Spaces:
Runtime error
Runtime error
| # 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 | |
| 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)) | |