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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2023 Intel Corporation
#
# 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.
#
def prepare_huggingface_input_data(dataset, hub_name, max_seq_length):
"""
Prepares the input data using the BertTokenizer from Hugging Face for TensorFlow
Args:
dataset (TensorFlow dataset): The TensorFlow dataset to preprocess
hub_name (str): The name of the Hugging Face model
max_seq_length (int): The maximum sentence length to use
Returns:
Tokenized input and labels
"""
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(hub_name)
data_converted = {
'sentences': [],
'labels': [],
}
for elem in dataset.as_numpy_iterator():
# elem would be in the following format:
# (array([sentence1, sentence2, ...]), array([label1, label2, ...]))
data_converted['sentences'].extend(elem[0])
data_converted['labels'].extend(elem[1])
data_converted["sentences"] = [x.decode() for x in data_converted['sentences']]
tokenized_dataset = tokenizer(data_converted['sentences'], padding='max_length',
max_length=max_seq_length, truncation=True, return_tensors='tf')
return tokenized_dataset, data_converted['labels']
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