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PaddleHub
提交 44aa7b51 编写于 作者: Z Zeyu Chen
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remove useless file and organize bert reader

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README.md
浏览文件 @ 44aa7b51
# PaddleHub
[![Build Status](https://travis-ci.org/PaddlePaddle/PaddleHub.svg?branch=master)](https://travis-ci.org/PaddlePaddle/PaddleHub)
[![Build Status](https://travis-ci.org/PaddlePaddle/PaddleHub.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/PaddleHub)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
demo/bert-cls/finetune_with_hub.py
浏览文件 @ 44aa7b51
......@@ -26,56 +26,42 @@ import paddle
import paddle.fluid as fluid
import paddle_hub as hub
import reader.cls as reader
import reader.task_reader as task_reader
from utils.args import ArgumentGroup, print_arguments
from paddle_hub.finetune.config import FinetuneConfig
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
train_g = ArgumentGroup(parser, "training", "training options.")
train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.")
train_g.add_arg("hub_module_dir", str, None, "PaddleHub module directory")
train_g.add_arg("lr_scheduler", str, "linear_warmup_decay", "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
train_g.add_arg("warmup_proportion", float, 0.1,
"Proportion of training steps to perform linear learning rate warmup for.")
data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
data_g.add_arg("data_dir", str, None, "Path to training data.")
data_g.add_arg("checkpoint_dir", str, None, "Directory to model checkpoint")
data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.")
data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
parser.add_argument("--lr_scheduler", type=str, default="linear_warmup_decay",
help="scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
args = parser.parse_args()
# yapf: enable.
if __name__ == '__main__':
print_arguments(args)
config = FinetuneConfig(
config = hub.FinetuneConfig(
log_interval=10,
eval_interval=100,
save_ckpt_interval=50,
use_cuda=True,
save_ckpt_interval=200,
checkpoint_dir=args.checkpoint_dir,
learning_rate=args.learning_rate,
num_epoch=args.epoch,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
max_seq_len=args.max_seq_len,
weight_decay=args.weight_decay,
finetune_strategy="bert_finetune",
enable_memory_optim=True,
optimizer=None,
warmup_proportion=args.warmup_proportion)
finetune_strategy="bert_finetune")
# loading Paddlehub BERT
module = hub.Module(module_dir=args.hub_module_dir)
reader = reader.BERTClassifyReader(
data_dir=args.data_dir,
# Use BERTTokenizeReader to tokenize the dataset according to model's
# vocabulary
reader = hub.reader.BERTTokenizeReader(
dataset=hub.dataset.ChnSentiCorp(), # download chnsenticorp dataset
vocab_path=module.get_vocab_path(),
max_seq_len=args.max_seq_len)
......
demo/bert-cls/reader/squad.py 已删除 100644 → 0
浏览文件 @ 4b35d202
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Run BERT on SQuAD 1.1 and SQuAD 2.0."""
import six
import math
import json
import random
import collections
import tokenization
from batching import prepare_batch_data
class SquadExample(object):
"""A single training/test example for simple sequence classification.
For examples without an answer, the start and end position are -1.
"""
def __init__(self,
qas_id,
question_text,
doc_tokens,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=False):
self.qas_id = qas_id
self.question_text = question_text
self.doc_tokens = doc_tokens
self.orig_answer_text = orig_answer_text
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ""
s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (tokenization.printable_text(
self.question_text))
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tokens,
token_to_orig_map,
token_is_max_context,
input_ids,
input_mask,
segment_ids,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.token_is_max_context = token_is_max_context
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def read_squad_examples(input_file, is_training, version_2_with_negative=False):
"""Read a SQuAD json file into a list of SquadExample."""
with open(input_file, "r") as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
for c in paragraph_text:
if is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
end_position = None
orig_answer_text = None
is_impossible = False
if is_training:
if version_2_with_negative:
is_impossible = qa["is_impossible"]
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer."
)
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
answer_offset = answer["answer_start"]
answer_length = len(orig_answer_text)
start_position = char_to_word_offset[answer_offset]
end_position = char_to_word_offset[answer_offset +
answer_length - 1]
# Only add answers where the text can be exactly recovered from the
# document. If this CAN'T happen it's likely due to weird Unicode
# stuff so we will just skip the example.
#
# Note that this means for training mode, every example is NOT
# guaranteed to be preserved.
actual_text = " ".join(
doc_tokens[start_position:(end_position + 1)])
cleaned_answer_text = " ".join(
tokenization.whitespace_tokenize(orig_answer_text))
if actual_text.find(cleaned_answer_text) == -1:
print("Could not find answer: '%s' vs. '%s'",
actual_text, cleaned_answer_text)
continue
else:
start_position = -1
end_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
doc_tokens=doc_tokens,
orig_answer_text=orig_answer_text,
start_position=start_position,
end_position=end_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def convert_examples_to_features(
examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
#output_fn
):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position +
1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(
tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(
doc_spans, doc_span_index, split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
#while len(input_ids) < max_seq_length:
# input_ids.append(0)
# input_mask.append(0)
# segment_ids.append(0)
#assert len(input_ids) == max_seq_length
#assert len(input_mask) == max_seq_length
#assert len(segment_ids) == max_seq_length
start_position = None
end_position = None
if is_training and not example.is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start
and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = 0
end_position = 0
else:
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and example.is_impossible:
start_position = 0
end_position = 0
if example_index < 3:
print("*** Example ***")
print("unique_id: %s" % (unique_id))
print("example_index: %s" % (example_index))
print("doc_span_index: %s" % (doc_span_index))
print("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
print("token_to_orig_map: %s" % " ".join([
"%d:%d" % (x, y)
for (x, y) in six.iteritems(token_to_orig_map)
]))
print("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y)
for (x, y) in six.iteritems(token_is_max_context)
]))
print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
print("input_mask: %s" % " ".join([str(x) for x in input_mask]))
print(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training and example.is_impossible:
print("impossible example")
if is_training and not example.is_impossible:
answer_text = " ".join(
tokens[start_position:(end_position + 1)])
print("start_position: %d" % (start_position))
print("end_position: %d" % (end_position))
print("answer: %s" %
(tokenization.printable_text(answer_text)))
feature = InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
start_position=start_position,
end_position=end_position,
is_impossible=example.is_impossible)
unique_id += 1
yield feature
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can
# often find a "better match". For example:
#
# Question: What year was John Smith born?
# Context: The leader was John Smith (1895-1943).
# Answer: 1895
#
# The original whitespace-tokenized answer will be "(1895-1943).". However
# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
# the exact answer, 1895.
#
# However, this is not always possible. Consider the following:
#
# Question: What country is the top exporter of electornics?
# Context: The Japanese electronics industry is the lagest in the world.
# Answer: Japan
#
# In this case, the annotator chose "Japan" as a character sub-span of
# the word "Japanese". Since our WordPiece tokenizer does not split
# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
# in SQuAD, but does happen.
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context,
num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
class DataProcessor(object):
def __init__(self, vocab_path, do_lower_case, max_seq_length, in_tokens,
doc_stride, max_query_length):
self._tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_path, do_lower_case=do_lower_case)
self._max_seq_length = max_seq_length
self._doc_stride = doc_stride
self._max_query_length = max_query_length
self._in_tokens = in_tokens
self.vocab = self._tokenizer.vocab
self.vocab_size = len(self.vocab)
self.pad_id = self.vocab["[PAD]"]
self.cls_id = self.vocab["[CLS]"]
self.sep_id = self.vocab["[SEP]"]
self.mask_id = self.vocab["[MASK]"]
self.current_train_example = -1
self.num_train_examples = -1
self.current_train_epoch = -1
self.train_examples = None
self.predict_examples = None
self.num_examples = {'train': -1, 'predict': -1}
def get_train_progress(self):
"""Gets progress for training phase."""
return self.current_train_example, self.current_train_epoch
def get_examples(self,
data_path,
is_training,
version_2_with_negative=False):
examples = read_squad_examples(
input_file=data_path,
is_training=is_training,
version_2_with_negative=version_2_with_negative)
return examples
def get_num_examples(self, phase):
if phase not in ['train', 'predict']:
raise ValueError(
"Unknown phase, which should be in ['train', 'predict'].")
return self.num_examples[phase]
def get_features(self, examples, is_training):
features = convert_examples_to_features(
examples=examples,
tokenizer=self._tokenizer,
max_seq_length=self._max_seq_length,
doc_stride=self._doc_stride,
max_query_length=self._max_query_length,
is_training=is_training)
return features
def data_generator(self,
data_path,
batch_size,
phase='train',
shuffle=False,
version_2_with_negative=False,
epoch=1):
if phase == 'train':
self.train_examples = self.get_examples(
data_path,
is_training=True,
version_2_with_negative=version_2_with_negative)
examples = self.train_examples
self.num_examples['train'] = len(self.train_examples)
elif phase == 'predict':
self.predict_examples = self.get_examples(
data_path,
is_training=False,
version_2_with_negative=version_2_with_negative)
examples = self.predict_examples
self.num_examples['predict'] = len(self.predict_examples)
else:
raise ValueError(
"Unknown phase, which should be in ['train', 'predict'].")
def batch_reader(features, batch_size, in_tokens):
batch, total_token_num, max_len = [], 0, 0
for (index, feature) in enumerate(features):
if phase == 'train':
self.current_train_example = index + 1
seq_len = len(feature.input_ids)
labels = [feature.unique_id
] if feature.start_position is None else [
feature.start_position, feature.end_position
]
example = [
feature.input_ids, feature.segment_ids,
range(seq_len)
] + labels
max_len = max(max_len, seq_len)
#max_len = max(max_len, len(token_ids))
if in_tokens:
to_append = (len(batch) + 1) * max_len <= batch_size
else:
to_append = len(batch) < batch_size
if to_append:
batch.append(example)
total_token_num += seq_len
else:
yield batch, total_token_num
batch, total_token_num, max_len = [example
], seq_len, seq_len
if len(batch) > 0:
yield batch, total_token_num
def wrapper():
for epoch_index in range(epoch):
if shuffle:
random.shuffle(examples)
if phase == 'train':
self.current_train_epoch = epoch_index
features = self.get_features(examples, is_training=True)
else:
features = self.get_features(examples, is_training=False)
for batch_data, total_token_num in batch_reader(
features, batch_size, self._in_tokens):
yield prepare_batch_data(
batch_data,
total_token_num,
voc_size=-1,
pad_id=self.pad_id,
cls_id=self.cls_id,
sep_id=self.sep_id,
mask_id=-1,
return_input_mask=True,
return_max_len=False,
return_num_token=False)
return wrapper
def write_predictions(all_examples, all_features, all_results, n_best_size,
max_answer_length, do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
version_2_with_negative, null_score_diff_threshold,
verbose):
"""Write final predictions to the json file and log-odds of null if needed."""
print("Writing predictions to: %s" % (output_prediction_file))
print("Writing nbest to: %s" % (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", [
"feature_index", "start_index", "end_index", "start_logit",
"end_logit"
])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[
0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(
pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(
orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case,
verbose)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't inlude the empty option in the n-best, inlcude it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(
text="",
start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
# debug
if best_non_null_entry is None:
print("Emmm..., sth wrong")
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (
best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
def get_final_text(pred_text, orig_text, do_lower_case, verbose):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heruistic between
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose:
print("Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose:
print("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose:
print("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose:
print("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(
enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
if __name__ == '__main__':
train_file = 'squad/train-v1.1.json'
vocab_file = 'uncased_L-12_H-768_A-12/vocab.txt'
do_lower_case = True
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
train_examples = read_squad_examples(
input_file=train_file, is_training=True)
print("begin converting")
for (index, feature) in enumerate(
convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=384,
doc_stride=128,
max_query_length=64,
is_training=True,
#output_fn=train_writer.process_feature
)):
if index < 10:
print(index, feature.input_ids, feature.input_mask,
feature.segment_ids)
#for (index, example) in enumerate(train_examples):
# if index < 5:
# print(example)
demo/bert-cls/run_fintune_with_hub.sh
浏览文件 @ 44aa7b51
......@@ -8,12 +8,11 @@ HUB_MODULE_DIR="./hub_module/bert_chinese_L-12_H-768_A-12.hub_module"
CKPT_DIR="./ckpt"
#rm -rf $CKPT_DIR
python -u finetune_with_hub.py \
--batch_size 128 \
--batch_size 32 \
--hub_module_dir=$HUB_MODULE_DIR \
--data_dir ${DATA_PATH} \
--weight_decay 0.01 \
--checkpoint_dir $CKPT_DIR \
--warmup_proportion 0.0 \
--epoch 2 \
--max_seq_len 16 \
--num_epoch 3 \
--max_seq_len 128 \
--learning_rate 5e-5
demo/bert-cls/utils/args.py 已删除 100644 → 0
浏览文件 @ 4b35d202
# Copyright (c) 2019 PaddlePaddle Authors. 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.
"""Arguments for configuration."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import argparse
def str2bool(v):
# because argparse does not support to parse "true, False" as python
# boolean directly
return v.lower() in ("true", "t", "1")
class ArgumentGroup(object):
def __init__(self, parser, title, des):
self._group = parser.add_argument_group(title=title, description=des)
def add_arg(self, name, type, default, help, **kwargs):
type = str2bool if type == bool else type
self._group.add_argument(
"--" + name,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
demo/bert-cls/utils/fp16.py 已删除 100644 → 0
浏览文件 @ 4b35d202
# Copyright (c) 2019 PaddlePaddle Authors. 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
def cast_fp16_to_fp32(i, o, prog):
prog.global_block().append_op(
type="cast",
inputs={"X": i},
outputs={"Out": o},
attrs={
"in_dtype": fluid.core.VarDesc.VarType.FP16,
"out_dtype": fluid.core.VarDesc.VarType.FP32
})
def cast_fp32_to_fp16(i, o, prog):
prog.global_block().append_op(
type="cast",
inputs={"X": i},
outputs={"Out": o},
attrs={
"in_dtype": fluid.core.VarDesc.VarType.FP32,
"out_dtype": fluid.core.VarDesc.VarType.FP16
})
def copy_to_master_param(p, block):
v = block.vars.get(p.name, None)
if v is None:
raise ValueError("no param name %s found!" % p.name)
new_p = fluid.framework.Parameter(
block=block,
shape=v.shape,
dtype=fluid.core.VarDesc.VarType.FP32,
type=v.type,
lod_level=v.lod_level,
stop_gradient=p.stop_gradient,
trainable=p.trainable,
optimize_attr=p.optimize_attr,
regularizer=p.regularizer,
gradient_clip_attr=p.gradient_clip_attr,
error_clip=p.error_clip,
name=v.name + ".master")
return new_p
def create_master_params_grads(params_grads, main_prog, startup_prog,
loss_scaling):
master_params_grads = []
tmp_role = main_prog._current_role
OpRole = fluid.core.op_proto_and_checker_maker.OpRole
main_prog._current_role = OpRole.Backward
for p, g in params_grads:
# create master parameters
master_param = copy_to_master_param(p, main_prog.global_block())
startup_master_param = startup_prog.global_block()._clone_variable(
master_param)
startup_p = startup_prog.global_block().var(p.name)
cast_fp16_to_fp32(startup_p, startup_master_param, startup_prog)
# cast fp16 gradients to fp32 before apply gradients
if g.name.find("layer_norm") > -1:
if loss_scaling > 1:
scaled_g = g / float(loss_scaling)
else:
scaled_g = g
master_params_grads.append([p, scaled_g])
continue
master_grad = fluid.layers.cast(g, "float32")
if loss_scaling > 1:
master_grad = master_grad / float(loss_scaling)
master_params_grads.append([master_param, master_grad])
main_prog._current_role = tmp_role
return master_params_grads
def master_param_to_train_param(master_params_grads, params_grads, main_prog):
for idx, m_p_g in enumerate(master_params_grads):
train_p, _ = params_grads[idx]
if train_p.name.find("layer_norm") > -1:
continue
with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]):
cast_fp32_to_fp16(m_p_g[0], train_p, main_prog)
demo/bert-cls/utils/init.py 已删除 100644 → 0
浏览文件 @ 4b35d202
# Copyright (c) 2019 PaddlePaddle Authors. 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.
from __future__ import print_function
import os
import six
import ast
import copy
import numpy as np
import paddle.fluid as fluid
def cast_fp32_to_fp16(exe, main_program):
print("Cast parameters to float16 data format.")
for param in main_program.global_block().all_parameters():
if not param.name.endswith(".master"):
param_t = fluid.global_scope().find_var(param.name).get_tensor()
data = np.array(param_t)
if param.name.find("layer_norm") == -1:
param_t.set(np.float16(data).view(np.uint16), exe.place)
master_param_var = fluid.global_scope().find_var(param.name +
".master")
if master_param_var is not None:
master_param_var.get_tensor().set(data, exe.place)
def init_checkpoint(exe, init_checkpoint_path, main_program, use_fp16=False):
assert os.path.exists(
init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path
def existed_persitables(var):
if not fluid.io.is_persistable(var):
return False
return os.path.exists(os.path.join(init_checkpoint_path, var.name))
fluid.io.load_vars(
exe,
init_checkpoint_path,
main_program=main_program,
predicate=existed_persitables)
print("Load model from {}".format(init_checkpoint_path))
if use_fp16:
cast_fp32_to_fp16(exe, main_program)
def init_pretraining_params(exe,
pretraining_params_path,
main_program,
use_fp16=False):
assert os.path.exists(pretraining_params_path
), "[%s] cann't be found." % pretraining_params_path
def existed_params(var):
if not isinstance(var, fluid.framework.Parameter):
print("param {} not exsist!".format(var.name))
return False
return os.path.exists(os.path.join(pretraining_params_path, var.name))
fluid.io.load_vars(
exe,
pretraining_params_path,
main_program=main_program,
predicate=existed_params)
print(
"Load pretraining parameters from {}.".format(pretraining_params_path))
if use_fp16:
cast_fp32_to_fp16(exe, main_program)
paddle_hub/__init__.py
浏览文件 @ 44aa7b51
......@@ -14,6 +14,7 @@
from . import module
from . import common
from . import io
from . import dataset
from .common.dir import USER_HOME
from .common.dir import HUB_HOME
......@@ -34,3 +35,5 @@ from .finetune.network import append_mlp_classifier
from .finetune.finetune import finetune_and_eval
from .finetune.config import FinetuneConfig
from .finetune.task import Task
from .reader import BERTTokenizeReader
paddle_hub/common/dir.py
浏览文件 @ 44aa7b51
......@@ -14,6 +14,7 @@
import os
# TODO: Change dir.py's filename, this naming rule is not qualified
USER_HOME = os.path.expanduser('~')
HUB_HOME = os.path.join(USER_HOME, ".hub")
MODULE_HOME = os.path.join(HUB_HOME, "modules")
......
paddle_hub/common/downloader.py
浏览文件 @ 44aa7b51
......@@ -88,7 +88,7 @@ class Downloader:
done = int(50 * dl / total_length)
if time.time() - starttime >= FLUSH_INTERVAL:
sys.stdout.write(
"\r%s : [%-50s]%.2f%%" %
"\r%s : [%-50s] %.2f%%" %
(save_name, '=' * done,
float(dl / total_length * 100)))
starttime = time.time()
......
paddle_hub/common/logger.py
浏览文件 @ 44aa7b51
......@@ -19,8 +19,7 @@ import logging
import math
class Logger:
class Logger(object):
PLACEHOLDER = '%'
NOLOG = "NOLOG"
......@@ -29,7 +28,7 @@ class Logger:
format='[%(asctime)-15s] [%(levelname)8s] - %(message)s')
if not name:
name = "paddle-hub"
name = "PaddleHub"
self.logger = logging.getLogger(name)
self.logLevel = "DEBUG"
......
paddle_hub/dataset/__init__.py
浏览文件 @ 44aa7b51
# Copyright (c) 2019 PaddlePaddle Authors. 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.
from .dataset import InputExample, HubDataset
from .chnsenticorp import ChnSentiCorp
from .msra_ner import MSRA_NER
paddle_hub/dataset/chnsenticorp.py
浏览文件 @ 44aa7b51
......@@ -12,31 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_hub.tools.downloader import default_downloader
from paddle_hub.dir import DATA_HOME
from paddle_hub.common.downloader import default_downloader
from paddle_hub.common.dir import DATA_HOME
import os
import csv
from paddle_hub.dataset import InputExample
from paddle_hub.dataset import HubDataset
from collections import namedtuple
DATA_URL = "https://paddlehub-dataset.bj.bcebos.com/chnsenticorp_data.tar.gz"
class HubDataset(object):
def get_train_examples(self):
raise NotImplementedError()
def get_dev_examples(self):
raise NotImplementedError()
def get_test_examples(self):
raise NotImplementedError()
def get_val_examples(self):
return self.get_dev_examples()
class ChnSentiCorp(HubDataset):
"""
ChnSentiCorp (by Tan Songbo at ICT of Chinese Academy of Sciences, and for
opinion mining)
"""
def __init__(self):
ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
url=DATA_URL, save_path=DATA_HOME, print_progress=True)
......@@ -66,15 +60,20 @@ class ChnSentiCorp(HubDataset):
def get_test_examples(self):
return self.test_examples
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
Example = namedtuple('Example', ["label", "text_a"])
examples = []
seq_id = 0
for line in reader:
example = Example(*line)
example = InputExample(
guid=seq_id, label=line[0], text_a=line[1])
seq_id += 1
examples.append(example)
return examples
......@@ -82,5 +81,5 @@ class ChnSentiCorp(HubDataset):
if __name__ == "__main__":
ds = ChnSentiCorp()
for e in ds.get_train_example():
for e in ds.get_train_examples():
print(e)
paddle_hub/dataset/dataset.py 0 → 100644
浏览文件 @ 44aa7b51
# Copyright (c) 2019 PaddlePaddle Authors. 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.
class InputExample(object):
"""
Input data structure of BERT/ERNIE, can satisfy single sequence task like
text classification, sequence lableing; Sequence pair task like dialog
task.
"""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class HubDataset(object):
def get_train_examples(self):
raise NotImplementedError()
def get_dev_examples(self):
raise NotImplementedError()
def get_test_examples(self):
raise NotImplementedError()
def get_val_examples(self):
return self.get_dev_examples()
def get_labels(self):
raise NotImplementedError()
paddle_hub/dataset/msra_ner.py
浏览文件 @ 44aa7b51
......@@ -12,8 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_hub.tools.downloader import default_downloader
from paddle_hub.dir import DATA_HOME
from paddle_hub.common.downloader import default_downloader
from paddle_hub.common.dir import DATA_HOME
import os
import csv
......@@ -28,7 +28,6 @@ class MSRA_NER(object):
ret, tips, self.dataset_dir = default_downloader.download_file_and_uncompress(
url=DATA_URL, save_path=DATA_HOME, print_progress=True)
print(self.dataset_dir)
self._load_label_map()
self._load_train_examples()
......@@ -44,6 +43,10 @@ class MSRA_NER(object):
def get_train_examples(self):
return self.train_examples
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
......
paddle_hub/dataset/task_reader.py 已删除 100644 → 0
浏览文件 @ 4b35d202
# Copyright (c) 2019 PaddlePaddle Authors. 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.
import os
import csv
import json
import numpy as np
from collections import namedtuple
import tokenization
from batching import pad_batch_data
class BaseReader(object):
def __init__(self,
vocab_path,
label_map_config=None,
max_seq_len=512,
do_lower_case=True,
in_tokens=False,
random_seed=None):
self.max_seq_len = max_seq_len
self.tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_path, do_lower_case=do_lower_case)
self.vocab = self.tokenizer.vocab
self.pad_id = self.vocab["[PAD]"]
self.cls_id = self.vocab["[CLS]"]
self.sep_id = self.vocab["[SEP]"]
self.in_tokens = in_tokens
np.random.seed(random_seed)
self.current_example = 0
self.current_epoch = 0
self.num_examples = 0
if label_map_config:
with open(label_map_config) as f:
self.label_map = json.load(f)
else:
self.label_map = None
pass
def get_train_progress(self):
"""Gets progress for training phase."""
return self.current_example, self.current_epoch
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
headers = next(reader)
Example = namedtuple('Example', headers)
examples = []
for line in reader:
example = Example(*line)
examples.append(example)
return examples
def _truncate_seq_pair(self, 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 _convert_example_to_record(self, example, max_seq_length, tokenizer):
"""Converts a single `Example` into a single `Record`."""
text_a = tokenization.convert_to_unicode(example.text_a)
tokens_a = tokenizer.tokenize(text_a)
tokens_b = None
if "text_b" in example._fields:
text_b = tokenization.convert_to_unicode(example.text_b)
tokens_b = tokenizer.tokenize(text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# The convention in BERT/ERNIE is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
text_type_ids = []
tokens.append("[CLS]")
text_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
text_type_ids.append(0)
tokens.append("[SEP]")
text_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
text_type_ids.append(1)
tokens.append("[SEP]")
text_type_ids.append(1)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
if self.label_map:
label_id = self.label_map[example.label]
else:
label_id = example.label
Record = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_id', 'qid'])
qid = None
if "qid" in example._fields:
qid = example.qid
record = Record(
token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids,
label_id=label_id,
qid=qid)
return record
def _prepare_batch_data(self, examples, batch_size, phase=None):
"""generate batch records"""
batch_records, max_len = [], 0
for index, example in enumerate(examples):
if phase == "train":
self.current_example = index
record = self._convert_example_to_record(example, self.max_seq_len,
self.tokenizer)
max_len = max(max_len, len(record.token_ids))
if self.in_tokens:
to_append = (len(batch_records) + 1) * max_len <= batch_size
else:
to_append = len(batch_records) < batch_size
if to_append:
batch_records.append(record)
else:
yield self._pad_batch_records(batch_records)
batch_records, max_len = [record], len(record.token_ids)
if len(batch_records) > 0:
yield self._pad_batch_records(batch_records)
def get_num_examples(self, input_file):
examples = self._read_tsv(input_file)
return len(examples)
def data_generator(self,
input_file,
batch_size,
epoch,
shuffle=True,
phase=None):
examples = self._read_tsv(input_file)
def wrapper():
for epoch_index in range(epoch):
if phase == "train":
self.current_example = 0
self.current_epoch = epoch_index
if shuffle:
np.random.shuffle(examples)
for batch_data in self._prepare_batch_data(
examples, batch_size, phase=phase):
yield batch_data
return wrapper
class ClassifyReader(BaseReader):
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
headers = next(reader)
text_indices = [
index for index, h in enumerate(headers) if h != "label"
]
Example = namedtuple('Example', headers)
examples = []
for line in reader:
for index, text in enumerate(line):
if index in text_indices:
line[index] = text.replace(' ', '')
example = Example(*line)
examples.append(example)
return examples
def _pad_batch_records(self, batch_records):
batch_token_ids = [record.token_ids for record in batch_records]
batch_text_type_ids = [record.text_type_ids for record in batch_records]
batch_position_ids = [record.position_ids for record in batch_records]
batch_labels = [record.label_id for record in batch_records]
batch_labels = np.array(batch_labels).astype("int64").reshape([-1, 1])
if batch_records[0].qid:
batch_qids = [record.qid for record in batch_records]
batch_qids = np.array(batch_qids).astype("int64").reshape([-1, 1])
else:
batch_qids = np.array([]).astype("int64").reshape([-1, 1])
# padding
padded_token_ids, next_sent_index, self_attn_bias = pad_batch_data(
batch_token_ids,
pad_idx=self.pad_id,
return_next_sent_pos=True,
return_attn_bias=True)
padded_text_type_ids = pad_batch_data(
batch_text_type_ids, pad_idx=self.pad_id)
padded_position_ids = pad_batch_data(
batch_position_ids, pad_idx=self.pad_id)
return_list = [
padded_token_ids, padded_text_type_ids, padded_position_ids,
self_attn_bias, batch_labels, next_sent_index, batch_qids
]
return return_list
class SequenceLabelReader(BaseReader):
def _pad_batch_records(self, batch_records):
batch_token_ids = [record.token_ids for record in batch_records]
batch_text_type_ids = [record.text_type_ids for record in batch_records]
batch_position_ids = [record.position_ids for record in batch_records]
batch_label_ids = [record.label_ids for record in batch_records]
batch_seq_lens = [len(record.token_ids) for record in batch_records]
# padding
padded_token_ids, self_attn_bias = pad_batch_data(
batch_token_ids,
pad_idx=self.pad_id,
return_next_sent_pos=False,
return_attn_bias=True)
padded_text_type_ids = pad_batch_data(
batch_text_type_ids, pad_idx=self.pad_id)
padded_position_ids = pad_batch_data(
batch_position_ids, pad_idx=self.pad_id)
padded_label_ids = pad_batch_data(
batch_label_ids, pad_idx=len(self.label_map) - 1)
batch_seq_lens = np.array(batch_seq_lens).astype("int64").reshape(
[-1, 1])
return_list = [
padded_token_ids, padded_text_type_ids, padded_position_ids,
self_attn_bias, padded_label_ids, batch_seq_lens
]
return return_list
def _reseg_token_label(self, tokens, labels, tokenizer):
assert len(tokens) == len(labels)
ret_tokens = []
ret_labels = []
for token, label in zip(tokens, labels):
sub_token = tokenizer.tokenize(token)
if len(sub_token) == 0:
continue
ret_tokens.extend(sub_token)
ret_labels.append(label)
if len(sub_token) < 2:
continue
sub_label = label
if label.startswith("B-"):
sub_label = "I-" + label[2:]
ret_labels.extend([sub_label] * (len(sub_token) - 1))
assert len(ret_tokens) == len(ret_labels)
return ret_tokens, ret_labels
def _convert_example_to_record(self, example, max_seq_length, tokenizer):
tokens = tokenization.convert_to_unicode(example.text_a).split(u"")
labels = tokenization.convert_to_unicode(example.label).split(u"")
tokens, labels = self._reseg_token_label(tokens, labels, tokenizer)
if len(tokens) > max_seq_length - 2:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
tokens = ["[CLS]"] + tokens + ["[SEP]"]
token_ids = tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
text_type_ids = [0] * len(token_ids)
no_entity_id = len(self.label_map) - 1
label_ids = [no_entity_id
] + [self.label_map[label]
for label in labels] + [no_entity_id]
Record = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_ids'])
record = Record(
token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids,
label_ids=label_ids)
return record
if __name__ == '__main__':
pass
paddle_hub/dataset/tokenization.py 已删除 100644 → 0
浏览文件 @ 4b35d202
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Tokenization classes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
fin = open(vocab_file)
for num, line in enumerate(fin):
items = convert_to_unicode(line.strip()).split("\t")
if len(items) > 2:
break
token = items[0]
index = items[1] if len(items) == 2 else num
token = token.strip()
vocab[token] = int(index)
return vocab
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a peice of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class FullTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
class CharTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in text.lower().split(" "):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = convert_to_unicode(text)
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer.
Returns:
A list of wordpiece tokens.
"""
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64)
or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
paddle_hub/finetune/checkpoint.py
浏览文件 @ 44aa7b51
......@@ -36,14 +36,17 @@ def load_checkpoint(checkpoint_dir, exe):
fluid.io.load_persistables(exe, ckpt.latest_model_dir)
logger.info("Checkpoint loaded. current_epoch={},"
"global_step={}".format(current_epoch, global_step))
logger.info("PaddleHub model checkpoint loaded. current_epoch={}, "
"global_step={}".format(ckpt.current_epoch,
ckpt.global_step))
return ckpt.current_epoch, ckpt.global_step
else:
current_epoch = 1
global_step = 0
latest_model_dir = None
logger.info("Checkpoint not found, start training from scratch...")
logger.info(
"PaddleHub model checkpoint not found, start training from scratch..."
)
exe.run(fluid.default_startup_program())
return current_epoch, global_step
......
paddle_hub/finetune/finetune.py
浏览文件 @ 44aa7b51
......@@ -40,7 +40,6 @@ def _get_running_device_info(config):
def _do_memory_optimization(task, config):
if config.enable_memory_optim:
logger.info("Memory optimization start...")
task_var_name = task.metric_variable_names()
......@@ -56,7 +55,7 @@ def _do_memory_optimization(task, config):
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=fluid.default_main_program(), batch_size=config.batch_size)
logger.info("Theoretical memory usage in training: %.3f - %.3f %s" %
logger.info("Theoretical memory usage in training: %.2f - %.2f %s" %
(lower_mem, upper_mem, unit)),
......@@ -102,6 +101,7 @@ def _finetune_model(task, data_reader, feed_list, config=None, do_eval=False):
eval_loss_scalar = logw.scalar(tag="loss[evaluate]")
eval_acc_scalar = logw.scalar(tag="accuracy[evaluate]")
# Finetune loop
for epoch in range(current_epoch, num_epoch + 1):
train_reader = data_reader.data_generator(
batch_size=batch_size, phase='train')
......@@ -134,9 +134,6 @@ def _finetune_model(task, data_reader, feed_list, config=None, do_eval=False):
num_trained_examples = acc_sum = loss_sum = 0
if global_step % config.save_ckpt_interval == 0:
model_saved_dir = os.path.join(config.checkpoint_dir,
"step_%d" % global_step)
fluid.io.save_persistables(exe, dirname=model_saved_dir)
# NOTE: current saved checkpoint machanism is not completed,
# it can't restore dataset training status
save_checkpoint(
......@@ -163,9 +160,6 @@ def _finetune_model(task, data_reader, feed_list, config=None, do_eval=False):
(model_saved_dir, best_eval_acc))
fluid.io.save_persistables(exe, dirname=model_saved_dir)
# update model and checkpoint
model_saved_dir = os.path.join(config.checkpoint_dir, "final_model")
fluid.io.save_persistables(exe, dirname=model_saved_dir)
# NOTE: current saved checkpoint machanism is not completed, it can't
# resotre dataset training status
save_checkpoint(
......@@ -188,6 +182,7 @@ def finetune(task, data_reader, feed_list, config=None):
def evaluate(task, data_reader, feed_list, phase="test", config=None):
logger.info("Evaluation on {} dataset start".format(phase))
inference_program = task.inference_program()
main_program = task.main_program()
loss = task.variable("loss")
......@@ -216,7 +211,8 @@ def evaluate(task, data_reader, feed_list, phase="test", config=None):
avg_loss = loss_sum / num_eval_examples
avg_acc = acc_sum / num_eval_examples
eval_speed = eval_step / eval_time_used
logger.info("[evaluation on %s set] loss=%.5f acc=%.5f [step/sec: %.2f]" %
(phase, avg_loss, avg_acc, eval_speed))
logger.info(
"[%s dataset evaluation result] loss=%.5f acc=%.5f [step/sec: %.2f]" %
(phase, avg_loss, avg_acc, eval_speed))
return avg_loss, avg_acc, eval_speed
paddle_hub/module/module.py
浏览文件 @ 44aa7b51
......@@ -104,6 +104,7 @@ class Module(object):
self.module_info = None
self.processor = None
self.name = "temp"
# TODO(wuzewu): print more module loading info log
if url:
self._init_with_url(url=url)
elif module_dir:
......
paddle_hub/reader/__init__.py 0 → 100644
浏览文件 @ 44aa7b51
# Copyright (c) 2019 PaddlePaddle Authors. 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.
from .nlp_reader import BERTTokenizeReader
demo/bert-cls/reader/batching.py → paddle_hub/reader/batching.py
浏览文件 @ 44aa7b51
......@@ -188,7 +188,3 @@ def pad_batch_data(insts,
return_list += [num_token]
return return_list if len(return_list) > 1 else return_list[0]
if __name__ == "__main__":
pass
demo/bert-cls/reader/cls.py → paddle_hub/reader/nlp_reader.py
浏览文件 @ 44aa7b51
......@@ -16,52 +16,50 @@ import os
import types
import csv
import numpy as np
import tokenization
#from paddle_hub import dataset
from paddle_hub.reader import tokenization
from .batching import prepare_batch_data
class DataProcessor(object):
class BERTTokenizeReader(object):
"""Base class for data converters for sequence classification data sets."""
def __init__(self,
data_dir,
dataset,
vocab_path,
max_seq_len,
do_lower_case=True,
in_tokens=False,
random_seed=None):
self.data_dir = data_dir
self.dataset = dataset
self.max_seq_len = max_seq_len
self.tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_path, do_lower_case=do_lower_case)
self.vocab = self.tokenizer.vocab
self.in_tokens = in_tokens
np.random.seed(random_seed)
self.current_train_example = -1
self.num_examples = {'train': -1, 'dev': -1, 'test': -1}
self.current_train_epoch = -1
def get_train_examples(self, data_dir):
def get_train_examples(self):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
return self.dataset.get_train_examples()
def get_dev_examples(self, data_dir):
def get_dev_examples(self):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
return self.dataset.get_dev_examples()
def get_val_examples(self, data_dir):
def get_val_examples(self):
"""Gets a collection of `InputExample`s for the val set."""
raise NotImplementedError()
return self.dataset.get_val_examples()
def get_test_examples(self, data_dir):
def get_test_examples(self):
"""Gets a collection of `InputExample`s for prediction."""
raise NotImplementedError()
return self.dataset.get_test_examples()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
return self.dataset.get_labels()
def convert_example(self, index, example, labels, max_seq_len, tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
......@@ -76,9 +74,10 @@ class DataProcessor(object):
Args:
feature: InputFeatures(object). A single set of features of data.
"""
input_pos = list(range(len(feature.input_ids)))
position_ids = list(range(len(feature.input_ids)))
return [
feature.input_ids, feature.segment_ids, input_pos, feature.label_id
feature.input_ids, feature.segment_ids, position_ids,
feature.label_id
]
def generate_batch_data(self,
......@@ -87,7 +86,7 @@ class DataProcessor(object):
voc_size=-1,
mask_id=-1,
return_input_mask=True,
return_max_len=True,
return_max_len=False,
return_num_token=False):
return prepare_batch_data(
batch_data,
......@@ -99,19 +98,9 @@ class DataProcessor(object):
sep_id=self.vocab["[SEP]"],
mask_id=-1,
return_input_mask=return_input_mask,
return_max_len=True,
return_max_len=return_max_len,
return_num_token=return_num_token)
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
lines.append(line)
return lines
def get_num_examples(self, phase):
"""Get number of examples for train, dev or test."""
if phase not in ['train', 'val', 'dev', 'test']:
......@@ -120,13 +109,9 @@ class DataProcessor(object):
)
return self.num_examples[phase]
def get_train_progress(self):
"""Gets progress for training phase."""
return self.current_train_example, self.current_train_epoch
def data_generator(self, batch_size, phase='train', epoch=1, shuffle=True):
def data_generator(self, batch_size, phase='train', shuffle=True):
"""
Generate data for train, dev or test.
Generate data for train, dev/val or test.
Args:
batch_size: int. The batch size of generated data.
......@@ -135,59 +120,49 @@ class DataProcessor(object):
shuffle: bool. Whether to shuffle examples.
"""
if phase == 'train':
examples = self.get_train_examples(self.data_dir)
examples = self.get_train_examples()
self.num_examples['train'] = len(examples)
elif phase == 'val' or phase == 'dev':
examples = self.get_dev_examples(self.data_dir)
examples = self.get_dev_examples()
self.num_examples['dev'] = len(examples)
elif phase == 'test':
examples = self.get_test_examples(self.data_dir)
examples = self.get_test_examples()
self.num_examples['test'] = len(examples)
else:
raise ValueError(
"Unknown phase, which should be in ['train', 'dev', 'test'].")
def instance_reader():
for epoch_index in range(epoch):
if shuffle:
np.random.shuffle(examples)
if phase == 'train':
self.current_train_epoch = epoch_index
for (index, example) in enumerate(examples):
if phase == 'train':
self.current_train_example = index + 1
feature = self.convert_example(index, example,
self.get_labels(),
self.max_seq_len,
self.tokenizer)
instance = self.generate_instance(feature)
yield instance
def batch_reader(reader, batch_size, in_tokens):
"""
convert a single instance to BERT input feature
"""
if shuffle:
np.random.shuffle(examples)
for (index, example) in enumerate(examples):
feature = self.convert_example(index, example,
self.get_labels(),
self.max_seq_len, self.tokenizer)
instance = self.generate_instance(feature)
yield instance
def batch_reader(reader, batch_size):
batch, total_token_num, max_len = [], 0, 0
for instance in reader():
token_ids, sent_ids, pos_ids, label = instance[:4]
max_len = max(max_len, len(token_ids))
if in_tokens:
to_append = (len(batch) + 1) * max_len <= batch_size
else:
to_append = len(batch) < batch_size
if to_append:
batch.append(instance)
total_token_num += len(token_ids)
else:
batch.append(instance)
total_token_num += len(token_ids)
if len(batch) == batch_size:
yield batch, total_token_num
batch, total_token_num, max_len = [
instance
], len(token_ids), len(token_ids)
batch, total_token_num, max_len = [], 0, 0
if len(batch) > 0:
yield batch, total_token_num
def wrapper():
for batch_data, total_token_num in batch_reader(
instance_reader, batch_size, self.in_tokens):
instance_reader, batch_size):
batch_data = self.generate_batch_data(
batch_data,
total_token_num,
......@@ -201,27 +176,6 @@ class DataProcessor(object):
return wrapper
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
......@@ -249,271 +203,6 @@ class InputFeatures(object):
self.label_id = label_id
class XnliProcessor(DataProcessor):
"""Processor for the XNLI data set."""
def get_train_examples(self, data_dir):
"""See base class."""
self.language = "zh"
lines = self._read_tsv(
os.path.join(data_dir, "multinli",
"multinli.train.%s.tsv" % self.language))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "train-%d" % (i)
text_a = tokenization.convert_to_unicode(line[0])
text_b = tokenization.convert_to_unicode(line[1])
label = tokenization.convert_to_unicode(line[2])
if label == tokenization.convert_to_unicode("contradictory"):
label = tokenization.convert_to_unicode("contradiction")
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_dev_examples(self, data_dir):
"""See base class."""
self.language = "zh"
lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "dev-%d" % (i)
language = tokenization.convert_to_unicode(line[0])
if language != tokenization.convert_to_unicode(self.language):
continue
text_a = tokenization.convert_to_unicode(line[6])
text_b = tokenization.convert_to_unicode(line[7])
label = tokenization.convert_to_unicode(line[1])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_test_examples(self, data_dir):
"""See base class."""
self.language = "zh"
lines = self._read_tsv(os.path.join(data_dir, "xnli.test.tsv"))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "test-%d" % (i)
language = tokenization.convert_to_unicode(line[0])
if language != tokenization.convert_to_unicode(self.language):
continue
text_a = tokenization.convert_to_unicode(line[6])
text_b = tokenization.convert_to_unicode(line[7])
label = tokenization.convert_to_unicode(line[1])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
"dev_matched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(
line[0]))
text_a = tokenization.convert_to_unicode(line[8])
text_b = tokenization.convert_to_unicode(line[9])
if set_type == "test":
label = "contradiction"
else:
label = tokenization.convert_to_unicode(line[-1])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[3])
text_b = tokenization.convert_to_unicode(line[4])
if set_type == "test":
label = "0"
else:
label = tokenization.convert_to_unicode(line[0])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
# Only the test set has a header
if set_type == "test" and i == 0:
continue
guid = "%s-%s" % (set_type, i)
if set_type == "test":
text_a = tokenization.convert_to_unicode(line[1])
label = "0"
else:
text_a = tokenization.convert_to_unicode(line[3])
label = tokenization.convert_to_unicode(line[1])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class ChnsenticorpProcessor(DataProcessor):
"""Processor for the Chnsenticorp data set."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[1])
label = tokenization.convert_to_unicode(line[0])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class BERTClassifyReader(DataProcessor):
"""Processor for the Chnsenticorp data set."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[1])
label = tokenization.convert_to_unicode(line[0])
examples.append(
InputExample(
guid=guid, text_a=text_a, text_b=None, label=label))
return examples
def convert_single_example_to_unicode(guid, single_example):
text_a = tokenization.convert_to_unicode(single_example[0])
text_b = tokenization.convert_to_unicode(single_example[1])
......
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