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""" Flaubert configuration, based on XLM. """
import logging
from .configuration_xlm import XLMConfig
logger = logging.getLogger(__name__)
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/config.json",
"flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/config.json",
"flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/config.json",
"flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/config.json",
}
[docs]class FlaubertConfig(XLMConfig):
"""
Configuration class to store the configuration of a `FlaubertModel`.
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
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Args:
pre_norm (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to apply the layer normalization before or after the feed forward layer following the
attention in each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018)
layerdrop (:obj:`float`, `optional`, defaults to 0.0):
Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand
with Structured Dropout. ICLR 2020)
vocab_size (:obj:`int`, optional, defaults to 30145):
Vocabulary size of the Flaubert model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.FlaubertModel`.
emb_dim (:obj:`int`, optional, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
n_layer (:obj:`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (:obj:`int`, optional, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probability for all fully connected
layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, optional, defaults to 0.1):
The dropout probability for the attention mechanism
gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
The non-linear activation function (function or string) in the
encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
causal (:obj:`boolean`, optional, defaults to :obj:`False`):
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
if a bidirectional context.
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
layer.
n_langs (:obj:`int`, optional, defaults to 1):
The number of languages the model handles. Set to 1 for monolingual models.
use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
Whether to use language embeddings. Some models use additional language embeddings, see
`the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
for information on how to use them.
max_position_embeddings (:obj:`int`, optional, defaults to 512):
The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
The standard deviation of the truncated_normal_initializer for
initializing the embedding matrices.
init_std (:obj:`int`, optional, defaults to 50257):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices except the embedding matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers.
bos_index (:obj:`int`, optional, defaults to 0):
The index of the beginning of sentence token in the vocabulary.
eos_index (:obj:`int`, optional, defaults to 1):
The index of the end of sentence token in the vocabulary.
pad_index (:obj:`int`, optional, defaults to 2):
The index of the padding token in the vocabulary.
unk_index (:obj:`int`, optional, defaults to 3):
The index of the unknown token in the vocabulary.
mask_index (:obj:`int`, optional, defaults to 5):
The index of the masking token in the vocabulary.
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.
summary_type (:obj:`string`, optional, defaults to "first"):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`.
Is one of the following options:
- 'last' => take the last token hidden state (like XLNet)
- 'first' => take the first token hidden state (like Bert)
- 'mean' => take the mean of all tokens hidden states
- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention
summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`.
Add a projection after the vector extraction
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
:class:`~transformers.XLMForSequenceClassification`.
'tanh' => add a tanh activation to the output, Other => no activation.
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
:class:`~transformers.XLMForSequenceClassification`.
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):
Argument used when doing sequence summary. Used in for the multiple choice head in
:class:`~transformers.XLMForSequenceClassification`.
Add a dropout before the projection and activation
start_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet.
end_n_top (:obj:`int`, optional, defaults to 5):
Used in the SQuAD evaluation script for XLM and XLNet.
mask_token_id (:obj:`int`, optional, defaults to 0):
Model agnostic parameter to identify masked tokens when generating text in an MLM context.
lang_id (:obj:`int`, optional, defaults to 1):
The ID of the language used by the model. This parameter is used when generating
text in a given language.
"""
pretrained_config_archive_map = FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "flaubert"
def __init__(self, layerdrop=0.0, pre_norm=False, pad_token_id=2, bos_token_id=0, **kwargs):
"""Constructs FlaubertConfig.
"""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
self.layerdrop = layerdrop
self.pre_norm = pre_norm