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configuration_camembert.py18770644editdlrm
modeling_camembert.py55030644editdlrm
modeling_tf_camembert.py64510644editdlrm
tokenization_camembert.py129470644editdlrm
tokenization_camembert_fast.py86570644editdlrm
__init__.py34340644editdlrm
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/camembert/configuration_camembert.py (1877B)
# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # 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. """ CamemBERT configuration""" from collections import OrderedDict from typing import Mapping from ...onnx import OnnxConfig from ...utils import logging from ..roberta.configuration_roberta import RobertaConfig logger = logging.get_logger(__name__) CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json", "umberto-commoncrawl-cased-v1": "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json", "umberto-wikipedia-uncased-v1": "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json", } class CamembertConfig(RobertaConfig): """ This class overrides [`RobertaConfig`]. Please check the superclass for the appropriate documentation alongside usage examples. """ model_type = "camembert" class CamembertOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ] )