base on A lightweight library for generating synthetic instruction tuning datasets for your data without GPT. # Bonito Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. This repo is a lightweight library for Bonito to easily create synthetic datasets built on top of the Hugging Face `transformers` and `vllm` libraries. - Paper: [Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334) - Model: [bonito-v1](https://huggingface.co/BatsResearch/bonito-v1) - Demo: [Bonito on Spaces](https://huggingface.co/spaces/nihalnayak/bonito) - Dataset: [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1) - Code: To reproduce experiments in our paper, see [nayak-aclfindings24-code](https://github.com/BatsResearch/nayak-aclfindings24-code).  ## News - 🐡 August 2024: Released [new Bonito model](https://huggingface.co/BatsResearch/Llama-3.1-8B-bonito-v1) with Meta Llama 3.1 as the base model. - 🐟 June 2024: Bonito is accepted to ACL Findings 2024. ## Installation Create an environment and install the package using the following commands: ```bash conda create -n bonito python=3.9 conda activate bonito pip install -e . ``` ## Basic Usage To generate synthetic instruction tuning dataset using Bonito, you can use the following code: ```python from bonito import Bonito from vllm import SamplingParams from datasets import load_dataset # Initialize the Bonito model bonito = Bonito("BatsResearch/bonito-v1") # load dataset with unannotated text unannotated_text = load_dataset( "BatsResearch/bonito-experiment", "unannotated_contract_nli" )["train"].select(range(10)) # Generate synthetic instruction tuning dataset sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1) synthetic_dataset = bonito.generate_tasks( unannotated_text, context_col="input", task_type="nli", sampling_params=sampling_params ) ``` ## Supported Task Types Here we include the supported task types [full name (short form)]: `extractive question answering` (`exqa`), `multiple-choice question answering` (`mcqa`), `question generation` (`qg`), `question answering without choices` (`qa`), `yes-no question answering` (`ynqa`), `coreference resolution` (`coref`), `paraphrase generation` (`paraphrase`), `paraphrase identification` (`paraphrase_id`), `sentence completion` (`sent_comp`), `sentiment` (`sentiment`), `summarization` (`summarization`), `text generation` (`text_gen`), `topic classification` (`topic_class`), `word sense disambiguation` (`wsd`), `textual entailment` (`te`), `natural language inference` (`nli`) You can use either the full name or the short form to specify the `task_type` in `generate_tasks`. ## Tutorial We have created a tutorial [here](https://colab.research.google.com/drive/12OCh4OYo1vr9ZvwIWK4JwZT7rkMrYrx2?usp=sharing) for how to use a quantized version of the model in a Google Colab T4 instance. The quantized version was graciously contributed by user [alexandreteles](https://github.com/alexandreteles). We have an additional tutorial to try out the Bonito model on A100 GPU on Google Colab [here](https://colab.research.google.com/drive/1XuDRVKpUUqdjrqg2-P2FIqkdAQBnqoNL?usp=sharing). ## Citation If you use Bonito in your research, please cite the following paper: ``` @inproceedings{bonito:aclfindings24, title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation}, author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2024}, year = {2024}} ``` ", Assign "at most 3 tags" to the expected json: {"id":"8241","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"