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:
  - Open-Orca/SlimOrca-Dedup
  - GAIR/lima
  - nomic-ai/gpt4all-j-prompt-generations
  - HuggingFaceH4/ultrachat_200k
  - ZenMoore/RoleBench
  - WizardLM/WizardLM_evol_instruct_V2_196
  - c-s-ale/alpaca-gpt4-data
  - THUDM/AgentInstruct
license: mit

Model Card of instructionRoberta-base for Bertology

A minimalistic instruction model with an already good analysed and pretrained encoder like roBERTa. So we can research the Bertology with instruction-tuned models, look at the attention and investigate what happens to BERT embeddings during fine-tuning.

The training code is released at the instructionBERT repository. We used the Huggingface API for warm-starting BertGeneration with Encoder-Decoder-Models for this purpose.

Run the model with a longer output

from transformers import AutoTokenizer, EncoderDecoderModel
# load the fine-tuned seq2seq model and corresponding tokenizer
model_name = "Bachstelze/instructionRoberta-base"
model = EncoderDecoderModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input = "Write a poem about love, peace and pancake."
input_ids = tokenizer(input, return_tensors="pt").input_ids
output_ids = model.generate(input_ids, max_new_tokens=200)
print(tokenizer.decode(output_ids[0]))

Training parameters

base model: "roberta-base"
trained for 1 epoche
batch size of 16
20000 warm-up steps
learning rate of 0.0001

Purpose of instructionRoberta-base

InstructionBERT is intended for research purposes. The model-generated text should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.