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--- |
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language: |
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- en |
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tags: |
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- text2text-generation |
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widget: |
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- text: "Please answer to the following question. Who is going to be the next Ballon d'or?" |
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example_title: "Question Answering" |
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- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." |
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example_title: "Logical reasoning" |
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- text: "Please answer the following question. What is the boiling point of Nitrogen?" |
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example_title: "Scientific knowledge" |
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- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" |
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example_title: "Yes/no question" |
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- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" |
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example_title: "Reasoning task" |
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- text: "Q: ( False or not False or False ) is? A: Let's think step by step" |
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example_title: "Boolean Expressions" |
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- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" |
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example_title: "Math reasoning" |
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- 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?" |
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example_title: "Premise and hypothesis" |
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datasets: |
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- Muennighoff/flan |
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- Open-Orca/SlimOrca-Dedup |
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- garage-bAInd/Open-Platypus |
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- Weyaxi/HelpSteer-filtered |
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- GAIR/lima |
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license: mit |
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--- |
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# Model Card for the test-version of instructionBERT for Bertology |
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<img src="https://cdn-lfs-us-1.huggingface.co/repos/af/f0/aff0dca78d45453b348b539097bf576b294ce2fb0d535457e710a8d8dbe30a25/b8575c4fcac97f746ed06d2bde14bf62daf91cf3b33992dfbc8424017f2bf184?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27The_cinematic_puppet_Bert_from_sesame_street_carries_89f3c10a_273b.png%3B+filename%3D%22The_cinematic_puppet_Bert_from_sesame_street_carries_89f3c10a_273b.png%22%3B&response-content-type=image%2Fpng&Expires=1702654270&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMjY1NDI3MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2FmL2YwL2FmZjBkY2E3OGQ0NTQ1M2IzNDhiNTM5MDk3YmY1NzZiMjk0Y2UyZmIwZDUzNTQ1N2U3MTBhOGQ4ZGJlMzBhMjUvYjg1NzVjNGZjYWM5N2Y3NDZlZDA2ZDJiZGUxNGJmNjJkYWY5MWNmM2IzMzk5MmRmYmM4NDI0MDE3ZjJiZjE4ND9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=Cq74lOcJRv-w1JieDOg1uYIHbekEe2MccwtxQyRFb08%7ENvQHAVqBAqmjAz2XxIajDmtklq-vh38U75%7ElT9Y5OzYRqJ4JwBv73vLMM8zbKELafPPOGWVfEcAh8KFMW5DKLNuqzxBMvInMKK4ylJ6wdT%7EXHBZijUGzrNC7j1R3pgdiG1uh-ndQ7%7EuL-Vw3AU213qC5YUq%7E8IzD8h0cErf-aQP96WtK03Z-50yZmtwLc6L-2FTO95GT5AUKf6BPbuNwkgMW0zzG4oYjE5raGRwrMWKIbTW2nWQK-2oHm9Ojv5TNAo%7Elc75p3AL0xIKC6yUGIxT8L82DUUWaYIF9IoJnwQ__&Key-Pair-Id=KCD77M1F0VK2B" |
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alt="instruction BERT drawing" width="600"/> |
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A minimalistic instruction model with an already good analysed and pretrained encoder like BERT. |
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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). |
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The trainings code is released at the [instructionBERT repository](https://gitlab.com/Bachstelze/instructionbert). |
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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. |
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## Run the model with a longer output |
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```python |
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from transformers import AutoTokenizer, EncoderDecoderModel |
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# load the fine-tuned seq2seq model and corresponding tokenizer |
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model_name = "Bachstelze/instructionBERTtest" |
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model = EncoderDecoderModel.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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input = "Write a poem about love, peace and pancake." |
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input_ids = tokenizer(input, return_tensors="pt").input_ids |
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output_ids = model.generate(input_ids, max_new_tokens=200) |
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print(tokenizer.decode(output_ids[0])) |
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``` |
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## Training parameters |
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- base model: "bert-base-cased" |
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- test subset of the Muennighoff/flan dataset |
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- trained for 0.97 epochs |
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- batch size of 14 |
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- 10000 warm-up steps |
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- learning rate of 0.00005 |
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## Purpose of instructionBERT |
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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. |
