transformers pytorch tensorboard bart text2text-generation summarization en dataset:multi_news license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- multi_news
language:
- en
license: mit
tags:
- summarization
model-index:
- name: ppiiesle3y/fined-tuned-bart
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: multi_news
      type: multi_news
      split: train
    metrics:
    - type: rouge
      value: 43.7065
      name: ROUGE-1
      verified: true
    - type: rouge
      value: 16.5533
      name: ROUGE-2
      verified: true
    - type: rouge
      value: 24.7588
      name: ROUGE-L
      verified: true
    - type: rouge
      value: 37.7586
      name: ROUGE-LSUM
      verified: true
    - type: loss
      value: 2.00663
      name: loss
      verified: true
    - type: gen_len
      value: 129.1379
      name: gen_len
      verified: true
---

# TL;DR AT2 Applied Natural Language Processing Assignment

## PROJECT OBJECTIVES
This project aims to use NLP technology to summarise longer passages of text into succinct and accurate summations.

## PROJECT OUTCOMES AND INSIGHTS
The expected outcomes from the project is a model that is able to intake a larger body of text and provide a shortened summary that is both succinct and accurate.  This will benefit most human readers by making it more efficient gain understanding from written text.  Applications for this technology include as a study aide, for people in roles where they are required to quickly assess documents such as book publishers reading through manuscripts to assess if they are fit for publishing or script readers etc. 
The most significant impact this project has is to increase information assimilation in a compressed timeframe, thus saving time.
