readme: ---
  license: afl-3.0
  ---
  ---
  About : 
  This model can be used for text summarization.
  
   
    
  The dataset on which it was fine tuned consisted of 10,323 articles.
  
  The Data Fields : 
  
  - "Headline" : title of the article
  - "articleBody" : the main article content
  - "source" : the link to the readmore page.
  
  The data splits were : 
  
  - Train : 8258.
  - Vaildation : 2065.
  
  
  ### How to use along with pipeline
  ```python
  from transformers import pipeline
  from transformers import AutoTokenizer, AutoModelForSeq2Seq
  
  tokenizer = AutoTokenizer.from_pretrained("AkashKhamkar/InSumT510k")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("AkashKhamkar/InSumT510k")
  
  summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
  
  summarizer("Text for summarization...", min_length=5, max_length=50)
  ```
  
  language:
  - English
    
  library_name: Pytorch  
  
  tags:
  - Summarization 
  - T5-base
  - Conditional Modelling  
  - 
  
  
  