mirror of
https://github.com/huggingface/transformers.git
synced 2025-08-03 03:31:05 +06:00
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
# 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.
|
|
""" Finetuning seq2seq models for abstractive summarization.
|
|
|
|
The finetuning method for abstractive summarization is inspired by [1]. We
|
|
concatenate the document and summary, mask words of the summary at random and
|
|
maximizing the likelihood of masked words.
|
|
|
|
[1] Dong Li, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng
|
|
Gao, Ming Zhou, and Hsiao-Wuen Hon. “Unified Language Model Pre-Training for
|
|
Natural Language Understanding and Generation.” (May 2019) ArXiv:1905.03197
|
|
"""
|
|
|
|
import logging
|
|
import random
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def set_seed(args):
|
|
random.seed(args.seed)
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
if args.n_gpu > 0:
|
|
torch.cuda.manual_seed_all(args.seed)
|
|
|
|
|
|
def train(args, train_dataset, model, tokenizer):
|
|
raise NotImplementedError
|
|
|
|
|
|
def evaluate(args, model, tokenizer, prefix=""):
|
|
raise NotImplementedError
|