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import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import streamlit as st
from keybert import KeyBERT
import re
# 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')
for sentence in re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', document.replace("\n", ' ')):
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 = [sentence]
length = 0
if sent:
nested.append(sent)
return nested
# Reference: https://github.com/MaartenGr/KeyBERT
@st.cache_resource
def load_keyword_model():
kw_model = KeyBERT()
return kw_model
def keyword_gen(kw_model, sequence:str):
keywords = kw_model.extract_keywords(sequence,
keyphrase_ngram_range=(1, 1),
stop_words='english',
use_mmr=True,
diversity=0.5,
top_n=10)
return keywords
# Reference: https://huggingface.co/facebook/bart-large-mnli
@st.cache_resource
def load_summary_model():
model_name = "facebook/bart-large-cnn"
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,
length_penalty=2.0,
max_length=maximum_tokens,
min_length=minimum_tokens,
do_sample=False,
early_stopping = True,
no_repeat_ngram_size=3)
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
@st.cache_resource
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']
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