readme: ---
  language:
  - en
  tags:
  - summarization
  - t&c
  - tos
  - distilbart
  - distilbart-6-6
  datasets:
  - tosdr
  metrics:
  - rouge1
  - rouge2
  - rougel
  inference:
    parameters:
      min_length: 5
      max_length: 512
      do_sample: False
  widget:
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  ---
  
  # T&C Summarization Model   
  
  T&C Summarization Model based on [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6), 
  
  This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline 
  which is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive 
  summarization shortens the T&C to be further summarized by this model.
  
  ## Finetuning Corpus
  
  We collaborated with [TOSDR](https://tosdr.org/) to work with their data, and the model is finetuned accordingly. The article and 
  summarization text is reduced via extractive summarization before it is finetuned to the model.
  
  ## Contact Us
  
  https://ml6.eu/ . 
  
  This abstractive model finetuning is the continuation of the Christmas Project 2021 done in ML6: https://bit.ly/XmasProjects .
  
  ## Load Finetuned Model
  
  ```
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
  ```
  
  ## Code Sample
  
  This sample requires [sumy](https://pypi.org/project/sumy/), the LSA Extractive Summarization library, as additional package to 
  run.
  
  ```
  import re
  import nltk
  nltk.download('punkt')
  from sumy.parsers.plaintext import PlaintextParser
  from sumy.nlp.tokenizers import Tokenizer
  from sumy.nlp.stemmers import Stemmer
  from sumy.summarizers.lsa import LsaSummarizer
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  LANGUAGE = "english"
  EXTRACTED_ARTICLE_SENTENCES_LEN = 12
  
  stemmer = Stemmer(LANGUAGE)
  lsa_summarizer = LsaSummarizer(stemmer)
  tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
  model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
  
  def get_extractive_summary(text, sentences_count):
    parser = PlaintextParser.from_string(text, Tokenizer(LANGUAGE))
    summarized_info = lsa_summarizer(parser.document, sentences_count)
    summarized_info = [element._text for element in summarized_info]
    return ' '.join(summarized_info)
  
  def get_summary(dict_summarizer_model, dict_tokenizer, text_content):
    text_content = get_extractive_summary(text_content, EXTRACTED_ARTICLE_SENTENCES_LEN)
    tokenizer = dict_tokenizer['tokenizer']
    model = dict_summarizer_model['model']
  
    inputs = tokenizer(text_content, max_length=dict_tokenizer['max_length'], truncation=True, return_tensors="pt")
    outputs = model.generate(
        inputs["input_ids"], max_length=dict_summarizer_model['max_length'], min_length=dict_summarizer_model['min_length'], 
    )
  
    summarized_text = tokenizer.decode(outputs[0])
    match = re.search(r"<s>(.*)</s>", summarized_text)
    if match is not N