NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models
Paper
ā¢
2410.11805
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Published
ā¢
14
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4
Tong Zhu
Spico
AI & ML interests
Information Extraction, Mixture-of-Experts, LLM
Recent Activity
updated
a dataset
8 days ago
Spico/Mirror_ACE
reacted
to
BramVanroy's
post
with ā¤ļø
8 days ago
š¢š¾ Introducing the Common Crawl Creative Commons Corpus (C5)!
C5 is a large-scale effort to heavily filter web-crawled data, as collected by the non-profit Common Crawl, to only documents that are Creative Commons-licensed such as cc-by-4.0 or public domain cc0. At this stage 150 billion tokens have been collected.
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š data: https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons
š§° software: https://github.com/BramVanroy/CommonCrawl-CreativeCommons
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</> To build C5, HTML pages are scrutinized and all links (if any) to CC licenses are collected, both in regular hyperlinks as well as in metadata. Additional data fields are included such as "was the license found in the `head`?" or "if multiple licenses were found, do they contradict each other?", which makes further filtering a breeze.
š In this first version of C5, 8 languages are included (Afrikaans, German, English, French, Frysian, Italian, Dutch and Spanish). The language set was limited for two reasons: computational and storage limitations, and a collaboration with GPT-NL, which requested CC data for these languages to train a Dutch-focused, copyright-conscious LLM. In total, this V1 release contains almost 150 thousand documents and 150 billion tokens. This data was not filtered on quality nor deduplicated so that you can decide for yourself how much data to keep. To give some quality indication, a dataset field is present to describe whether a document is included in the FineWeb(-2) datasets, which are of high quality.
š More work needs to be done! Only 7 out of 100+ Common Crawl crawls have been processed so far. That's encouraging because it means there is a lot more Creative Commons data to be collected! But to get there I need help in terms of compute. The current processing was already heavily sponsored by the Flemish Supercomputer but more is needed. If you have the compute available and which to collaborate in an open and transparent manner, please get in touch!
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A Survey of Efficient Reasoning for Large Reasoning Models: Language,
Multimodality, and Beyond
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