106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
|
|
# Copyright (c) 2018 Microsoft and The HuggingFace Inc. 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 sequence generation.
|
|
|
|
We use the procedure described in [1] to finetune models for sequence
|
|
generation. Let S1 and S2 be the source and target sequence respectively; we
|
|
pack them using the start of sequence [SOS] and end of sequence [EOS] token:
|
|
|
|
[SOS] S1 [EOS] S2 [EOS]
|
|
|
|
We then mask a fixed percentage of token from S2 at random and learn to predict
|
|
the masked words. [EOS] can be masked during finetuning so the model learns to
|
|
terminate the generation process.
|
|
|
|
[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 argparse
|
|
import logging
|
|
import random
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from transformers import BertConfig, Bert2Rnd, BertTokenizer
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def set_seed(args):
|
|
random.seed(args.seed)
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
|
|
|
|
def load_and_cache_examples(args, tokenizer):
|
|
raise NotImplementedError
|
|
|
|
|
|
def train(args, train_dataset, model, tokenizer):
|
|
""" Fine-tune the pretrained model on the corpus. """
|
|
raise NotImplementedError
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# Required parameters
|
|
parser.add_argument("--train_data_file",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The input training data file (a text file).")
|
|
parser.add_argument("--output_dir",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The output directory where the model predictions and checkpoints will be written.")
|
|
|
|
# Optional parameters
|
|
parser.add_argument("--model_name_or_path",
|
|
default="bert-base-cased",
|
|
type=str,
|
|
help="The model checkpoint for weights initialization.")
|
|
parser.add_argument("--seed", default=42, type=int)
|
|
args = parser.parse_args()
|
|
|
|
# Set up training device
|
|
device = torch.device("cpu")
|
|
|
|
# Set seed
|
|
set_seed(args)
|
|
|
|
# Load pretrained model and tokenizer
|
|
config_class, model_class, tokenizer_class = BertConfig, Bert2Rnd, BertTokenizer
|
|
config = config_class.from_pretrained(args.model_name_or_path)
|
|
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
|
|
model = model_class.from_pretrained(args.model_name_or_path, config=config)
|
|
model.to(device)
|
|
|
|
logger.info("Training/evaluation parameters %s", args)
|
|
|
|
# Training
|
|
train_dataset = load_and_cache_examples(args, tokenizer)
|
|
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
|
|
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|