README.md

metadata

language:
  - en
tags:
  - text2text-generation
widget:
  - text: >-
      Please answer to the following question. Who is going to be the next
      Ballon d'or?
    example_title: Question Answering
  - text: >-
      Q: Can Geoffrey Hinton have a conversation with George Washington? Give
      the rationale before answering.
    example_title: Logical reasoning
  - text: >-
      Please answer the following question. What is the boiling point of
      Nitrogen?
    example_title: Scientific knowledge
  - text: >-
      Answer the following yes/no question. Can you write a whole Haiku in a
      single tweet?
    example_title: Yes/no question
  - text: >-
      Answer the following yes/no question by reasoning step-by-step. Can you
      write a whole Haiku in a single tweet?
    example_title: Reasoning task
  - text: 'Q: ( False or not False or False ) is? A: Let''s think step by step'
    example_title: Boolean Expressions
  - text: >-
      The square root of x is the cube root of y. What is y to the power of 2,
      if x = 4?
    example_title: Math reasoning
  - text: >-
      Premise:  At my age you will probably have learned one lesson.
      Hypothesis:  It's not certain how many lessons you'll learn by your
      thirties. Does the premise entail the hypothesis?
    example_title: Premise and hypothesis
datasets:
  - Muennighoff/flan
  - Open-Orca/SlimOrca-Dedup
  - garage-bAInd/Open-Platypus
  - Weyaxi/HelpSteer-filtered
  - GAIR/lima
license: mit

Model Card for the test-version of instructionBERT for Bertology

A minimalistic instruction model with an already good analysed and pretrained encoder like BERT. So we can research the Bertology with instruction-tuned models and investigate what happens to BERT embeddings during fine-tuning. We used the Huggingface API for warm-starting BertGeneration with Encoder-Decoder-Models for this purpose.