File size: 4,929 Bytes
8ebda9e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
# coding: utf-8
# Copyright 2019 Sinovation Ventures AI Institute
#
# 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.
"""utils for ngram for ZEN model."""
import os
import logging
from transformers import cached_path
NGRAM_DICT_NAME = 'ngram.txt'
logger = logging.getLogger(__name__)
PRETRAINED_VOCAB_ARCHIVE_MAP = {'IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese': 'https://huggingface.co/IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese/resolve/main/ngram.txt'}
class ZenNgramDict(object):
"""
Dict class to store the ngram
"""
def __init__(self, ngram_freq_path, tokenizer, max_ngram_in_seq=128):
"""Constructs ZenNgramDict
:param ngram_freq_path: ngrams with frequency
"""
if os.path.isdir(ngram_freq_path):
ngram_freq_path = os.path.join(ngram_freq_path, NGRAM_DICT_NAME)
self.ngram_freq_path = ngram_freq_path
self.max_ngram_in_seq = max_ngram_in_seq
self.id_to_ngram_list = ["[pad]"]
self.ngram_to_id_dict = {"[pad]": 0}
self.ngram_to_freq_dict = {}
logger.info("loading ngram frequency file {}".format(ngram_freq_path))
with open(ngram_freq_path, "r", encoding="utf-8") as fin:
for i, line in enumerate(fin):
ngram, freq = line.split(",")
tokens = tuple(tokenizer.tokenize(ngram))
self.ngram_to_freq_dict[ngram] = freq
self.id_to_ngram_list.append(tokens)
self.ngram_to_id_dict[tokens] = i + 1
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
ngram_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
logger.warning("The pre-trained model you are loading is a cased model but you have not set "
"`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
"you may want to check this behavior.")
kwargs['do_lower_case'] = False
elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
logger.warning("The pre-trained model you are loading is an uncased model but you have set "
"`do_lower_case` to False. We are setting `do_lower_case=True` for you "
"but you may want to check this behavior.")
kwargs['do_lower_case'] = True
else:
ngram_file = pretrained_model_name_or_path
if os.path.isdir(ngram_file):
ngram_file = os.path.join(ngram_file, NGRAM_DICT_NAME)
# redirect to the cache, if necessary
try:
resolved_ngram_file = cached_path(ngram_file, cache_dir=cache_dir)
except EnvironmentError:
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
logger.error(
"Couldn't reach server at '{}' to download vocabulary.".format(
ngram_file))
else:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
ngram_file))
return None
if resolved_ngram_file == ngram_file:
logger.info("loading vocabulary file {}".format(ngram_file))
else:
logger.info("loading vocabulary file {} from cache at {}".format(
ngram_file, resolved_ngram_file))
# Instantiate ngram.
ngram_dict = cls(resolved_ngram_file, **kwargs)
return ngram_dict
def save(self, ngram_freq_path):
with open(ngram_freq_path, "w", encoding="utf-8") as fout:
for ngram, freq in self.ngram_to_freq_dict.items():
fout.write("{},{}\n".format(ngram, freq))
|