Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/flaubert
/configuration_flaubert.py
| # 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""" | |
| from collections import OrderedDict | |
| from typing import Mapping | |
| from ...configuration_utils import PretrainedConfig | |
| from ...onnx import OnnxConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| class FlaubertConfig(PretrainedConfig): | |
| """ | |
| 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. | |
| Instantiating a configuration with the defaults will yield a similar configuration to that of the FlauBERT | |
| [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_uncased) 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" | |
| attribute_map = { | |
| "hidden_size": "emb_dim", | |
| "num_attention_heads": "n_heads", | |
| "num_hidden_layers": "n_layers", | |
| "n_words": "vocab_size", # For backward compatibility | |
| } | |
| def __init__( | |
| self, | |
| pre_norm=False, | |
| layerdrop=0.0, | |
| vocab_size=30145, | |
| emb_dim=2048, | |
| n_layers=12, | |
| n_heads=16, | |
| dropout=0.1, | |
| attention_dropout=0.1, | |
| gelu_activation=True, | |
| sinusoidal_embeddings=False, | |
| causal=False, | |
| asm=False, | |
| n_langs=1, | |
| use_lang_emb=True, | |
| max_position_embeddings=512, | |
| embed_init_std=2048**-0.5, | |
| layer_norm_eps=1e-12, | |
| init_std=0.02, | |
| bos_index=0, | |
| eos_index=1, | |
| pad_index=2, | |
| unk_index=3, | |
| mask_index=5, | |
| is_encoder=True, | |
| summary_type="first", | |
| summary_use_proj=True, | |
| summary_activation=None, | |
| summary_proj_to_labels=True, | |
| summary_first_dropout=0.1, | |
| start_n_top=5, | |
| end_n_top=5, | |
| mask_token_id=0, | |
| lang_id=0, | |
| pad_token_id=2, | |
| bos_token_id=0, | |
| **kwargs, | |
| ): | |
| """Constructs FlaubertConfig.""" | |
| self.pre_norm = pre_norm | |
| self.layerdrop = layerdrop | |
| self.vocab_size = vocab_size | |
| self.emb_dim = emb_dim | |
| self.n_layers = n_layers | |
| self.n_heads = n_heads | |
| self.dropout = dropout | |
| self.attention_dropout = attention_dropout | |
| self.gelu_activation = gelu_activation | |
| self.sinusoidal_embeddings = sinusoidal_embeddings | |
| self.causal = causal | |
| self.asm = asm | |
| self.n_langs = n_langs | |
| self.use_lang_emb = use_lang_emb | |
| self.layer_norm_eps = layer_norm_eps | |
| self.bos_index = bos_index | |
| self.eos_index = eos_index | |
| self.pad_index = pad_index | |
| self.unk_index = unk_index | |
| self.mask_index = mask_index | |
| self.is_encoder = is_encoder | |
| self.max_position_embeddings = max_position_embeddings | |
| self.embed_init_std = embed_init_std | |
| self.init_std = init_std | |
| self.summary_type = summary_type | |
| self.summary_use_proj = summary_use_proj | |
| self.summary_activation = summary_activation | |
| self.summary_proj_to_labels = summary_proj_to_labels | |
| self.summary_first_dropout = summary_first_dropout | |
| self.start_n_top = start_n_top | |
| self.end_n_top = end_n_top | |
| self.mask_token_id = mask_token_id | |
| self.lang_id = lang_id | |
| if "n_words" in kwargs: | |
| self.n_words = kwargs["n_words"] | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs) | |
| class FlaubertOnnxConfig(OnnxConfig): | |
| def inputs(self) -> Mapping[str, Mapping[int, str]]: | |
| if self.task == "multiple-choice": | |
| dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} | |
| else: | |
| dynamic_axis = {0: "batch", 1: "sequence"} | |
| return OrderedDict( | |
| [ | |
| ("input_ids", dynamic_axis), | |
| ("attention_mask", dynamic_axis), | |
| ] | |
| ) | |