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68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018 Microsoft and The HuggingFace Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Finetuning seq2seq models for sequence generation.
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We use the procedure described in [1] to finetune models for sequence
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generation. Let S1 and S2 be the source and target sequence respectively; we
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pack them using the start of sequence [SOS] and end of sequence [EOS] token:
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[SOS] S1 [EOS] S2 [EOS]
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We then mask a fixed percentage of token from S2 at random and learn to predict
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the masked words. [EOS] can be masked during finetuning so the model learns to
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terminate the generation process.
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[1] Dong Li, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng
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Gao, Ming Zhou, and Hsiao-Wuen Hon. “Unified Language Model Pre-Training for
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Natural Language Understanding and Generation.” (May 2019) ArXiv:1905.03197
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"""
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import logging
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import random
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import numpy as np
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import torch
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logger = logging.getLogger(__name__)
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def set_seed(args):
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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def train(args, train_dataset, model, tokenizer):
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""" Fine-tune the pretrained model on the corpus. """
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# Data sampler
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# Data loader
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# Training
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raise NotImplementedError
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def evaluate(args, model, tokenizer, prefix=""):
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raise NotImplementedError
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def main():
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raise NotImplementedError
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def __main__():
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main()
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