NishiSearch

Web This site Trusted

7 result(s) for “okapi bm25 site hotchips pp site github com site vina di unit it site:huggingface.co” · 872 ms

Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.

Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)

co/spaces/Praxel/praxy-voice-demo","githubRepo":"https://github
from the top result below

Daily Papers - Hugging Face

https://huggingface.co/papers?q=companion
It is\nfound that the lower limit of binarity is 30

microsoft/benchpress-score-matrix · Datasets at Hugging Face

https://huggingface.co/datasets/microsoft/benchpress-score-matrix
00933 defines the full MCP-Atlas benchmark as 1,000 tasks over 36 real MCP servers and 220 tools; the ScaleAI/MCP-Atlas HF card and scaleapi/mcp-atlas GitHub repo expose the public release as 500 evaluation prompts/tasks, matching this MCPAtlas Public benchmark row

GitHub Actions · Hugging Face

https://huggingface.co/docs/hub/repositories-github-actions
The official huggingface/hub-sync action supports syncing Models, Datasets, and Spaces

Daily Papers - Hugging Face

https://huggingface.co/papers?q=reflective%20essays
","upvotes":3,"discussionId":"6679affcf926ce3cf48fd0d1","githubRepo":"https://github

Daily Papers - Hugging Face

https://huggingface.co/papers?q=persona
Our findings reveal a critical trade-off: while PP can improve classification in socially-sensitive tasks, it often comes at the cost of rationale quality and fails to mitigate underlying biases, urging caution in its application

Daily Papers - Hugging Face

https://huggingface.co/papers?q=role-play
Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team

Trending Papers - Hugging Face

https://huggingface.co/papers/trending
8T scale, Kimi K3 is supported by infrastructure advances in multiple areas: algorithm-system co-design for KDA, perfectly balanced expert-parallel training with efficient memory management, million-token agentic RL with persistent rollout and sandbox states, and deployment innovations