255 lines
13 KiB
Python
255 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" XLM configuration """
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import logging
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"xlm-mlm-en-2048": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
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"xlm-mlm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
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"xlm-mlm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
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"xlm-mlm-enro-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
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"xlm-mlm-tlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
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"xlm-mlm-xnli15-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
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"xlm-clm-enfr-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
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"xlm-clm-ende-1024": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
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"xlm-mlm-17-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
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"xlm-mlm-100-1280": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
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}
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class XLMConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`.
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It is used to instantiate an XLM model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
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to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
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for more information.
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Args:
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vocab_size (:obj:`int`, optional, defaults to 30145):
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Vocabulary size of the XLM model. Defines the different tokens that
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can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`.
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emb_dim (:obj:`int`, optional, defaults to 2048):
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Dimensionality of the encoder layers and the pooler layer.
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n_layer (:obj:`int`, optional, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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n_head (:obj:`int`, optional, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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dropout (:obj:`float`, optional, defaults to 0.1):
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The dropout probability for all fully connected
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layers in the embeddings, encoder, and pooler.
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attention_dropout (:obj:`float`, optional, defaults to 0.1):
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The dropout probability for the attention mechanism
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gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
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The non-linear activation function (function or string) in the
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encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
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sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
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Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
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causal (:obj:`boolean`, optional, defaults to :obj:`False`):
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Set this to `True` for the model to behave in a causal manner.
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Causal models use a triangular attention mask in order to only attend to the left-side context instead
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if a bidirectional context.
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asm (:obj:`boolean`, optional, defaults to :obj:`False`):
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Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction
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layer.
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n_langs (:obj:`int`, optional, defaults to 1):
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The number of languages the model handles. Set to 1 for monolingual models.
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use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
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Whether to use language embeddings. Some models use additional language embeddings, see
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`the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
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for information on how to use them.
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max_position_embeddings (:obj:`int`, optional, defaults to 512):
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The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
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The standard deviation of the truncated_normal_initializer for
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initializing the embedding matrices.
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init_std (:obj:`int`, optional, defaults to 50257):
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The standard deviation of the truncated_normal_initializer for
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initializing all weight matrices except the embedding matrices.
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layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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bos_index (:obj:`int`, optional, defaults to 0):
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The index of the beginning of sentence token in the vocabulary.
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eos_index (:obj:`int`, optional, defaults to 1):
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The index of the end of sentence token in the vocabulary.
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pad_index (:obj:`int`, optional, defaults to 2):
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The index of the padding token in the vocabulary.
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unk_index (:obj:`int`, optional, defaults to 3):
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The index of the unknown token in the vocabulary.
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mask_index (:obj:`int`, optional, defaults to 5):
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The index of the masking token in the vocabulary.
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is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`):
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Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
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summary_type (:obj:`string`, optional, defaults to "first"):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.XLMForSequenceClassification`.
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Is one of the following options:
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- 'last' => take the last token hidden state (like XLNet)
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- 'first' => take the first token hidden state (like Bert)
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- 'mean' => take the mean of all tokens hidden states
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- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
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- 'attn' => Not implemented now, use multi-head attention
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summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.XLMForSequenceClassification`.
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Add a projection after the vector extraction
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summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.XLMForSequenceClassification`.
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'tanh' => add a tanh activation to the output, Other => no activation.
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summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.XLMForSequenceClassification`.
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If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
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summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
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Argument used when doing sequence summary. Used in for the multiple choice head in
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:class:`~transformers.XLMForSequenceClassification`.
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Add a dropout before the projection and activation
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start_n_top (:obj:`int`, optional, defaults to 5):
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Used in the SQuAD evaluation script for XLM and XLNet.
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end_n_top (:obj:`int`, optional, defaults to 5):
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Used in the SQuAD evaluation script for XLM and XLNet.
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mask_token_id (:obj:`int`, optional, defaults to 0):
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Model agnostic parameter to identify masked tokens when generating text in an MLM context.
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lang_id (:obj:`int`, optional, defaults to 1):
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The ID of the language used by the model. This parameter is used when generating
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text in a given language.
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Example::
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from transformers import XLMConfig, XLMModel
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# Initializing a XLM configuration
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configuration = XLMConfig()
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# Initializing a model from the configuration
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model = XLMModel(configuration)
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# Accessing the model configuration
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configuration = model.config
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Attributes:
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pretrained_config_archive_map (Dict[str, str]):
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A dictionary containing all the available pre-trained checkpoints.
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"""
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pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
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model_type = "xlm"
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def __init__(
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self,
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vocab_size=30145,
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emb_dim=2048,
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n_layers=12,
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n_heads=16,
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dropout=0.1,
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attention_dropout=0.1,
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gelu_activation=True,
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sinusoidal_embeddings=False,
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causal=False,
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asm=False,
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n_langs=1,
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use_lang_emb=True,
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max_position_embeddings=512,
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embed_init_std=2048 ** -0.5,
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layer_norm_eps=1e-12,
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init_std=0.02,
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bos_index=0,
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eos_index=1,
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pad_index=2,
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unk_index=3,
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mask_index=5,
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is_encoder=True,
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summary_type="first",
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summary_use_proj=True,
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summary_activation=None,
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summary_proj_to_labels=True,
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summary_first_dropout=0.1,
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start_n_top=5,
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end_n_top=5,
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mask_token_id=0,
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lang_id=0,
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**kwargs
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):
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"""Constructs XLMConfig.
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"""
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.emb_dim = emb_dim
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.gelu_activation = gelu_activation
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self.sinusoidal_embeddings = sinusoidal_embeddings
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self.causal = causal
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self.asm = asm
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self.n_langs = n_langs
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self.use_lang_emb = use_lang_emb
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self.layer_norm_eps = layer_norm_eps
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self.bos_index = bos_index
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self.eos_index = eos_index
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self.pad_index = pad_index
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self.unk_index = unk_index
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self.mask_index = mask_index
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self.is_encoder = is_encoder
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self.max_position_embeddings = max_position_embeddings
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self.embed_init_std = embed_init_std
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self.init_std = init_std
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_proj_to_labels = summary_proj_to_labels
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self.summary_first_dropout = summary_first_dropout
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self.start_n_top = start_n_top
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self.end_n_top = end_n_top
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self.mask_token_id = mask_token_id
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self.lang_id = lang_id
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if "n_words" in kwargs:
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self.n_words = kwargs["n_words"]
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@property
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def n_words(self): # For backward compatibility
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return self.vocab_size
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@n_words.setter
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def n_words(self, value): # For backward compatibility
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self.vocab_size = value
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@property
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def hidden_size(self):
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return self.emb_dim
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@property
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def num_attention_heads(self):
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return self.n_heads
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@property
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def num_hidden_layers(self):
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return self.n_layers
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