562 lines
24 KiB
Python
562 lines
24 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
|
|
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""BERT finetuning runner."""
|
|
|
|
import argparse
|
|
import csv
|
|
import logging
|
|
import os
|
|
import random
|
|
import sys
|
|
from io import open
|
|
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
|
|
TensorDataset)
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
from tqdm import tqdm, trange
|
|
|
|
from file_utils import PYTORCH_PRETRAINED_BERT_CACHE
|
|
from modeling import BertForMultipleChoice, BertConfig, WEIGHTS_NAME, CONFIG_NAME
|
|
from optimization import BertAdam, warmup_linear
|
|
from tokenization import BertTokenizer
|
|
|
|
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
|
|
datefmt = '%m/%d/%Y %H:%M:%S',
|
|
level = logging.INFO)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class SwagExample(object):
|
|
"""A single training/test example for the SWAG dataset."""
|
|
def __init__(self,
|
|
swag_id,
|
|
context_sentence,
|
|
start_ending,
|
|
ending_0,
|
|
ending_1,
|
|
ending_2,
|
|
ending_3,
|
|
label = None):
|
|
self.swag_id = swag_id
|
|
self.context_sentence = context_sentence
|
|
self.start_ending = start_ending
|
|
self.endings = [
|
|
ending_0,
|
|
ending_1,
|
|
ending_2,
|
|
ending_3,
|
|
]
|
|
self.label = label
|
|
|
|
def __str__(self):
|
|
return self.__repr__()
|
|
|
|
def __repr__(self):
|
|
l = [
|
|
"swag_id: {}".format(self.swag_id),
|
|
"context_sentence: {}".format(self.context_sentence),
|
|
"start_ending: {}".format(self.start_ending),
|
|
"ending_0: {}".format(self.endings[0]),
|
|
"ending_1: {}".format(self.endings[1]),
|
|
"ending_2: {}".format(self.endings[2]),
|
|
"ending_3: {}".format(self.endings[3]),
|
|
]
|
|
|
|
if self.label is not None:
|
|
l.append("label: {}".format(self.label))
|
|
|
|
return ", ".join(l)
|
|
|
|
|
|
class InputFeatures(object):
|
|
def __init__(self,
|
|
example_id,
|
|
choices_features,
|
|
label
|
|
|
|
):
|
|
self.example_id = example_id
|
|
self.choices_features = [
|
|
{
|
|
'input_ids': input_ids,
|
|
'input_mask': input_mask,
|
|
'segment_ids': segment_ids
|
|
}
|
|
for _, input_ids, input_mask, segment_ids in choices_features
|
|
]
|
|
self.label = label
|
|
|
|
|
|
def read_swag_examples(input_file, is_training):
|
|
with open(input_file, 'r', encoding='utf-8') as f:
|
|
reader = csv.reader(f)
|
|
lines = []
|
|
for line in reader:
|
|
if sys.version_info[0] == 2:
|
|
line = list(unicode(cell, 'utf-8') for cell in line)
|
|
lines.append(line)
|
|
|
|
if is_training and lines[0][-1] != 'label':
|
|
raise ValueError(
|
|
"For training, the input file must contain a label column."
|
|
)
|
|
|
|
examples = [
|
|
SwagExample(
|
|
swag_id = line[2],
|
|
context_sentence = line[4],
|
|
start_ending = line[5], # in the swag dataset, the
|
|
# common beginning of each
|
|
# choice is stored in "sent2".
|
|
ending_0 = line[7],
|
|
ending_1 = line[8],
|
|
ending_2 = line[9],
|
|
ending_3 = line[10],
|
|
label = int(line[11]) if is_training else None
|
|
) for line in lines[1:] # we skip the line with the column names
|
|
]
|
|
|
|
return examples
|
|
|
|
def convert_examples_to_features(examples, tokenizer, max_seq_length,
|
|
is_training):
|
|
"""Loads a data file into a list of `InputBatch`s."""
|
|
|
|
# Swag is a multiple choice task. To perform this task using Bert,
|
|
# we will use the formatting proposed in "Improving Language
|
|
# Understanding by Generative Pre-Training" and suggested by
|
|
# @jacobdevlin-google in this issue
|
|
# https://github.com/google-research/bert/issues/38.
|
|
#
|
|
# Each choice will correspond to a sample on which we run the
|
|
# inference. For a given Swag example, we will create the 4
|
|
# following inputs:
|
|
# - [CLS] context [SEP] choice_1 [SEP]
|
|
# - [CLS] context [SEP] choice_2 [SEP]
|
|
# - [CLS] context [SEP] choice_3 [SEP]
|
|
# - [CLS] context [SEP] choice_4 [SEP]
|
|
# The model will output a single value for each input. To get the
|
|
# final decision of the model, we will run a softmax over these 4
|
|
# outputs.
|
|
features = []
|
|
for example_index, example in enumerate(examples):
|
|
context_tokens = tokenizer.tokenize(example.context_sentence)
|
|
start_ending_tokens = tokenizer.tokenize(example.start_ending)
|
|
|
|
choices_features = []
|
|
for ending_index, ending in enumerate(example.endings):
|
|
# We create a copy of the context tokens in order to be
|
|
# able to shrink it according to ending_tokens
|
|
context_tokens_choice = context_tokens[:]
|
|
ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
|
|
# Modifies `context_tokens_choice` and `ending_tokens` in
|
|
# place so that the total length is less than the
|
|
# specified length. Account for [CLS], [SEP], [SEP] with
|
|
# "- 3"
|
|
_truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
|
|
|
|
tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
|
|
segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
|
|
|
|
input_ids = tokenizer.convert_tokens_to_ids(tokens)
|
|
input_mask = [1] * len(input_ids)
|
|
|
|
# Zero-pad up to the sequence length.
|
|
padding = [0] * (max_seq_length - len(input_ids))
|
|
input_ids += padding
|
|
input_mask += padding
|
|
segment_ids += padding
|
|
|
|
assert len(input_ids) == max_seq_length
|
|
assert len(input_mask) == max_seq_length
|
|
assert len(segment_ids) == max_seq_length
|
|
|
|
choices_features.append((tokens, input_ids, input_mask, segment_ids))
|
|
|
|
label = example.label
|
|
if example_index < 5:
|
|
logger.info("*** Example ***")
|
|
logger.info("swag_id: {}".format(example.swag_id))
|
|
for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
|
|
logger.info("choice: {}".format(choice_idx))
|
|
logger.info("tokens: {}".format(' '.join(tokens)))
|
|
logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
|
|
logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
|
|
logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
|
|
if is_training:
|
|
logger.info("label: {}".format(label))
|
|
|
|
features.append(
|
|
InputFeatures(
|
|
example_id = example.swag_id,
|
|
choices_features = choices_features,
|
|
label = label
|
|
)
|
|
)
|
|
|
|
return features
|
|
|
|
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
|
|
"""Truncates a sequence pair in place to the maximum length."""
|
|
|
|
# This is a simple heuristic which will always truncate the longer sequence
|
|
# one token at a time. This makes more sense than truncating an equal percent
|
|
# of tokens from each, since if one sequence is very short then each token
|
|
# that's truncated likely contains more information than a longer sequence.
|
|
while True:
|
|
total_length = len(tokens_a) + len(tokens_b)
|
|
if total_length <= max_length:
|
|
break
|
|
if len(tokens_a) > len(tokens_b):
|
|
tokens_a.pop()
|
|
else:
|
|
tokens_b.pop()
|
|
|
|
def accuracy(out, labels):
|
|
outputs = np.argmax(out, axis=1)
|
|
return np.sum(outputs == labels)
|
|
|
|
def select_field(features, field):
|
|
return [
|
|
[
|
|
choice[field]
|
|
for choice in feature.choices_features
|
|
]
|
|
for feature in features
|
|
]
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
## Required parameters
|
|
parser.add_argument("--data_dir",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The input data dir. Should contain the .csv files (or other data files) for the task.")
|
|
parser.add_argument("--bert_model", default=None, type=str, required=True,
|
|
help="Bert pre-trained model selected in the list: bert-base-uncased, "
|
|
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
|
|
"bert-base-multilingual-cased, bert-base-chinese.")
|
|
parser.add_argument("--output_dir",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The output directory where the model checkpoints will be written.")
|
|
parser.add_argument("--init_checkpoint",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The checkpoint file from pretraining")
|
|
|
|
## Other parameters
|
|
parser.add_argument("--max_seq_length",
|
|
default=128,
|
|
type=int,
|
|
help="The maximum total input sequence length after WordPiece tokenization. \n"
|
|
"Sequences longer than this will be truncated, and sequences shorter \n"
|
|
"than this will be padded.")
|
|
parser.add_argument("--do_train",
|
|
action='store_true',
|
|
help="Whether to run training.")
|
|
parser.add_argument("--do_eval",
|
|
action='store_true',
|
|
help="Whether to run eval on the dev set.")
|
|
parser.add_argument("--do_lower_case",
|
|
action='store_true',
|
|
help="Set this flag if you are using an uncased model.")
|
|
parser.add_argument("--train_batch_size",
|
|
default=32,
|
|
type=int,
|
|
help="Total batch size for training.")
|
|
parser.add_argument("--eval_batch_size",
|
|
default=8,
|
|
type=int,
|
|
help="Total batch size for eval.")
|
|
parser.add_argument("--learning_rate",
|
|
default=5e-5,
|
|
type=float,
|
|
help="The initial learning rate for Adam.")
|
|
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=-1.0, type=float,
|
|
help="Total number of training steps to perform.")
|
|
parser.add_argument("--warmup_proportion",
|
|
default=0.1,
|
|
type=float,
|
|
help="Proportion of training to perform linear learning rate warmup for. "
|
|
"E.g., 0.1 = 10%% of training.")
|
|
parser.add_argument("--no_cuda",
|
|
action='store_true',
|
|
help="Whether not to use CUDA when available")
|
|
parser.add_argument("--local_rank",
|
|
type=int,
|
|
default=-1,
|
|
help="local_rank for distributed training on gpus")
|
|
parser.add_argument('--seed',
|
|
type=int,
|
|
default=42,
|
|
help="random seed for initialization")
|
|
parser.add_argument('--gradient_accumulation_steps',
|
|
type=int,
|
|
default=1,
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.")
|
|
parser.add_argument('--fp16',
|
|
action='store_true',
|
|
help="Whether to use 16-bit float precision instead of 32-bit")
|
|
parser.add_argument('--loss_scale',
|
|
type=float, default=0,
|
|
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
|
|
"0 (default value): dynamic loss scaling.\n"
|
|
"Positive power of 2: static loss scaling value.\n")
|
|
|
|
args = parser.parse_args()
|
|
|
|
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")
|
|
n_gpu = torch.cuda.device_count()
|
|
else:
|
|
torch.cuda.set_device(args.local_rank)
|
|
device = torch.device("cuda", args.local_rank)
|
|
n_gpu = 1
|
|
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
|
torch.distributed.init_process_group(backend='nccl')
|
|
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
|
|
device, n_gpu, bool(args.local_rank != -1), args.fp16))
|
|
|
|
if args.gradient_accumulation_steps < 1:
|
|
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
|
|
args.gradient_accumulation_steps))
|
|
|
|
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
|
|
|
|
random.seed(args.seed)
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
if n_gpu > 0:
|
|
torch.cuda.manual_seed_all(args.seed)
|
|
|
|
if not args.do_train and not args.do_eval:
|
|
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
|
|
|
|
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
|
|
print("WARNING: Output directory ({}) already exists and is not empty.".format(args.output_dir))
|
|
if not os.path.exists(args.output_dir):
|
|
os.makedirs(args.output_dir)
|
|
|
|
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
|
|
|
|
train_examples = None
|
|
num_train_optimization_steps = None
|
|
if args.do_train:
|
|
train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
|
|
num_train_optimization_steps = int(
|
|
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
|
|
if args.local_rank != -1:
|
|
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
|
|
|
|
# Prepare model
|
|
model = BertForMultipleChoice.from_pretrained(args.bert_model,
|
|
cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank)),
|
|
num_choices=4)
|
|
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'), strict=False)
|
|
|
|
if args.fp16:
|
|
model.half()
|
|
model.to(device)
|
|
if args.local_rank != -1:
|
|
try:
|
|
from apex.parallel import DistributedDataParallel as DDP
|
|
except ImportError:
|
|
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
|
|
|
|
model = DDP(model)
|
|
elif n_gpu > 1:
|
|
model = torch.nn.DataParallel(model)
|
|
|
|
# Prepare optimizer
|
|
param_optimizer = list(model.named_parameters())
|
|
|
|
# hack to remove pooler, which is not used
|
|
# thus it produce None grad that break apex
|
|
param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
|
|
|
|
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
|
|
optimizer_grouped_parameters = [
|
|
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
|
|
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
|
|
]
|
|
if args.fp16:
|
|
try:
|
|
from apex.optimizers import FP16_Optimizer
|
|
from apex.optimizers import FusedAdam
|
|
except ImportError:
|
|
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
|
|
|
|
optimizer = FusedAdam(optimizer_grouped_parameters,
|
|
lr=args.learning_rate,
|
|
bias_correction=False,
|
|
max_grad_norm=1.0)
|
|
if args.loss_scale == 0:
|
|
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
|
|
else:
|
|
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
|
|
else:
|
|
optimizer = BertAdam(optimizer_grouped_parameters,
|
|
lr=args.learning_rate,
|
|
warmup=args.warmup_proportion,
|
|
t_total=num_train_optimization_steps)
|
|
|
|
global_step = 0
|
|
if args.do_train:
|
|
train_features = convert_examples_to_features(
|
|
train_examples, tokenizer, args.max_seq_length, True)
|
|
logger.info("***** Running training *****")
|
|
logger.info(" Num examples = %d", len(train_examples))
|
|
logger.info(" Batch size = %d", args.train_batch_size)
|
|
logger.info(" Num steps = %d", num_train_optimization_steps)
|
|
all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
|
|
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
|
|
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
|
|
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
|
|
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
|
|
if args.local_rank == -1:
|
|
train_sampler = RandomSampler(train_data)
|
|
else:
|
|
train_sampler = DistributedSampler(train_data)
|
|
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
|
|
|
|
model.train()
|
|
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
|
|
tr_loss = 0
|
|
nb_tr_examples, nb_tr_steps = 0, 0
|
|
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
|
|
# Terminate early for benchmarking
|
|
if args.max_steps > 0 and global_step > args.max_steps:
|
|
break
|
|
|
|
batch = tuple(t.to(device) for t in batch)
|
|
input_ids, input_mask, segment_ids, label_ids = batch
|
|
loss = model(input_ids, segment_ids, input_mask, label_ids)
|
|
if n_gpu > 1:
|
|
loss = loss.mean() # mean() to average on multi-gpu.
|
|
if args.fp16 and args.loss_scale != 1.0:
|
|
# rescale loss for fp16 training
|
|
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
|
|
loss = loss * args.loss_scale
|
|
if args.gradient_accumulation_steps > 1:
|
|
loss = loss / args.gradient_accumulation_steps
|
|
tr_loss += loss.item()
|
|
nb_tr_examples += input_ids.size(0)
|
|
nb_tr_steps += 1
|
|
|
|
if args.fp16:
|
|
optimizer.backward(loss)
|
|
else:
|
|
loss.backward()
|
|
if (step + 1) % args.gradient_accumulation_steps == 0:
|
|
if args.fp16:
|
|
# modify learning rate with special warm up BERT uses
|
|
# if args.fp16 is False, BertAdam is used that handles this automatically
|
|
lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
|
|
for param_group in optimizer.param_groups:
|
|
param_group['lr'] = lr_this_step
|
|
optimizer.step()
|
|
optimizer.zero_grad()
|
|
global_step += 1
|
|
|
|
|
|
if args.do_train:
|
|
# Save a trained model and the associated configuration
|
|
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
|
|
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
|
|
torch.save(model_to_save.state_dict(), output_model_file)
|
|
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
|
|
with open(output_config_file, 'w') as f:
|
|
f.write(model_to_save.config.to_json_string())
|
|
|
|
# Load a trained model and config that you have fine-tuned
|
|
config = BertConfig(output_config_file)
|
|
model = BertForMultipleChoice(config, num_choices=4)
|
|
model.load_state_dict(torch.load(output_model_file))
|
|
else:
|
|
model = BertForMultipleChoice.from_pretrained(args.bert_model, num_choices=4)
|
|
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'), strict=False)
|
|
model.to(device)
|
|
|
|
|
|
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
|
eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
|
|
eval_features = convert_examples_to_features(
|
|
eval_examples, tokenizer, args.max_seq_length, True)
|
|
logger.info("***** Running evaluation *****")
|
|
logger.info(" Num examples = %d", len(eval_examples))
|
|
logger.info(" Batch size = %d", args.eval_batch_size)
|
|
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
|
|
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
|
|
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
|
|
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
|
|
# Run prediction for full data
|
|
eval_sampler = SequentialSampler(eval_data)
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
|
|
|
model.eval()
|
|
eval_loss, eval_accuracy = 0, 0
|
|
nb_eval_steps, nb_eval_examples = 0, 0
|
|
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
|
|
input_ids = input_ids.to(device)
|
|
input_mask = input_mask.to(device)
|
|
segment_ids = segment_ids.to(device)
|
|
label_ids = label_ids.to(device)
|
|
|
|
with torch.no_grad():
|
|
tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
|
|
logits = model(input_ids, segment_ids, input_mask)
|
|
|
|
logits = logits.detach().cpu().numpy()
|
|
label_ids = label_ids.to('cpu').numpy()
|
|
tmp_eval_accuracy = accuracy(logits, label_ids)
|
|
|
|
eval_loss += tmp_eval_loss.mean().item()
|
|
eval_accuracy += tmp_eval_accuracy
|
|
|
|
nb_eval_examples += input_ids.size(0)
|
|
nb_eval_steps += 1
|
|
|
|
eval_loss = eval_loss / nb_eval_steps
|
|
eval_accuracy = eval_accuracy / nb_eval_examples
|
|
|
|
result = {'eval_loss': eval_loss,
|
|
'eval_accuracy': eval_accuracy,
|
|
'global_step': global_step,
|
|
'loss': tr_loss/nb_tr_steps}
|
|
|
|
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
|
|
with open(output_eval_file, "w") as writer:
|
|
logger.info("***** Eval results *****")
|
|
for key in sorted(result.keys()):
|
|
logger.info(" %s = %s", key, str(result[key]))
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|