args:
vocab_size (:obj:`int`, optional, defaults to 32000): vocab_size (:obj:`int`, optional, defaults to 32000):
Vocabulary size of the XLNet model. Defines the different tokens that Vocabulary size of the XLNet model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLNetModel`. can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLNetModel`.
d_model (:obj:`int`, optional, defaults to 1024): d_model (:obj:`int`, optional, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer. Dimensionality of the encoder layers and the pooler layer.
n_layer (:obj:`int`, optional, defaults to 24): n_layer (:obj:`int`, optional, defaults to 24):
Number of hidden layers in the Transformer encoder. Number of hidden layers in the Transformer encoder.
n_head (:obj:`int`, optional, defaults to 16): n_head (:obj:`int`, optional, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder. Number of attention heads for each attention layer in the Transformer encoder.
d_inner (:obj:`int`, optional, defaults to 4096): d_inner (:obj:`int`, optional, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
ff_activation (:obj:`string`, optional, defaults to "gelu"): ff_activation (:obj:`string`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported. encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
untie_r (:obj:`boolean`, optional, defaults to :obj:`True`): untie_r (:obj:`boolean`, optional, defaults to :obj:`True`):
Untie relative position biases Untie relative position biases
attn_type (:obj:`string`, optional, defaults to "bi"): attn_type (:obj:`string`, optional, defaults to "bi"):
The attention type used by the model. Set 'bi' for XLNet, 'uni' for Transformer-XL. The attention type used by the model. Set 'bi' for XLNet, 'uni' for Transformer-XL.
initializer_range (:obj:`float`, optional, defaults to 0.02): initializer_range (:obj:`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers. The epsilon used by the layer normalization layers.
dropout (:obj:`float`, optional, defaults to 0.1): 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.
mem_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`): mem_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`):
The number of tokens to cache. The key/value pairs that have already been pre-computed 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 in a previous forward pass won't be re-computed. See the
`quickstart <https://huggingface.co/transformers/quickstart.html#using-the-past>`__ `quickstart <https://huggingface.co/transformers/quickstart.html#using-the-past>`__
for more information. for more information.
reuse_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`): reuse_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`):
The number of tokens in the current batch to be cached and reused in the future. The number of tokens in the current batch to be cached and reused in the future.
bi_data (:obj:`boolean`, optional, defaults to :obj:`False`): bi_data (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use bidirectional input pipeline. Usually set to `True` during Whether to use bidirectional input pipeline. Usually set to `True` during
pretraining and `False` during finetuning. pretraining and `False` during finetuning.
clamp_len (:obj:`int`, optional, defaults to -1): clamp_len (:obj:`int`, optional, defaults to -1):
Clamp all relative distances larger than clamp_len. Clamp all relative distances larger than clamp_len.
Setting this attribute to -1 means no clamping. Setting this attribute to -1 means no clamping.
same_length (:obj:`boolean`, optional, defaults to :obj:`False`): same_length (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use the same attention length for each token. Whether to use the same attention length for each token.
summary_type (:obj:`string`, optional, defaults to "last"): summary_type (:obj:`string`, optional, defaults to "last"):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`. :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
Is one of the following options: Is one of the following options:
- 'last' => take the last token hidden state (like XLNet) - 'last' => take the last token hidden state (like XLNet)
- 'first' => take the first token hidden state (like Bert) - 'first' => take the first token hidden state (like Bert)
- 'mean' => take the mean of all tokens hidden states - 'mean' => take the mean of all tokens hidden states
- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2) - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention - 'attn' => Not implemented now, use multi-head attention
summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`): summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`. :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
Add a projection after the vector extraction Add a projection after the vector extraction
summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`): summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`. :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
'tanh' => add a tanh activation to the output, Other => no activation. 'tanh' => add a tanh activation to the output, Other => no activation.
summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`): summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`. :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False. If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
summary_last_dropout (:obj:`float`, optional, defaults to 0.1): summary_last_dropout (:obj:`float`, optional, defaults to 0.1):
Argument used when doing sequence summary. Used in for the multiple choice head in Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`. :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
Add a dropout after the projection and activation Add a dropout after the projection and activation
start_n_top (:obj:`int`, optional, defaults to 5): start_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet. Used in the SQuAD evaluation script for XLM and XLNet.
end_n_top (:obj:`int`, optional, defaults to 5): end_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet. Used in the SQuAD evaluation script for XLM and XLNet.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last pre-computed hidden states. Whether or not the model should return the last pre-computed hidden states.
.. note:: .. note::
This flag behaves differently from with other models: it just controls the inference behavior, during This flag behaves differently from with other models: it just controls the inference behavior, during
training the model always uses ``use_cache=True``. training the model always uses ``use_cache=True``.
Example:: Example::
>>> from transformers import XLNetConfig, XLNetModel >>> from transformers import XLNetConfig, XLNetModel
>>> # Initializing a XLNet configuration >>> # Initializing a XLNet configuration
>>> configuration = XLNetConfig() >>> configuration = XLNetConfig()
>>> # Initializing a model from the configuration >>> # Initializing a model from the configuration
>>> model = XLNetModel(configuration) >>> model = XLNetModel(configuration)
>>> # Accessing the model configuration >>> # Accessing the model configuration
>>> configuration = model.config >>> configuration = model.config
""" """
model_type = "xlnet" model_type = "xlnet"
...@@ -162,8 +162,7 @@ class XLNetConfig(PretrainedConfig): ...@@ -162,8 +162,7 @@ class XLNetConfig(PretrainedConfig):
eos_token_id=2, eos_token_id=2,
**kwargs **kwargs
): ):
"""Constructs XLNetConfig. """Constructs XLNetConfig."""
"""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size self.vocab_size = vocab_size
self.d_model = d_model self.d_model = d_model
......
src/transformers/convert_dialogpt_original_pytorch_checkpoint_to_pytorch.py
View file @ a75c64d8
...@@ -27,5 +27,6 @@ if __name__ == "__main__": ...@@ -27,5 +27,6 @@ if __name__ == "__main__":
checkpoint_path = os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl") checkpoint_path = os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
pytorch_dump_folder_path = f"./DialoGPT-{MODEL}" pytorch_dump_folder_path = f"./DialoGPT-{MODEL}"
convert_dialogpt_checkpoint( convert_dialogpt_checkpoint(
checkpoint_path, pytorch_dump_folder_path, checkpoint_path,
pytorch_dump_folder_path,
) )
src/transformers/convert_graph_to_onnx.py
View file @ a75c64d8
...@@ -38,24 +38,39 @@ class OnnxConverterArgumentParser(ArgumentParser): ...@@ -38,24 +38,39 @@ class OnnxConverterArgumentParser(ArgumentParser):
super().__init__("ONNX Converter") super().__init__("ONNX Converter")
self.add_argument( self.add_argument(
"--pipeline", type=str, choices=SUPPORTED_PIPELINES, default="feature-extraction", "--pipeline",
type=str,
choices=SUPPORTED_PIPELINES,
default="feature-extraction",
) )
self.add_argument( self.add_argument(
"--model", type=str, required=True, help="Model's id or path (ex: bert-base-cased)", "--model",
type=str,
required=True,
help="Model's id or path (ex: bert-base-cased)",
) )
self.add_argument("--tokenizer", type=str, help="Tokenizer's id or path (ex: bert-base-cased)") self.add_argument("--tokenizer", type=str, help="Tokenizer's id or path (ex: bert-base-cased)")
self.add_argument( self.add_argument(
"--framework", type=str, choices=["pt", "tf"], help="Framework for loading the model", "--framework",
type=str,
choices=["pt", "tf"],
help="Framework for loading the model",
) )
self.add_argument("--opset", type=int, default=11, help="ONNX opset to use") self.add_argument("--opset", type=int, default=11, help="ONNX opset to use")
self.add_argument( self.add_argument(
"--check-loading", action="store_true", help="Check ONNX is able to load the model", "--check-loading",
action="store_true",
help="Check ONNX is able to load the model",
) )
self.add_argument( self.add_argument(
"--use-external-format", action="store_true", help="Allow exporting model >= than 2Gb", "--use-external-format",
action="store_true",
help="Allow exporting model >= than 2Gb",
) )
self.add_argument( self.add_argument(
"--quantize", action="store_true", help="Quantize the neural network to be run with int8", "--quantize",
action="store_true",
help="Quantize the neural network to be run with int8",
) )
self.add_argument("output") self.add_argument("output")
...@@ -376,7 +391,10 @@ def quantize(onnx_model_path: Path) -> Path: ...@@ -376,7 +391,10 @@ def quantize(onnx_model_path: Path) -> Path:
) )
quantized_model = quantize( quantized_model = quantize(
model=onnx_model, quantization_mode=QuantizationMode.IntegerOps, force_fusions=True, symmetric_weight=True, model=onnx_model,
quantization_mode=QuantizationMode.IntegerOps,
force_fusions=True,
symmetric_weight=True,
) )
# Append "-quantized" at the end of the model's name # Append "-quantized" at the end of the model's name
......
src/transformers/convert_marian_to_pytorch.py
View file @ a75c64d8
...@@ -255,7 +255,11 @@ license: apache-2.0 ...@@ -255,7 +255,11 @@ license: apache-2.0
def write_model_card( def write_model_card(
hf_model_name: str, repo_root="OPUS-MT-train", save_dir=Path("marian_converted"), dry_run=False, extra_metadata={}, hf_model_name: str,
repo_root="OPUS-MT-train",
save_dir=Path("marian_converted"),
dry_run=False,
extra_metadata={},
) -> str: ) -> str:
"""Copy the most recent model's readme section from opus, and add metadata. """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 upload command: aws s3 sync model_card_dir s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
...@@ -604,7 +608,9 @@ class OpusState: ...@@ -604,7 +608,9 @@ class OpusState:
assert "hidden_size" not in cfg.to_dict() assert "hidden_size" not in cfg.to_dict()
load_layers_( load_layers_(
model.model.encoder.layers, state_dict, BART_CONVERTER, model.model.encoder.layers,
state_dict,
BART_CONVERTER,
) )
load_layers_(model.model.decoder.layers, state_dict, BART_CONVERTER, is_decoder=True) load_layers_(model.model.decoder.layers, state_dict, BART_CONVERTER, is_decoder=True)
......
src/transformers/convert_pytorch_checkpoint_to_tf2.py
View file @ a75c64d8
...@@ -108,7 +108,12 @@ if is_torch_available(): ...@@ -108,7 +108,12 @@ if is_torch_available():
logging.set_verbosity_info() logging.set_verbosity_info()
MODEL_CLASSES = { MODEL_CLASSES = {
"bert": (BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,), "bert": (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"bert-large-uncased-whole-word-masking-finetuned-squad": ( "bert-large-uncased-whole-word-masking-finetuned-squad": (
BertConfig, BertConfig,
TFBertForQuestionAnswering, TFBertForQuestionAnswering,
...@@ -127,9 +132,24 @@ MODEL_CLASSES = { ...@@ -127,9 +132,24 @@ MODEL_CLASSES = {
BertForSequenceClassification, BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
), ),
"gpt2": (GPT2Config, TFGPT2LMHeadModel, GPT2LMHeadModel, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,), "gpt2": (
"xlnet": (XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,), GPT2Config,
"xlm": (XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,), TFGPT2LMHeadModel,
GPT2LMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"xlnet": (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"xlm": (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"xlm-roberta": ( "xlm-roberta": (
XLMRobertaConfig, XLMRobertaConfig,
TFXLMRobertaForMaskedLM, TFXLMRobertaForMaskedLM,
...@@ -148,7 +168,12 @@ MODEL_CLASSES = { ...@@ -148,7 +168,12 @@ MODEL_CLASSES = {
OpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
), ),
"roberta": (RobertaConfig, TFRobertaForMaskedLM, RobertaForMaskedLM, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,), "roberta": (
RobertaConfig,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"roberta-large-mnli": ( "roberta-large-mnli": (
RobertaConfig, RobertaConfig,
TFRobertaForSequenceClassification, TFRobertaForSequenceClassification,
...@@ -179,10 +204,30 @@ MODEL_CLASSES = { ...@@ -179,10 +204,30 @@ MODEL_CLASSES = {
DistilBertForQuestionAnswering, DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
), ),
"ctrl": (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,), "ctrl": (
"albert": (AlbertConfig, TFAlbertForPreTraining, AlbertForPreTraining, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,), CTRLConfig,
"t5": (T5Config, TFT5ForConditionalGeneration, T5ForConditionalGeneration, T5_PRETRAINED_CONFIG_ARCHIVE_MAP,), TFCTRLLMHeadModel,
"electra": (ElectraConfig, TFElectraForPreTraining, ElectraForPreTraining, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,), CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"albert": (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"t5": (
T5Config,
TFT5ForConditionalGeneration,
T5ForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"electra": (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
} }
......
src/transformers/convert_reformer_trax_checkpoint_to_pytorch.py
View file @ a75c64d8
...@@ -49,10 +49,12 @@ def set_layer_weights_in_torch_lsh(weights, torch_layer, hidden_size): ...@@ -49,10 +49,12 @@ def set_layer_weights_in_torch_lsh(weights, torch_layer, hidden_size):
torch.tensor(np_query_key).transpose(1, 2).contiguous().view(-1, hidden_size), torch.tensor(np_query_key).transpose(1, 2).contiguous().view(-1, hidden_size),
) )
set_param( set_param(
torch_layer.self_attention.value, torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size), torch_layer.self_attention.value,
torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size),
) )
set_param( set_param(
torch_layer.output.dense, torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1), torch_layer.output.dense,
torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1),
) )
...@@ -64,16 +66,20 @@ def set_layer_weights_in_torch_local(weights, torch_layer, hidden_size): ...@@ -64,16 +66,20 @@ def set_layer_weights_in_torch_local(weights, torch_layer, hidden_size):
np_dense = np.asarray(weights[3]) np_dense = np.asarray(weights[3])
set_param( set_param(
torch_layer.self_attention.query, torch.tensor(np_query).transpose(1, 2).contiguous().view(-1, hidden_size), torch_layer.self_attention.query,
torch.tensor(np_query).transpose(1, 2).contiguous().view(-1, hidden_size),
) )
set_param( set_param(
torch_layer.self_attention.key, torch.tensor(np_key).transpose(1, 2).contiguous().view(-1, hidden_size), torch_layer.self_attention.key,
torch.tensor(np_key).transpose(1, 2).contiguous().view(-1, hidden_size),
) )
set_param( set_param(
torch_layer.self_attention.value, torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size), torch_layer.self_attention.value,
torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, hidden_size),
) )
set_param( set_param(
torch_layer.output.dense, torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1), torch_layer.output.dense,
torch.tensor(np_dense).view(-1, hidden_size).contiguous().transpose(0, 1),
) )
...@@ -83,7 +89,9 @@ def set_block_weights_in_torch(weights, torch_block, hidden_size): ...@@ -83,7 +89,9 @@ def set_block_weights_in_torch(weights, torch_block, hidden_size):
layer_norm_1_weight = np.asarray(layer_norm_1[0]) layer_norm_1_weight = np.asarray(layer_norm_1[0])
layer_norm_1_bias = np.asarray(layer_norm_1[1]) layer_norm_1_bias = np.asarray(layer_norm_1[1])
set_param( set_param(
torch_block.attention.layer_norm, torch.tensor(layer_norm_1_weight), torch.tensor(layer_norm_1_bias), torch_block.attention.layer_norm,
torch.tensor(layer_norm_1_weight),
torch.tensor(layer_norm_1_bias),
) )
# lsh weights + output # lsh weights + output
...@@ -104,7 +112,9 @@ def set_block_weights_in_torch(weights, torch_block, hidden_size): ...@@ -104,7 +112,9 @@ def set_block_weights_in_torch(weights, torch_block, hidden_size):
layer_norm_2_weight = np.asarray(intermediate_weights[0][0]) layer_norm_2_weight = np.asarray(intermediate_weights[0][0])
layer_norm_2_bias = np.asarray(intermediate_weights[0][1]) layer_norm_2_bias = np.asarray(intermediate_weights[0][1])
set_param( set_param(
torch_block.feed_forward.layer_norm, torch.tensor(layer_norm_2_weight), torch.tensor(layer_norm_2_bias), torch_block.feed_forward.layer_norm,
torch.tensor(layer_norm_2_weight),
torch.tensor(layer_norm_2_bias),
) )
# intermediate dense # intermediate dense
...@@ -133,7 +143,8 @@ def set_model_weights_in_torch(weights, torch_model, hidden_size): ...@@ -133,7 +143,8 @@ def set_model_weights_in_torch(weights, torch_model, hidden_size):
# word embeds # word embeds
word_embeddings = np.asarray(weights[1]) word_embeddings = np.asarray(weights[1])
set_param( set_param(
torch_model_reformer.embeddings.word_embeddings, torch.tensor(word_embeddings), torch_model_reformer.embeddings.word_embeddings,
torch.tensor(word_embeddings),
) )
if isinstance(weights[3], tuple): if isinstance(weights[3], tuple):
......
src/transformers/data/datasets/glue.py
View file @ a75c64d8
...@@ -86,7 +86,10 @@ class GlueDataset(Dataset): ...@@ -86,7 +86,10 @@ class GlueDataset(Dataset):
cached_features_file = os.path.join( cached_features_file = os.path.join(
cache_dir if cache_dir is not None else args.data_dir, cache_dir if cache_dir is not None else args.data_dir,
"cached_{}_{}_{}_{}".format( "cached_{}_{}_{}_{}".format(
mode.value, tokenizer.__class__.__name__, str(args.max_seq_length), args.task_name, mode.value,
tokenizer.__class__.__name__,
str(args.max_seq_length),
args.task_name,
), ),
) )
label_list = self.processor.get_labels() label_list = self.processor.get_labels()
......
src/transformers/data/datasets/language_modeling.py
View file @ a75c64d8
...@@ -21,7 +21,11 @@ class TextDataset(Dataset): ...@@ -21,7 +21,11 @@ class TextDataset(Dataset):
""" """
def __init__( def __init__(
self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
): ):
assert os.path.isfile(file_path), f"Input file path {file_path} not found" assert os.path.isfile(file_path), f"Input file path {file_path} not found"
...@@ -29,7 +33,12 @@ class TextDataset(Dataset): ...@@ -29,7 +33,12 @@ class TextDataset(Dataset):
directory, filename = os.path.split(file_path) directory, filename = os.path.split(file_path)
cached_features_file = os.path.join( cached_features_file = os.path.join(
directory, "cached_lm_{}_{}_{}".format(tokenizer.__class__.__name__, str(block_size), filename,), directory,
"cached_lm_{}_{}_{}".format(
tokenizer.__class__.__name__,
str(block_size),
filename,
),
) )
# Make sure only the first process in distributed training processes the dataset, # Make sure only the first process in distributed training processes the dataset,
......
src/transformers/data/datasets/squad.py
View file @ a75c64d8
...@@ -119,7 +119,10 @@ class SquadDataset(Dataset): ...@@ -119,7 +119,10 @@ class SquadDataset(Dataset):
cached_features_file = os.path.join( cached_features_file = os.path.join(
cache_dir if cache_dir is not None else args.data_dir, cache_dir if cache_dir is not None else args.data_dir,
"cached_{}_{}_{}_{}".format( "cached_{}_{}_{}_{}".format(
mode.value, tokenizer.__class__.__name__, str(args.max_seq_length), version_tag, mode.value,
tokenizer.__class__.__name__,
str(args.max_seq_length),
version_tag,
), ),
) )
......
src/transformers/data/metrics/squad_metrics.py
View file @ a75c64d8
...@@ -589,10 +589,10 @@ def compute_predictions_log_probs( ...@@ -589,10 +589,10 @@ def compute_predictions_log_probs(
tokenizer, tokenizer,
verbose_logging, verbose_logging,
): ):
""" XLNet write prediction logic (more complex than Bert's). """XLNet write prediction logic (more complex than Bert's).
Write final predictions to the json file and log-odds of null if needed. Write final predictions to the json file and log-odds of null if needed.
Requires utils_squad_evaluate.py Requires utils_squad_evaluate.py
""" """
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"] "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]
......
src/transformers/data/processors/glue.py
View file @ a75c64d8
...@@ -69,7 +69,10 @@ def glue_convert_examples_to_features( ...@@ -69,7 +69,10 @@ def glue_convert_examples_to_features(
if is_tf_available(): if is_tf_available():
def _tf_glue_convert_examples_to_features( def _tf_glue_convert_examples_to_features(
examples: tf.data.Dataset, tokenizer: PreTrainedTokenizer, task=str, max_length: Optional[int] = None, examples: tf.data.Dataset,
tokenizer: PreTrainedTokenizer,
task=str,
max_length: Optional[int] = None,
) -> tf.data.Dataset: ) -> tf.data.Dataset:
""" """
Returns: Returns:
......
src/transformers/data/processors/utils.py
View file @ a75c64d8
...@@ -269,7 +269,9 @@ class SingleSentenceClassificationProcessor(DataProcessor): ...@@ -269,7 +269,9 @@ class SingleSentenceClassificationProcessor(DataProcessor):
logger.info("Tokenizing example %d", ex_index) logger.info("Tokenizing example %d", ex_index)
input_ids = tokenizer.encode( input_ids = tokenizer.encode(
example.text_a, add_special_tokens=True, max_length=min(max_length, tokenizer.max_len), example.text_a,
add_special_tokens=True,
max_length=min(max_length, tokenizer.max_len),
) )
all_input_ids.append(input_ids) all_input_ids.append(input_ids)
......
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