metadata

language:
  - en
tags:
  - t5
  - qa
  - askscience
  - lfqa
  - information retrieval
datasets:
  - eli5
metrics:
  - rouge
widget:
  - text: why aren't there more planets in our solar system?
    example_title: solar system
  - text: >-
      question: what is a probability distribution? context: I am just learning
      about statistics.
    example_title: probability distribution
  - text: >-
      question: how does exercise help us lose weight? context: I started
      working out two weeks ago and already feel a lot better, and started to
      think about it and became deeply confused.
    example_title: pumpen
  - text: what is a neural network?
    example_title: deep learning
  - text: How can computers understand human language?
    example_title: NLP
inference:
  parameters:
    max_length: 64
    no_repeat_ngram_size: 2
    encoder_no_repeat_ngram_size: 3
    repetition_penalty: 2.4
    length_penalty: 0.5
    num_beams: 4
    early_stopping: true

t5 - base- askscience

t5-v1_1 trained on the entirety of the askscience sub-section of the eli5 dataset for one epoch.
compare to bart on eli5 here
note that for the inference API, the model is restricted to outputting 64 tokens - by using the model in python with the transformers library, you can get longer outputs.

training

for inputs, the model was presented with the post title and the post selftext encoded as: question: <post title> context: <post selftext>. You may see better results if queries are posed in this fashion.
The top two replies were aggregated and presented to the model as the output text.
Training for longer will be explored, but given that the dataset has 127k examples and the loss flatlines at 0.5 epochs so this model should be fairly viable.