wehub-resource-sync
812 lines
28 KiB
Python
812 lines
28 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, NVIDIA CORPORATION. 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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""" Fine-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """
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from __future__ import absolute_import, division, print_function
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import argparse
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import glob
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import logging
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import os
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import random
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import shutil
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import numpy as np
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import torch
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from seqeval.metrics import (
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classification_report,
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f1_score,
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precision_score,
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recall_score,
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)
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from tensorboardX import SummaryWriter
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from torch.nn import CrossEntropyLoss
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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 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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BertConfig,
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BertForTokenClassification,
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BertTokenizer,
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RobertaConfig,
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RobertaForTokenClassification,
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RobertaTokenizer,
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get_linear_schedule_with_warmup,
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)
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from layoutlm import FunsdDataset, LayoutlmConfig, LayoutlmForTokenClassification
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logger = logging.getLogger(__name__)
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ALL_MODELS = sum(
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(
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tuple(conf.pretrained_config_archive_map.keys())
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for conf in (BertConfig, RobertaConfig, LayoutlmConfig)
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),
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(),
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)
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MODEL_CLASSES = {
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"bert": (BertConfig, BertForTokenClassification, BertTokenizer),
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"roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
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"layoutlm": (LayoutlmConfig, LayoutlmForTokenClassification, BertTokenizer),
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}
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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 collate_fn(data):
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batch = [i for i in zip(*data)]
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for i in range(len(batch)):
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if i < len(batch) - 2:
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batch[i] = torch.stack(batch[i], 0)
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return tuple(batch)
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def get_labels(path):
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with open(path, "r") as f:
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labels = f.read().splitlines()
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if "O" not in labels:
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labels = ["O"] + labels
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return labels
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def train( # noqa C901
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args, train_dataset, model, tokenizer, labels, pad_token_label_id
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):
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""" Train the model """
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter(logdir="runs/" + os.path.basename(args.output_dir))
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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 = (
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RandomSampler(train_dataset)
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if args.local_rank == -1
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else DistributedSampler(train_dataset)
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)
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train_dataloader = DataLoader(
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train_dataset,
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sampler=train_sampler,
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batch_size=args.train_batch_size,
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collate_fn=None,
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)
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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 = (
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args.max_steps
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// (len(train_dataloader) // args.gradient_accumulation_steps)
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+ 1
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)
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else:
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t_total = (
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len(train_dataloader)
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// args.gradient_accumulation_steps
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* args.num_train_epochs
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)
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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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{
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"params": [
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p
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for n, p in model.named_parameters()
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if not any(nd in n for nd in no_decay)
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],
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"weight_decay": args.weight_decay,
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},
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{
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"params": [
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p
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for n, p in model.named_parameters()
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if any(nd in n for nd in no_decay)
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],
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"weight_decay": 0.0,
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},
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]
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optimizer = AdamW(
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optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon
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)
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scheduler = get_linear_schedule_with_warmup(
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optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
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)
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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(
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"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
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)
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model, optimizer = amp.initialize(
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model, optimizer, opt_level=args.fp16_opt_level
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)
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# multi-gpu training (should be after apex fp16 initialization)
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if args.n_gpu > 1:
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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(
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model,
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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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)
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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(
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" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size
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)
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logger.info(
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" Total train batch size (w. parallel, distributed & accumulation) = %d",
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args.train_batch_size
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* args.gradient_accumulation_steps
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* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
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)
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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(
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int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
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)
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set_seed(args) # Added here for reproductibility (even between python 2 and 3)
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for _ in train_iterator:
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epoch_iterator = tqdm(
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train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]
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)
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for step, batch in enumerate(epoch_iterator):
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model.train()
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inputs = {
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"input_ids": batch[0].to(args.device),
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"attention_mask": batch[1].to(args.device),
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"labels": batch[3].to(args.device),
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}
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if args.model_type in ["layoutlm"]:
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inputs["bbox"] = batch[4].to(args.device)
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inputs["token_type_ids"] = (
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batch[2].to(args.device) if args.model_type in ["bert", "layoutlm"] else None
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) # RoBERTa don"t use segment_ids
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outputs = model(**inputs)
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# model outputs are always tuple in pytorch-transformers (see doc)
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loss = outputs[0]
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel 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_(
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amp.master_params(optimizer), args.max_grad_norm
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)
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else:
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torch.nn.utils.clip_grad_norm_(
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model.parameters(), args.max_grad_norm
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)
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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 (
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args.local_rank in [-1, 0]
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and args.logging_steps > 0
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and global_step % args.logging_steps == 0
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):
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# Log metrics
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if (
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args.local_rank in [-1, 0] and args.evaluate_during_training
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): # Only evaluate when single GPU otherwise metrics may not average well
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results, _ = evaluate(
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args,
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model,
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tokenizer,
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labels,
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pad_token_label_id,
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mode="dev",
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)
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for key, value in results.items():
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tb_writer.add_scalar(
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"eval_{}".format(key), value, global_step
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)
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tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
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tb_writer.add_scalar(
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"loss",
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(tr_loss - logging_loss) / args.logging_steps,
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global_step,
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)
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logging_loss = tr_loss
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if (
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args.local_rank in [-1, 0]
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and args.save_steps > 0
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and global_step % args.save_steps == 0
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):
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# Save model checkpoint
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output_dir = os.path.join(
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args.output_dir, "checkpoint-{}".format(global_step)
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)
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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 = (
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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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tokenizer.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 args.max_steps > 0 and global_step > args.max_steps:
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epoch_iterator.close()
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break
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if args.max_steps > 0 and global_step > args.max_steps:
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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, labels, pad_token_label_id, mode, prefix=""):
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eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=mode)
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args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
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eval_sampler = SequentialSampler(eval_dataset)
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eval_dataloader = DataLoader(
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eval_dataset,
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sampler=eval_sampler,
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batch_size=args.eval_batch_size,
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collate_fn=None,
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)
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# Eval!
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logger.info("***** Running evaluation %s *****", prefix)
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logger.info(" Num examples = %d", len(eval_dataset))
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logger.info(" Batch size = %d", args.eval_batch_size)
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eval_loss = 0.0
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nb_eval_steps = 0
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preds = None
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out_label_ids = None
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model.eval()
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for batch in tqdm(eval_dataloader, desc="Evaluating"):
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with torch.no_grad():
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inputs = {
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"input_ids": batch[0].to(args.device),
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"attention_mask": batch[1].to(args.device),
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"labels": batch[3].to(args.device),
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}
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if args.model_type in ["layoutlm"]:
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inputs["bbox"] = batch[4].to(args.device)
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inputs["token_type_ids"] = (
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batch[2].to(args.device)
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if args.model_type in ["bert", "layoutlm"]
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else None
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) # RoBERTa don"t use segment_ids
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outputs = model(**inputs)
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tmp_eval_loss, logits = outputs[:2]
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if args.n_gpu > 1:
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tmp_eval_loss = (
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tmp_eval_loss.mean()
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) # mean() to average on multi-gpu parallel evaluating
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eval_loss += tmp_eval_loss.item()
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nb_eval_steps += 1
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if preds is None:
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preds = logits.detach().cpu().numpy()
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out_label_ids = inputs["labels"].detach().cpu().numpy()
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else:
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preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
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out_label_ids = np.append(
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out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0
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)
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eval_loss = eval_loss / nb_eval_steps
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preds = np.argmax(preds, axis=2)
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label_map = {i: label for i, label in enumerate(labels)}
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out_label_list = [[] for _ in range(out_label_ids.shape[0])]
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preds_list = [[] for _ in range(out_label_ids.shape[0])]
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for i in range(out_label_ids.shape[0]):
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for j in range(out_label_ids.shape[1]):
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if out_label_ids[i, j] != pad_token_label_id:
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out_label_list[i].append(label_map[out_label_ids[i][j]])
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preds_list[i].append(label_map[preds[i][j]])
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results = {
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"loss": eval_loss,
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"precision": precision_score(out_label_list, preds_list),
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"recall": recall_score(out_label_list, preds_list),
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"f1": f1_score(out_label_list, preds_list),
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}
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report = classification_report(out_label_list, preds_list)
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logger.info("\n" + report)
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logger.info("***** Eval results %s *****", prefix)
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for key in sorted(results.keys()):
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logger.info(" %s = %s", key, str(results[key]))
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return results, preds_list
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def main(): # noqa C901
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument(
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"--data_dir",
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default=None,
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type=str,
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required=True,
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help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
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)
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parser.add_argument(
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"--model_type",
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default=None,
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type=str,
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required=True,
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help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
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)
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parser.add_argument(
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"--model_name_or_path",
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default=None,
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type=str,
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required=True,
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help="Path to pre-trained model or shortcut name selected in the list: "
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+ ", ".join(ALL_MODELS),
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)
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parser.add_argument(
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"--output_dir",
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default=None,
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type=str,
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required=True,
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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## Other parameters
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parser.add_argument(
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"--labels",
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default="",
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type=str,
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help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.",
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)
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parser.add_argument(
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"--config_name",
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default="",
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type=str,
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help="Pretrained config name or path if not the same as model_name",
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)
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parser.add_argument(
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"--tokenizer_name",
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default="",
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type=str,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--cache_dir",
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default="",
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type=str,
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help="Where do you want to store the pre-trained models downloaded from s3",
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)
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parser.add_argument(
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"--max_seq_length",
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default=512,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--do_train", action="store_true", help="Whether to run training."
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)
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parser.add_argument(
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"--do_eval", action="store_true", help="Whether to run eval on the dev set."
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)
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parser.add_argument(
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"--do_predict",
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action="store_true",
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help="Whether to run predictions on the test set.",
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)
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parser.add_argument(
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"--evaluate_during_training",
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action="store_true",
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help="Whether to run evaluation during training at each logging step.",
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)
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parser.add_argument(
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"--do_lower_case",
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action="store_true",
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help="Set this flag if you are using an uncased model.",
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)
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parser.add_argument(
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"--per_gpu_train_batch_size",
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default=8,
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type=int,
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help="Batch size per GPU/CPU for training.",
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)
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parser.add_argument(
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"--per_gpu_eval_batch_size",
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default=8,
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type=int,
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help="Batch size per GPU/CPU for evaluation.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--learning_rate",
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default=5e-5,
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type=float,
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help="The initial learning rate for Adam.",
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)
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parser.add_argument(
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"--weight_decay", default=0.0, type=float, help="Weight decay if we apply some."
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)
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parser.add_argument(
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"--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer."
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)
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parser.add_argument(
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"--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
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)
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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_steps", default=0, type=int, help="Linear warmup over warmup_steps."
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--logging_steps", type=int, default=50, help="Log every X updates steps."
|
|
)
|
|
parser.add_argument(
|
|
"--save_steps",
|
|
type=int,
|
|
default=50,
|
|
help="Save checkpoint every X updates steps.",
|
|
)
|
|
parser.add_argument(
|
|
"--eval_all_checkpoints",
|
|
action="store_true",
|
|
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
|
|
)
|
|
parser.add_argument(
|
|
"--no_cuda", action="store_true", help="Avoid using 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(
|
|
"--seed", type=int, default=42, help="random seed for initialization"
|
|
)
|
|
|
|
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(
|
|
"--local_rank",
|
|
type=int,
|
|
default=-1,
|
|
help="For distributed training: local_rank",
|
|
)
|
|
parser.add_argument(
|
|
"--server_ip", type=str, default="", help="For distant debugging."
|
|
)
|
|
parser.add_argument(
|
|
"--server_port", type=str, default="", help="For distant debugging."
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
if (
|
|
os.path.exists(args.output_dir)
|
|
and os.listdir(args.output_dir)
|
|
and args.do_train
|
|
):
|
|
if 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
|
|
)
|
|
)
|
|
else:
|
|
if args.local_rank in [-1, 0]:
|
|
shutil.rmtree(args.output_dir)
|
|
|
|
if not os.path.exists(args.output_dir) and (args.do_eval or args.do_predict):
|
|
raise ValueError(
|
|
"Output directory ({}) does not exist. Please train and save the model before inference stage.".format(
|
|
args.output_dir
|
|
)
|
|
)
|
|
|
|
if (
|
|
not os.path.exists(args.output_dir)
|
|
and args.do_train
|
|
and args.local_rank in [-1, 0]
|
|
):
|
|
os.makedirs(args.output_dir)
|
|
|
|
# Setup distant debugging if needed
|
|
if args.server_ip and args.server_port:
|
|
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
|
|
import ptvsd
|
|
|
|
print("Waiting for debugger attach")
|
|
ptvsd.enable_attach(
|
|
address=(args.server_ip, args.server_port), redirect_output=True
|
|
)
|
|
ptvsd.wait_for_attach()
|
|
|
|
# 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 = 0 if args.no_cuda else torch.cuda.device_count()
|
|
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(
|
|
filename=os.path.join(args.output_dir, "train.log")
|
|
if args.local_rank in [-1, 0]
|
|
else None,
|
|
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)
|
|
|
|
labels = get_labels(args.labels)
|
|
num_labels = len(labels)
|
|
# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
|
|
pad_token_label_id = CrossEntropyLoss().ignore_index
|
|
|
|
# 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
|
|
|
|
args.model_type = args.model_type.lower()
|
|
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
|
|
config = config_class.from_pretrained(
|
|
args.config_name if args.config_name else args.model_name_or_path,
|
|
num_labels=num_labels,
|
|
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
)
|
|
tokenizer = tokenizer_class.from_pretrained(
|
|
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
|
|
do_lower_case=args.do_lower_case,
|
|
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
)
|
|
model = model_class.from_pretrained(
|
|
args.model_name_or_path,
|
|
from_tf=bool(".ckpt" in args.model_name_or_path),
|
|
config=config,
|
|
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
)
|
|
|
|
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)
|
|
|
|
# Training
|
|
if args.do_train:
|
|
train_dataset = FunsdDataset(
|
|
args, tokenizer, labels, pad_token_label_id, mode="train"
|
|
)
|
|
global_step, tr_loss = train(
|
|
args, train_dataset, model, tokenizer, labels, pad_token_label_id
|
|
)
|
|
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
|
|
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
|
|
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()`
|
|
model_to_save = (
|
|
model.module if hasattr(model, "module") else model
|
|
) # Take care of distributed/parallel training
|
|
model_to_save.save_pretrained(args.output_dir)
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
|
|
# Good practice: save your training arguments together with the trained model
|
|
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
|
|
|
|
# Evaluation
|
|
results = {}
|
|
if args.do_eval and args.local_rank in [-1, 0]:
|
|
tokenizer = tokenizer_class.from_pretrained(
|
|
args.output_dir, do_lower_case=args.do_lower_case
|
|
)
|
|
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("pytorch_transformers.modeling_utils").setLevel(
|
|
logging.WARN
|
|
) # Reduce logging
|
|
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
|
for checkpoint in checkpoints:
|
|
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
|
model = model_class.from_pretrained(checkpoint)
|
|
model.to(args.device)
|
|
result, _ = evaluate(
|
|
args,
|
|
model,
|
|
tokenizer,
|
|
labels,
|
|
pad_token_label_id,
|
|
mode="test",
|
|
prefix=global_step,
|
|
)
|
|
if global_step:
|
|
result = {"{}_{}".format(global_step, k): v for k, v in result.items()}
|
|
results.update(result)
|
|
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
|
|
with open(output_eval_file, "w") as writer:
|
|
for key in sorted(results.keys()):
|
|
writer.write("{} = {}\n".format(key, str(results[key])))
|
|
|
|
if args.do_predict and args.local_rank in [-1, 0]:
|
|
tokenizer = tokenizer_class.from_pretrained(
|
|
args.model_name_or_path, do_lower_case=args.do_lower_case
|
|
)
|
|
model = model_class.from_pretrained(args.output_dir)
|
|
model.to(args.device)
|
|
result, predictions = evaluate(
|
|
args, model, tokenizer, labels, pad_token_label_id, mode="test"
|
|
)
|
|
# Save results
|
|
output_test_results_file = os.path.join(args.output_dir, "test_results.txt")
|
|
with open(output_test_results_file, "w") as writer:
|
|
for key in sorted(result.keys()):
|
|
writer.write("{} = {}\n".format(key, str(result[key])))
|
|
# Save predictions
|
|
output_test_predictions_file = os.path.join(
|
|
args.output_dir, "test_predictions.txt"
|
|
)
|
|
with open(output_test_predictions_file, "w", encoding="utf8") as writer:
|
|
with open(
|
|
os.path.join(args.data_dir, "test.txt"), "r", encoding="utf8"
|
|
) as f:
|
|
example_id = 0
|
|
for line in f:
|
|
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
|
|
writer.write(line)
|
|
if not predictions[example_id]:
|
|
example_id += 1
|
|
elif predictions[example_id]:
|
|
output_line = (
|
|
line.split()[0]
|
|
+ " "
|
|
+ predictions[example_id].pop(0)
|
|
+ "\n"
|
|
)
|
|
writer.write(output_line)
|
|
else:
|
|
logger.warning(
|
|
"Maximum sequence length exceeded: No prediction for '%s'.",
|
|
line.split()[0],
|
|
)
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|