wehub-resource-sync
575 lines
29 KiB
Python
575 lines
29 KiB
Python
from __future__ import absolute_import, division, print_function
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import argparse
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import logging
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import os
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import random
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import glob
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import timeit
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import numpy as np
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import torch
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from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler)
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from torch.utils.data.distributed import DistributedSampler
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from tensorboardX import SummaryWriter
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from tqdm import tqdm, trange
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from transformers import (
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WEIGHTS_NAME,
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AdamW,
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get_linear_schedule_with_warmup,
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)
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from markuplmft.models.markuplm import MarkupLMConfig, MarkupLMTokenizer, MarkupLMTokenizerFast, MarkupLMForQuestionAnswering
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from utils import StrucDataset
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from utils import (read_squad_examples, convert_examples_to_features, RawResult, write_predictions)
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from utils_evaluate import EvalOpts, main as evaluate_on_squad
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logger = logging.getLogger(__name__)
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def set_seed(args):
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r"""
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Fix the random seed for reproduction.
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"""
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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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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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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 to_list(tensor):
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return tensor.detach().cpu().tolist()
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def train(args, train_dataset, model, tokenizer):
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r"""
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Train the model
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"""
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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else:
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tb_writer = None
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args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
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train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
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train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
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if args.max_steps > 0:
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t_total = args.max_steps
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args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
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else:
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t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ['bias', 'LayerNorm.weight']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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'weight_decay': args.weight_decay},
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{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = get_linear_schedule_with_warmup(optimizer,
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num_warmup_steps=int(args.warmup_ratio * t_total),
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num_training_steps=t_total)
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if args.fp16:
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try:
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from apex import amp
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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# multi-gpu training (should be after apex fp16 initialization)
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Distributed training (should be after apex fp16 initialization)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
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output_device=args.local_rank,
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find_unused_parameters=True)
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# Train!
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_dataset))
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logger.info(" Num Epochs = %d", args.num_train_epochs)
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logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
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logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
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args.train_batch_size * args.gradient_accumulation_steps * (
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torch.distributed.get_world_size() if args.local_rank != -1 else 1))
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logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
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logger.info(" Total optimization steps = %d", t_total)
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global_step = 0
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tr_loss, logging_loss = 0.0, 0.0
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model.zero_grad()
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train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
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set_seed(args) # Added here for reproductibility (even between python 2 and 3)
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for epoch in train_iterator:
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if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
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train_dataloader.sampler.set_epoch(epoch)
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epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
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for step, batch in enumerate(epoch_iterator):
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model.train()
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batch = tuple(t.to(args.device) for t in batch)
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inputs = {'input_ids': batch[0],
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'attention_mask': batch[1],
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'token_type_ids': batch[2],
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'xpath_tags_seq': batch[3],
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'xpath_subs_seq': batch[4],
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'start_positions': batch[5],
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'end_positions': batch[6],
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}
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outputs = model(**inputs)
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loss = outputs[0] # model outputs are always tuple in transformers (see doc)
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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if args.fp16:
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torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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else:
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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optimizer.step()
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scheduler.step() # Update learning rate schedule
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model.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
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# Log metrics
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if args.local_rank == -1 and args.evaluate_during_training:
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results = evaluate(args, model, tokenizer, prefix=str(global_step))
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for key, value in results.items():
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tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
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tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
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tb_writer.add_scalar('loss', (tr_loss - logging_loss) / args.logging_steps, global_step)
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logging_loss = tr_loss
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if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
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# Save model checkpoint
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output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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model_to_save = model.module if hasattr(model, 'module') else model
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# Take care of distributed/parallel training
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model_to_save.save_pretrained(output_dir)
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torch.save(args, os.path.join(output_dir, 'training_args.bin'))
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logger.info("Saving model checkpoint to %s", output_dir)
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if 0 < args.max_steps < global_step:
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epoch_iterator.close()
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break
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if 0 < args.max_steps < global_step:
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train_iterator.close()
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break
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if args.local_rank in [-1, 0]:
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tb_writer.close()
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return global_step, tr_loss / global_step
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def evaluate(args, model, tokenizer, max_depth, prefix=""):
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r"""
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Evaluate the model
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"""
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dataset, examples, features = load_and_cache_examples(args, tokenizer, max_depth=max_depth, evaluate=True,
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output_examples=True)
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if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(args.output_dir)
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args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
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# Note that DistributedSampler samples randomly
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eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
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eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
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, num_workers=args.dataloader_workers)
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# multi-gpu evaluate
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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logger.info("***** Running evaluation {} *****".format(prefix))
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logger.info(" Num examples = %d", len(dataset))
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logger.info(" Batch size = %d", args.eval_batch_size)
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all_results = []
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start_time = timeit.default_timer()
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for batch in tqdm(eval_dataloader, desc="Evaluating"):
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model.eval()
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batch = tuple(t.to(args.device) for t in batch)
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with torch.no_grad():
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inputs = {'input_ids': batch[0],
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'attention_mask': batch[1],
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'token_type_ids': batch[2],
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'xpath_tags_seq': batch[4],
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'xpath_subs_seq': batch[5],
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}
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feature_indices = batch[3]
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outputs = model(**inputs)
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for i, feature_index in enumerate(feature_indices):
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eval_feature = features[feature_index.item()]
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unique_id = int(eval_feature.unique_id)
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result = RawResult(unique_id=unique_id,
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start_logits=to_list(outputs[0][i]),
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end_logits=to_list(outputs[1][i]))
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all_results.append(result)
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eval_time = timeit.default_timer() - start_time
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logger.info(" Evaluation done in total %f secs (%f sec per example)", eval_time, eval_time / len(dataset))
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# Compute predictions
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output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
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output_tag_prediction_file = os.path.join(args.output_dir, "tag_predictions_{}.json".format(prefix))
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output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
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output_result_file = os.path.join(args.output_dir, "qas_eval_results_{}.json".format(prefix))
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output_file = os.path.join(args.output_dir, "eval_matrix_results_{}".format(prefix))
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write_predictions(examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case,
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output_prediction_file, output_tag_prediction_file, output_nbest_file, args.verbose_logging,
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tokenizer)
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# Evaluate
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evaluate_options = EvalOpts(data_file=args.predict_file,
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root_dir=args.root_dir,
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pred_file=output_prediction_file,
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tag_pred_file=output_tag_prediction_file,
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result_file=output_result_file,
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out_file=output_file)
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results = evaluate_on_squad(evaluate_options)
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return results
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def load_and_cache_examples(args, tokenizer, max_depth=50, evaluate=False, output_examples=False):
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r"""
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Load and process the raw data.
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"""
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if args.local_rank not in [-1, 0] and not evaluate:
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torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset,
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# and the others will use the cache
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# Load data features from cache or dataset file
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input_file = args.predict_file if evaluate else args.train_file
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cached_features_file = os.path.join(os.path.dirname(input_file), 'cached', 'cached_{}_{}_{}_{}'.format(
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'dev' if evaluate else 'train',
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"markuplm",
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str(args.max_seq_length),
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str(max_depth)
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))
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if not os.path.exists(os.path.dirname(cached_features_file)):
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os.makedirs(os.path.dirname(cached_features_file))
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if os.path.exists(cached_features_file) and not args.overwrite_cache:
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logger.info("Loading features from cached file %s", cached_features_file)
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features = torch.load(cached_features_file)
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if output_examples:
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examples, tag_list = read_squad_examples(input_file=input_file,
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root_dir=args.root_dir,
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is_training=not evaluate,
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tokenizer=tokenizer,
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simplify=True,
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max_depth=max_depth
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)
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else:
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examples = None
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else:
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logger.info("Creating features from dataset file at %s", input_file)
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examples, _ = read_squad_examples(input_file=input_file,
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root_dir=args.root_dir,
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is_training=not evaluate,
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tokenizer=tokenizer,
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simplify=False,
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max_depth=max_depth)
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features = convert_examples_to_features(examples=examples,
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tokenizer=tokenizer,
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max_seq_length=args.max_seq_length,
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doc_stride=args.doc_stride,
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max_query_length=args.max_query_length,
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is_training=not evaluate,
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cls_token=tokenizer.cls_token,
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sep_token=tokenizer.sep_token,
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pad_token=tokenizer.pad_token_id,
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sequence_a_segment_id=0,
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sequence_b_segment_id=0,
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max_depth=max_depth)
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if args.local_rank in [-1, 0] and args.save_features:
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logger.info("Saving features into cached file %s", cached_features_file)
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torch.save(features, cached_features_file)
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if args.local_rank == 0 and not evaluate:
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torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset,
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# and the others will use the cache
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# Convert to Tensors and build dataset
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all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
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all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
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all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
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all_xpath_tags_seq = torch.tensor([f.xpath_tags_seq for f in features], dtype=torch.long)
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all_xpath_subs_seq = torch.tensor([f.xpath_subs_seq for f in features], dtype=torch.long)
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if evaluate:
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all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
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dataset = StrucDataset(all_input_ids, all_input_mask, all_segment_ids, all_feature_index,
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all_xpath_tags_seq, all_xpath_subs_seq, )
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else:
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all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
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all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
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dataset = StrucDataset(all_input_ids, all_input_mask, all_segment_ids,
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all_xpath_tags_seq, all_xpath_subs_seq,
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all_start_positions, all_end_positions, )
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if output_examples:
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dataset = (dataset, examples, features)
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return dataset
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def main():
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parser = argparse.ArgumentParser()
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# Required parameters
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parser.add_argument("--train_file", default=None, type=str, required=True,
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help="json for training. E.g., train-v1.1.json")
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parser.add_argument("--predict_file", default=None, type=str, required=True,
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help="json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
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parser.add_argument("--root_dir", default=None, type=str, required=True,
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help="the root directory of the raw WebSRC dataset, which contains the HTML files.")
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parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models")
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parser.add_argument("--output_dir", default=None, type=str, required=True,
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help="The output directory where the model checkpoints and predictions will be written.")
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# Other parameters
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parser.add_argument("--config_name", default="", type=str,
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help="Pretrained config name or path if not the same as model_name")
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parser.add_argument("--tokenizer_name", default="", type=str,
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help="Pretrained tokenizer name or path if not the same as model_name")
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parser.add_argument("--cache_dir", default=None, type=str,
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help="Where do you want to store the pre-trained models downloaded from s3")
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parser.add_argument("--do_lower_case", action='store_true',
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help="Set this flag if you are using an uncased model.")
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parser.add_argument("--max_seq_length", default=384, type=int,
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help="The maximum total input sequence length after WordPiece tokenization. Sequences "
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"longer than this will be truncated, and sequences shorter than this will be padded.")
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parser.add_argument("--doc_stride", default=128, type=int,
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help="When splitting up a long document into chunks, how much stride to take between chunks.")
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parser.add_argument("--max_query_length", default=64, type=int,
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help="The maximum number of tokens for the question. Questions longer than this will "
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"be truncated to this length.")
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parser.add_argument("--max_answer_length", default=30, type=int,
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help="The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another.")
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parser.add_argument("--verbose_logging", action='store_true',
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help="If true, all of the warnings related to data processing will be printed. "
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"A number of warnings are expected for a normal SQuAD evaluation.")
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parser.add_argument("--do_train", action='store_true',
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help="Whether to run training.")
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parser.add_argument("--do_eval", action='store_true',
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help="Whether to run eval on the dev set.")
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parser.add_argument("--evaluate_during_training", action='store_true',
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help="Run evaluation during training at each logging step.")
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parser.add_argument("--eval_all_checkpoints", action='store_true',
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help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending "
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"with step number")
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parser.add_argument('--eval_from_checkpoint', type=int, default=0,
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help="Only evaluate the checkpoints with prefix larger than or equal to it, beside the final "
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"checkpoint with no prefix")
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parser.add_argument('--eval_to_checkpoint', type=int, default=None,
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help="Only evaluate the checkpoints with prefix smaller than it, beside the final checkpoint "
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"with no prefix")
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parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
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help="Batch size per GPU/CPU for training.")
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parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
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help="Batch size per GPU/CPU for evaluation.")
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parser.add_argument("--learning_rate", default=1e-5, type=float,
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help="The initial learning rate for Adam.")
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parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.")
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parser.add_argument("--weight_decay", default=0.0, type=float,
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help="Weight decay if we apply some.")
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parser.add_argument("--adam_epsilon", default=1e-8, type=float,
|
|
help="Epsilon for Adam optimizer.")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float,
|
|
help="Max gradient norm.")
|
|
parser.add_argument("--num_train_epochs", default=3.0, type=float,
|
|
help="Total number of training epochs to perform.")
|
|
parser.add_argument("--max_steps", default=-1, type=int,
|
|
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
|
|
parser.add_argument("--warmup_ratio", default=0.0, type=float,
|
|
help="RT.")
|
|
parser.add_argument("--n_best_size", default=20, type=int,
|
|
help="The total number of n-best predictions to generate in the nbest_predictions.json output "
|
|
"file.")
|
|
|
|
parser.add_argument('--logging_steps', type=int, default=10,
|
|
help="Log every X updates steps.")
|
|
parser.add_argument('--save_steps', type=int, default=3000,
|
|
help="Save checkpoint every X updates steps.")
|
|
parser.add_argument("--no_cuda", action='store_true',
|
|
help="Whether not to use CUDA when available")
|
|
parser.add_argument('--overwrite_output_dir', action='store_true',
|
|
help="Overwrite the content of the output directory")
|
|
parser.add_argument('--overwrite_cache', action='store_true',
|
|
help="Overwrite the cached training and evaluation sets")
|
|
parser.add_argument('--save_features', type=bool, default=True,
|
|
help="whether or not to save the processed features, default is True")
|
|
parser.add_argument('--seed', type=int, default=42,
|
|
help="random seed for initialization")
|
|
|
|
parser.add_argument("--local_rank", type=int, default=-1,
|
|
help="local_rank for distributed training on gpus")
|
|
parser.add_argument('--fp16', action='store_true',
|
|
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
|
|
parser.add_argument('--fp16_opt_level', type=str, default='O1',
|
|
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
|
|
"See details at https://nvidia.github.io/apex/amp.html")
|
|
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
|
|
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
|
|
args = parser.parse_args()
|
|
|
|
if os.path.exists(args.output_dir) and os.listdir(
|
|
args.output_dir) and args.do_train and not args.overwrite_output_dir:
|
|
raise ValueError(
|
|
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
|
|
args.output_dir))
|
|
|
|
# Setup CUDA, GPU & distributed training
|
|
if args.local_rank == -1 or args.no_cuda:
|
|
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
|
args.n_gpu = torch.cuda.device_count() if not args.no_cuda else 0
|
|
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
|
torch.cuda.set_device(args.local_rank)
|
|
device = torch.device("cuda", args.local_rank)
|
|
torch.distributed.init_process_group(backend='nccl')
|
|
args.n_gpu = 1
|
|
args.device = device
|
|
|
|
# Setup logging
|
|
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
|
|
datefmt='%m/%d/%Y %H:%M:%S',
|
|
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
|
|
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
|
|
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
|
|
|
|
# Set seed
|
|
set_seed(args)
|
|
|
|
# Load pretrained model and tokenizer
|
|
if args.local_rank not in [-1, 0]:
|
|
torch.distributed.barrier()
|
|
# Make sure only the first process in distributed training will download model & vocab
|
|
|
|
config = MarkupLMConfig.from_pretrained(args.model_name_or_path)
|
|
logger.info("=====Config for model=====")
|
|
logger.info(str(config))
|
|
max_depth = config.max_depth
|
|
tokenizer = MarkupLMTokenizer.from_pretrained(args.model_name_or_path)
|
|
model = MarkupLMForQuestionAnswering.from_pretrained(args.model_name_or_path, config=config)
|
|
|
|
if args.local_rank == 0:
|
|
torch.distributed.barrier()
|
|
# Make sure only the first process in distributed training will download model & vocab
|
|
|
|
model.to(args.device)
|
|
|
|
logger.info("Training/evaluation parameters %s", args)
|
|
|
|
# Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is
|
|
# set. Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running
|
|
# `--fp16_opt_level="O2"` will remove the need for this code, but it is still valid.
|
|
if args.fp16:
|
|
try:
|
|
import apex
|
|
apex.amp.register_half_function(torch, 'einsum')
|
|
except ImportError:
|
|
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
|
|
|
|
# Training
|
|
if args.do_train:
|
|
train_dataset = load_and_cache_examples(args, tokenizer, max_depth=max_depth, evaluate=False,
|
|
output_examples=False)
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
model.to(args.device)
|
|
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
|
|
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
|
|
# Save the trained model and the tokenizer
|
|
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
|
# Create output directory if needed
|
|
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
|
|
os.makedirs(args.output_dir)
|
|
|
|
logger.info("Saving model checkpoint to %s", args.output_dir)
|
|
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
|
|
# They can then be reloaded using `from_pretrained()`
|
|
# Take care of distributed/parallel training
|
|
model_to_save = model.module if hasattr(model, 'module') else model
|
|
model_to_save.save_pretrained(args.output_dir)
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
|
|
|
|
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
|
|
results = {}
|
|
if args.do_eval and args.local_rank in [-1, 0]:
|
|
checkpoints = [args.output_dir]
|
|
if args.eval_all_checkpoints:
|
|
checkpoints = list(
|
|
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
|
|
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs
|
|
|
|
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
|
config = MarkupLMConfig.from_pretrained(args.output_dir)
|
|
tokenizer = MarkupLMTokenizer.from_pretrained(args.output_dir)
|
|
|
|
for checkpoint in checkpoints:
|
|
# Reload the model
|
|
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
|
|
try:
|
|
int(global_step)
|
|
except ValueError:
|
|
global_step = ""
|
|
if global_step and int(global_step) < args.eval_from_checkpoint:
|
|
continue
|
|
if global_step and args.eval_to_checkpoint is not None and int(global_step) >= args.eval_to_checkpoint:
|
|
continue
|
|
model = MarkupLMForQuestionAnswering.from_pretrained(checkpoint, config=config)
|
|
model.to(args.device)
|
|
|
|
# Evaluate
|
|
result = evaluate(args, model, tokenizer, max_depth=max_depth, prefix=global_step)
|
|
|
|
result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
|
|
results.update(result)
|
|
|
|
logger.info("Results: {}".format(results))
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|