mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-18 20:18:24 +06:00
3bf3f9596f
Reason for issue was that optimzation steps where computed from example size, which is different from actual size of dataloader when an example is chunked into multiple instances. Solution in this pull request is to compute num_optimization_steps directly from len(data_loader).
555 lines
24 KiB
Python
555 lines
24 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace 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."""
|
|
|
|
from __future__ import absolute_import
|
|
|
|
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 pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
|
|
from pytorch_pretrained_bert.modeling import BertForMultipleChoice, BertConfig
|
|
from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
|
|
from pytorch_pretrained_bert.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.")
|
|
|
|
## 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("--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):
|
|
raise ValueError("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)
|
|
train_features = convert_examples_to_features(
|
|
train_examples, tokenizer, args.max_seq_length, True)
|
|
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)
|
|
|
|
num_train_optimization_steps = len(train_dataloader) / 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(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)),
|
|
num_choices=4)
|
|
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
|
|
if args.do_train:
|
|
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)
|
|
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
|
|
t_total=num_train_optimization_steps)
|
|
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:
|
|
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)
|
|
|
|
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")):
|
|
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.get_lr(global_step, 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, configuration and tokenizer
|
|
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
|
|
|
|
# If we save using the predefined names, we can load using `from_pretrained`
|
|
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
|
|
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
|
|
|
|
torch.save(model_to_save.state_dict(), output_model_file)
|
|
model_to_save.config.to_json_file(output_config_file)
|
|
tokenizer.save_vocabulary(args.output_dir)
|
|
|
|
# Load a trained model and vocabulary that you have fine-tuned
|
|
model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4)
|
|
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
|
else:
|
|
model = BertForMultipleChoice.from_pretrained(args.bert_model, num_choices=4)
|
|
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/global_step}
|
|
|
|
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()
|