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.