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| # coding=utf-8 | |
| # Copyright 2018 The Google AI 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. | |
| # Lint as: python3 | |
| """Helper library for ALBERT fine-tuning. | |
| This library can be used to construct ALBERT models for fine-tuning, either from | |
| json config files or from TF-Hub modules. | |
| """ | |
| from albert import modeling | |
| from albert import tokenization | |
| import tensorflow.compat.v1 as tf | |
| import tensorflow_hub as hub | |
| def _create_model_from_hub(hub_module, is_training, input_ids, input_mask, | |
| segment_ids): | |
| """Creates an ALBERT model from TF-Hub.""" | |
| tags = set() | |
| if is_training: | |
| tags.add("train") | |
| albert_module = hub.Module(hub_module, tags=tags, trainable=True) | |
| albert_inputs = dict( | |
| input_ids=input_ids, | |
| input_mask=input_mask, | |
| segment_ids=segment_ids) | |
| albert_outputs = albert_module( | |
| inputs=albert_inputs, | |
| signature="tokens", | |
| as_dict=True) | |
| return (albert_outputs["pooled_output"], albert_outputs["sequence_output"]) | |
| def _create_model_from_scratch(albert_config, is_training, input_ids, | |
| input_mask, segment_ids, use_one_hot_embeddings, | |
| use_einsum): | |
| """Creates an ALBERT model from scratch/config.""" | |
| model = modeling.AlbertModel( | |
| config=albert_config, | |
| is_training=is_training, | |
| input_ids=input_ids, | |
| input_mask=input_mask, | |
| token_type_ids=segment_ids, | |
| use_one_hot_embeddings=use_one_hot_embeddings, | |
| use_einsum=use_einsum) | |
| return (model.get_pooled_output(), model.get_sequence_output()) | |
| def create_albert(albert_config, is_training, input_ids, input_mask, | |
| segment_ids, use_one_hot_embeddings, use_einsum, hub_module): | |
| """Creates an ALBERT, either from TF-Hub or from scratch.""" | |
| if hub_module: | |
| tf.logging.info("creating model from hub_module: %s", hub_module) | |
| return _create_model_from_hub(hub_module, is_training, input_ids, | |
| input_mask, segment_ids) | |
| else: | |
| tf.logging.info("creating model from albert_config") | |
| return _create_model_from_scratch(albert_config, is_training, input_ids, | |
| input_mask, segment_ids, | |
| use_one_hot_embeddings, use_einsum) | |
| def create_vocab(vocab_file, do_lower_case, spm_model_file, hub_module): | |
| """Creates a vocab, either from vocab file or from a TF-Hub module.""" | |
| if hub_module: | |
| use_spm = True if spm_model_file else False | |
| return tokenization.FullTokenizer.from_hub_module( | |
| hub_module=hub_module, use_spm=use_spm) | |
| else: | |
| return tokenization.FullTokenizer.from_scratch( | |
| vocab_file=vocab_file, do_lower_case=do_lower_case, | |
| spm_model_file=spm_model_file) | |