wehub-resource-sync
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
from __future__ import absolute_import, division, print_function
|
|
|
|
import argparse
|
|
import logging
|
|
import os
|
|
import random
|
|
import glob
|
|
import timeit
|
|
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler)
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
from tensorboardX import SummaryWriter
|
|
from tqdm import tqdm, trange
|
|
from transformers import (
|
|
WEIGHTS_NAME,
|
|
AdamW,
|
|
get_linear_schedule_with_warmup,
|
|
)
|
|
|
|
from markuplmft.models.markuplm import MarkupLMConfig, MarkupLMTokenizer, MarkupLMTokenizerFast, MarkupLMForQuestionAnswering
|
|
|
|
from utils import StrucDataset
|
|
from utils import (read_squad_examples, convert_examples_to_features, RawResult, write_predictions)
|
|
from utils_evaluate import EvalOpts, main as evaluate_on_squad
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
mp = "../../../../../results/markuplm-base"
|
|
op = "./moli"
|
|
config = MarkupLMConfig.from_pretrained(mp)
|
|
logger.info("=====Config for model=====")
|
|
logger.info(str(config))
|
|
max_depth = config.max_depth
|
|
tokenizer = MarkupLMTokenizer.from_pretrained(mp)
|
|
model = MarkupLMForQuestionAnswering.from_pretrained(mp, config=config)
|
|
|
|
tokenizer.save_pretrained(op) |