transformers safetensors encoder-decoder text2text-generation en dataset:Open-Orca/SlimOrca-Dedup dataset:GAIR/lima dataset:nomic-ai/gpt4all-j-prompt-generations dataset:HuggingFaceH4/ultrachat_200k dataset:ZenMoore/RoleBench dataset:WizardLM/WizardLM_evol_instruct_V2_196 dataset:c-s-ale/alpaca-gpt4-data dataset:THUDM/AgentInstruct license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
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
language:
- en
license: apache-2.0
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
---
# Model Card for the test-version of instructionBERT for Bertology

![BERT illustration](./The_cinematic_puppet_Bert_from_sesame_street_carries_89f3c10a_273b.png)


A minimalistic instruction model with an already good analysed and pretrained encoder like BERT.
So we can research the [Bertology](https://aclanthology.org/2020.tacl-1.54.pdf) with instruction-tuned models, [look at the attention](https://colab.research.google.com/drive/1mNP7c0RzABnoUgE6isq8FTp-NuYNtrcH?usp=sharing) and investigate [what happens to BERT embeddings during fine-tuning](https://aclanthology.org/2020.blackboxnlp-1.4.pdf).

The training code is released at the [instructionBERT repository](https://gitlab.com/Bachstelze/instructionbert).
We used the Huggingface API for [warm-starting](https://huggingface.co/blog/warm-starting-encoder-decoder) [BertGeneration](https://huggingface.co/docs/transformers/model_doc/bert-generation) with [Encoder-Decoder-Models](https://huggingface.co/docs/transformers/v4.35.2/en/model_doc/encoder-decoder) for this purpose.

## Run the model with a longer output

```python
from transformers import AutoTokenizer, EncoderDecoderModel
# load the fine-tuned seq2seq model and corresponding tokenizer
model_name = "Bachstelze/instructionBERT"
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: "bert-base-uncased"
- trained for 1 epoche
- batch size of 16
- 20000 warm-up steps
- learning rate of 0.0001

## Purpose of instructionBERT
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.