datadreamer.json

{
    "data_card": {
        "Get CNN & Daily Mail News Articles": {
            "Date & Time": "2024-01-30T17:35:07.645458",
            "Dataset Name": [
                "cnn_dailymail"
            ],
            "Dataset Card": [
                "https://huggingface.co/datasets/cnn_dailymail"
            ],
            "License Information": [
                "apache-2.0"
            ],
            "Citation Information": [
                "@inproceedings{see-etal-2017-get,\n    title = \"Get To The Point: Summarization with Pointer-Generator Networks\",\n    author = \"See, Abigail  and\n      Liu, Peter J.  and\n      Manning, Christopher D.\",\n    booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2017\",\n    address = \"Vancouver, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P17-1099\",\n    doi = \"10.18653/v1/P17-1099\",\n    pages = \"1073--1083\",\n    abstract = \"Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.\",\n}",
                "@inproceedings{DBLP:conf/nips/HermannKGEKSB15,\n  author={Karl Moritz Hermann and Tom\u00e1s Kocisk\u00fd and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},\n  title={Teaching Machines to Read and Comprehend},\n  year={2015},\n  cdate={1420070400000},\n  pages={1693-1701},\n  url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},\n  booktitle={NIPS},\n  crossref={conf/nips/2015}\n}"
            ]
        },
        "Filter to only keep sports articles": {
            "Date & Time": "2024-01-30T17:35:07.645458",
            "Citation Information": [
                "@article{Zheng2023JudgingLW,\n  title={Judging LLM-as-a-judge with MT-Bench and Chatbot Arena},\n  author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric P. Xing and Haotong Zhang and Joseph Gonzalez and Ion Stoica},\n  journal={ArXiv},\n  year={2023},\n  volume={abs/2306.05685},\n  url={https://api.semanticscholar.org/CorpusID:259129398}\n}"
            ]
        },
        "Filter to only keep sports articles / Getting filter generations": {
            "Date & Time": "2024-01-30T17:35:07.645458",
            "Model Name": [
                "gpt-4"
            ],
            "Model Card": [
                "https://cdn.openai.com/papers/gpt-4-system-card.pdf"
            ],
            "License Information": [
                "https://openai.com/policies"
            ],
            "Citation Information": [
                "@article{OpenAI2023GPT4TR,\n  title={GPT-4 Technical Report},\n  author={OpenAI},\n  journal={ArXiv},\n  year={2023},\n  volume={abs/2303.08774},\n  url={https://api.semanticscholar.org/CorpusID:257532815}\n}",
                "@article{ouyang2022training,\n  title={Training language models to follow instructions with human feedback},\n  author={Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},\n  journal={Advances in Neural Information Processing Systems},\n  volume={35},\n  pages={27730--27744},\n  year={2022}\n}"
            ]
        }
    },
    "__version__": "0.1.0",
    "datetime": "2024-01-30T17:35:07.645458",
    "type": "FilterWithPrompt",
    "name": "Filter to only keep sports articles",
    "version": 1.0,
    "fingerprint": "985a722dd195b5e4",
    "pickled": false,
    "req_versions": {
        "dill": "0.3.7",
        "sqlitedict": "2.1.0",
        "torch": "2.1.2",
        "numpy": "1.26.3",
        "transformers": "4.37.2",
        "datasets": "2.16.1",
        "huggingface_hub": "0.20.3",
        "accelerate": "0.26.1",
        "peft": "0.7.1",
        "tiktoken": "0.5.2",
        "tokenizers": "0.15.1",
        "petals": "2.2.0",
        "openai": "1.10.0",
        "ctransformers": "0.2.27",
        "optimum": "1.16.2",
        "bitsandbytes": "0.42.0",
        "litellm": "1.19.4",
        "trl": "0.7.6",
        "setfit": "1.0.3",
        "together": "0.2.10",
        "google.generativeai": "0.2.1",
        "google-cloud-aiplatform": "1.35.0"
    },
    "interpreter": "3.11.7 (main, Dec  4 2023, 18:10:11) [Clang 15.0.0 (clang-1500.1.0.2.5)]"
}