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      nezha_evaluate/nezha_evaluate.py
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      nezha_evaluate/tokenization.py

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nezha_evaluate/README.md 查看文件

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NEZHA-EVALUATE
=============
The scripts and model are based on https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-Gen-TensorFlow
Release Notes
=============
First version: 2021/04/22
Environment
============
The scripts are tested sucessfully with Tensorflow 1.13 and Python 3.6.
The python package ``fire`` is required. You may need to install the ``fire`` package with the command:
```
pip3 install fire
```
Usage
====================
Step 1: Download the folder named ``models_gpt`` from the link below and move the folder to the same directory with the scripts.
Step 2: Move the file named ``dev.json`` to the same directory with the scripts.
Step 3: Run the script ``nezha_evaluate.py`` with the command:
```
python3 nezha_evaluate.py
```
Model download
===========================
* Chinese GPT
* [Google Drive](https://drive.google.com/drive/folders/1i4f_8LhaVDNjnGlLXNJ0rNgBP0E4L6V0?usp=sharing)
* [Baidu Netdisk](https://pan.baidu.com/s/1Bgle8TpcxHyuUz_jAXOBWw)(code:rb5m)

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nezha_evaluate/nezha_evaluate.py
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nezha_evaluate/tokenization.py 查看文件

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# 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 re
import unicodedata
import six
import tensorflow as tf


def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
"""Checks whether the casing config is consistent with the checkpoint name."""

# The casing has to be passed in by the user and there is no explicit check
# as to whether it matches the checkpoint. The casing information probably
# should have been stored in the bert_config.json file, but it's not, so
# we have to heuristically detect it to validate.

if not init_checkpoint:
return

m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
if m is None:
return

model_name = m.group(1)

lower_models = [
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
]

cased_models = [
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
"multi_cased_L-12_H-768_A-12"
]

is_bad_config = False
if model_name in lower_models and not do_lower_case:
is_bad_config = True
actual_flag = "False"
case_name = "lowercased"
opposite_flag = "True"

if model_name in cased_models and do_lower_case:
is_bad_config = True
actual_flag = "True"
case_name = "cased"
opposite_flag = "False"

if is_bad_config:
raise ValueError(
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
"However, `%s` seems to be a %s model, so you "
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
"how the model was pre-training. If this error is wrong, please "
"just comment out this check." % (actual_flag, init_checkpoint,
model_name, case_name, opposite_flag))


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()
index = 0
with tf.gfile.GFile(vocab_file, "r") as reader:
while True:
token = convert_to_unicode(reader.readline())
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
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 piece 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 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=200):
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

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