Improve dataset card: Add library_name, license, benchmark tag, GitHub link, and sample usage (#3)
Browse files- Improve dataset card: Add library_name, license, benchmark tag, GitHub link, and sample usage (c76f084a1da8b4df007b8ce879f04a806e1d5b93)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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---
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task_categories:
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- question-answering
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- summarization
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- text-generation
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- en
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tags:
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- llm
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- kv_cache
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-
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-
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configs:
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- config_name: multi-turn_FS
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data_files: multi_turn/few_shot_learning/*.jsonl
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Huggingface: https://huggingface.co/papers/2507.13681
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βββ README.md
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βββ conversations.jsonl
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βββ multi_turn
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β βββ few_shot_learning
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β βββ needle_in_haystack
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β βββ question_answering
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β βββ summarization
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βββ single_turn
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βββ few_shot_learning
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βββ needle_in_haystack
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βββ question_answering
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βββ summarization
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```
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# Introduction
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For more details, please refer to our paper.
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# Citation
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``` bibtex
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@misc{li2025loopserveadaptivedualphasellm,
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2507.13681},
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}
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```
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---
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language:
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- en
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task_categories:
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- question-answering
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- summarization
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- text-generation
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pretty_name: LoopServe Multi-Turn Dialogue Benchmark
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tags:
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- llm
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- kv_cache
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- benchmark
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library_name: datasets
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license: cc-by-4.0
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configs:
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- config_name: multi-turn_FS
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data_files: multi_turn/few_shot_learning/*.jsonl
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Huggingface: https://huggingface.co/papers/2507.13681
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Code: https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management
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# Introduction
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For more details, please refer to our paper.
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# Sample Usage
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The dataset can be easily loaded using the `load_dataset` function from the π€ Datasets library.
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```python
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from datasets import load_dataset
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# Load a specific configuration, for example, the multi-turn question answering data
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dataset = load_dataset("TreeAILab/Multi-turn_Long-context_Benchmark_for_LLMs", "multi-turn_QA")
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# Access the training split
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print(dataset["train"])
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# Iterate through an example
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for example in dataset["train"]:
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print(example)
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break
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```
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# Citation
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``` bibtex
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@misc{li2025loopserveadaptivedualphasellm,
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2507.13681},
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}
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```
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