# Source code for transformers.models.flaubert.configuration_flaubert

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""" Flaubert configuration, based on XLM. """

from ...utils import logging
from ..xlm.configuration_xlm import XLMConfig

logger = logging.get_logger(__name__)

FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
"flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
"flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
"flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}

[docs]class FlaubertConfig(XLMConfig):
"""
This is the configuration class to store the configuration of a :class:~transformers.FlaubertModel or a
:class:~transformers.TFFlaubertModel. It is used to instantiate a FlauBERT model according to the specified
arguments, defining the model architecture.

Configuration objects inherit from :class:~transformers.PretrainedConfig and can be used to control the model
outputs. Read the documentation from :class:~transformers.PretrainedConfig for more information.

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 number of different tokens that can be represented by
the :obj:inputs_ids passed when calling :class:~transformers.FlaubertModel or
:class:~transformers.TFFlaubertModel.
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:bool, optional, defaults to :obj:True):
Whether or not to use a gelu activation instead of relu.
sinusoidal_embeddings (:obj:bool, optional, defaults to :obj:False):
Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
causal (:obj:bool, optional, defaults to :obj:False):
Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in
order to only attend to the left-side context instead if a bidirectional context.
asm (:obj:bool, optional, defaults to :obj:False):
Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
layer.
n_langs (:obj:int, optional, defaults to 1):
The number of languages the model handles. Set to 1 for monolingual models.
use_lang_emb (:obj:bool, optional, defaults to :obj:True)
Whether to use language embeddings. Some models use additional language embeddings, see the multilingual
models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>__ for
information on how to use them.
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:bool, optional, defaults to :obj:True):
Whether or not 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 the sequence classification and multiple choice models.

Has to be one of the following options:

- :obj:"last": Take the last token hidden state (like XLNet).
- :obj:"first": Take the first token hidden state (like BERT).
- :obj:"mean": Take the mean of all tokens hidden states.
- :obj:"cls_index": Supply a Tensor of classification token position (like GPT/GPT-2).
- :obj:"attn": Not implemented now, use multi-head attention.
summary_use_proj (:obj:bool, optional, defaults to :obj:True):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

Whether or not to add a projection after the vector extraction.
summary_activation (:obj:str, optional):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

Pass :obj:"tanh" for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (:obj:bool, optional, defaults to :obj:True):
Used in the sequence classification and multiple choice models.

Whether the projection outputs should have :obj:config.num_labels or :obj:config.hidden_size classes.
summary_first_dropout (:obj:float, optional, defaults to 0.1):
Used in the sequence classification and multiple choice models.

The dropout ratio to be used after the projection and activation.
start_n_top (:obj:int, optional, defaults to 5):
Used in the SQuAD evaluation script.
end_n_top (:obj:int, optional, defaults to 5):
Used in the SQuAD evaluation script.
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.
"""

model_type = "flaubert"

def __init__(self, layerdrop=0.0, pre_norm=False, pad_token_id=2, bos_token_id=0, **kwargs):
"""Constructs FlaubertConfig."""
self.layerdrop = layerdrop
self.pre_norm = pre_norm