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| # coding=utf-8 | |
| # Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ 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", | |
| } | |
| 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.""" | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs) | |
| self.layerdrop = layerdrop | |
| self.pre_norm = pre_norm | |