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multi-label-summary-text

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Paula Leonova

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	import torch
	from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

	import spacy
	nlp = spacy.load('en_core_web_sm')

	# Reference: https://discuss.huggingface.co/t/summarization-on-long-documents/920/7
	def create_nest_sentences(document:str, token_max_length = 1024):
	nested = []
	sent = []
	length = 0
	tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
	tokens = nlp(document)

	for sentence in tokens.sents:
	tokens_in_sentence = tokenizer(str(sentence), truncation=False, padding=False)[0] # hugging face transformer tokenizer
	length += len(tokens_in_sentence)

	if length < token_max_length:
	sent.append(sentence)
	else:
	nested.append(sent)
	sent = []
	length = 0

	if sent:
	nested.append(sent)
	return nested

	# Reference: https://huggingface.co/facebook/bart-large-mnli
	def load_summary_model():
	model_name = "facebook/bart-large-mnli"
	summarizer = pipeline(task='summarization', model=model_name)
	return summarizer


	# def load_summary_model():
	# model_name = "facebook/bart-large-mnli"
	# tokenizer = BartTokenizer.from_pretrained(model_name)
	# model = BartForConditionalGeneration.from_pretrained(model_name)
	# summarizer = pipeline(task='summarization', model=model, tokenizer=tokenizer, framework='pt')
	# return summarizer


	def summarizer_gen(summarizer, sequence:str, maximum_tokens:int, minimum_tokens:int):
	output = summarizer(sequence, num_beams=4, max_length=maximum_tokens, min_length=minimum_tokens, do_sample=False)
	return output[0].get('summary_text')


	# # Reference: https://www.datatrigger.org/post/nlp_hugging_face/
	# # Custom summarization pipeline (to handle long articles)
	# def summarize(text, minimum_length_of_summary = 100):
	# # Tokenize and truncate
	# inputs = tokenizer_bart([text], truncation=True, max_length=1024, return_tensors='pt').to('cuda')
	# # Generate summary
	# summary_ids = model_bart.generate(inputs['input_ids'], num_beams=4, min_length = minimum_length_of_summary, max_length=400, early_stopping=True)
	# # Untokenize
	# return([tokenizer_bart.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids][0])


	# Reference: https://huggingface.co/spaces/team-zero-shot-nli/zero-shot-nli/blob/main/utils.py
	def load_model():
	model_name = "facebook/bart-large-mnli"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForSequenceClassification.from_pretrained(model_name)
	classifier = pipeline(task='zero-shot-classification', model=model, tokenizer=tokenizer, framework='pt')
	return classifier

	def classifier_zero(classifier, sequence:str, labels:list, multi_class:bool):
	outputs = classifier(sequence, labels, multi_label=multi_class)
	return outputs['labels'], outputs['scores']