Source code for transformers.configuration_longformer

# coding=utf-8
# Copyright 2020 The Allen Institute for AI team 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" Longformer configuration """

from typing import List, Union

from .configuration_roberta import RobertaConfig
from .utils import logging

logger = logging.get_logger(__name__)

    "allenai/longformer-base-4096": "",
    "allenai/longformer-large-4096": "",
    "allenai/longformer-large-4096-finetuned-triviaqa": "",
    "allenai/longformer-base-4096-extra.pos.embd.only": "",
    "allenai/longformer-large-4096-extra.pos.embd.only": "",

[docs]class LongformerConfig(RobertaConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.LongformerModel`. It is used to instantiate an Longformer 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 RoBERTa `roberta-base <>`__ architecture with a sequence length 4,096. The :class:`~transformers.LongformerConfig` class directly inherits :class:`~transformers.RobertaConfig`. It reuses the same defaults. Please check the parent class for more information. Args: attention_window (:obj:`int` or :obj:`List[int]`, optional, defaults to 512): Size of an attention window around each token. If :obj:`int`, use the same size for all layers. To specify a different window size for each layer, use a :obj:`List[int]` where ``len(attention_window) == num_hidden_layers``. Example:: >>> from transformers import LongformerConfig, LongformerModel >>> # Initializing a Longformer configuration >>> configuration = LongformerConfig() >>> # Initializing a model from the configuration >>> model = LongformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "longformer" def __init__(self, attention_window: Union[List[int], int] = 512, sep_token_id: int = 2, **kwargs): super().__init__(**kwargs) self.attention_window = attention_window self.sep_token_id = sep_token_id