/usr/local/lib/python3.6/site-packages/transformers/models/flaubert
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/flaubert/configuration_flaubert.py (8940B)
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# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
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# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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""" Flaubert configuration, based on XLM."""
from collections import OrderedDict
from typing import Mapping
from ...onnx import OnnxConfig
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",
}
class FlaubertConfig(XLMConfig):
"""
This is the configuration class to store the configuration of a [`FlaubertModel`] or a [`TFFlaubertModel`]. It is
used to instantiate a FlauBERT model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
pre_norm (`bool`, *optional*, defaults to `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 (`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 (`int`, *optional*, defaults to 30145):
Vocabulary size of the FlauBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`FlaubertModel`] or [`TFFlaubertModel`].
emb_dim (`int`, *optional*, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention mechanism
gelu_activation (`bool`, *optional*, defaults to `True`):
Whether or not to use a *gelu* activation instead of *relu*.
sinusoidal_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
causal (`bool`, *optional*, defaults to `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 (`bool`, *optional*, defaults to `False`):
Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
layer.
n_langs (`int`, *optional*, defaults to 1):
The number of languages the model handles. Set to 1 for monolingual models.
use_lang_emb (`bool`, *optional*, defaults to `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 (`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 (`float`, *optional*, defaults to 2048^-0.5):
The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
init_std (`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 (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
bos_index (`int`, *optional*, defaults to 0):
The index of the beginning of sentence token in the vocabulary.
eos_index (`int`, *optional*, defaults to 1):
The index of the end of sentence token in the vocabulary.
pad_index (`int`, *optional*, defaults to 2):
The index of the padding token in the vocabulary.
unk_index (`int`, *optional*, defaults to 3):
The index of the unknown token in the vocabulary.
mask_index (`int`, *optional*, defaults to 5):
The index of the masking token in the vocabulary.
is_encoder(`bool`, *optional*, defaults to `True`):
Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
summary_type (`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:
- `"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 (like GPT/GPT-2).
- `"attn"`: Not implemented now, use multi-head attention.
summary_use_proj (`bool`, *optional*, defaults to `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 (`str`, *optional*):
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Used in the sequence classification and multiple choice models.
Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
summary_first_dropout (`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 (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
end_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
mask_token_id (`int`, *optional*, defaults to 0):
Model agnostic parameter to identify masked tokens when generating text in an MLM context.
lang_id (`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
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
class FlaubertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
]
)