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@kaggle

Kaggle

@kaggle
Kaggle is the largest global AI community of developers, researchers, and enthusiasts who compete, collaborate, and benchmark what's next in AI.
San Francisco
kaggle.com
Joined October 2009
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  • Pinned
    @kaggle
    Kaggle
    @kaggle
    Sep 4
    Introducing ExtractBench on Kaggle Benchmarks with @llama_index. When AI agents rely on schema-guided extraction before human review, one truncated schedule or invented value becomes a wrong payment or decision. ExtractBench evaluates models in workflows based on real-world
    The image shows a bar chart of the ExtractBench benchmark leaderboard by LlamaIndex at Kaggle.

The rankings are:
1. GPT-5.6 Sol - 91.0%
2. GPT-5.6 Terra - 90.0%
3. GPT-5.5 - 89.1%
4. Gemini 3 Flash Preview - 89.0%
5. GPT-5.6 Luna - 89.0%
6. Claude Opus 5 - 88.8%
7. Gemini 3.8 Flash - 87.1%
8. GPT-5.4 Mini - 86.4%
9. Gemini 3.5 Flash - 85.6%
10. Gemini 3.7 Flash - 85.5%
11. Claude Haiku 4.5 - 81.6%
12. Gemma 4 31B IT - 79.9%
13. Gemini 3.5 Flash Lite - 79.4%
14. Gemma 4 26B A4B IT - 77.8%
15. GPT-5.4 Nano - 68.9%
16. Gemini 3.6 Flash - 66.3%
17. DeepSeek V3.1 - 0.0%

Source: ExtractBench Leaderboard (kaggle.com/benchmarks/llamaindex-org/extractbench-leaderboard)
    5
  • @kaggle
    Kaggle
    @kaggle
    22h
    We’re sitting down with Orbit Wars competitors across the hardware spectrum for our first community podcast episode. What questions do you have for them? 👇
    4
  • @kaggle
    Kaggle
    @kaggle
    Aug 29
    Ready to build models that support knee MRI interpretation? 🩻🦵 Check out this starter notebook by Pilkwang Kim. It is a great starting point to read the DICOM acquisitions, sample and normalise the MRI slices, and turn a pretrained vision backbone into twelve abnormality
    Explore and run AI code with Kaggle Notebooks | Using data from multiple data sources
    RSNA Knee baseline v1
    From kaggle.com
    2
  • @kaggle
    Kaggle
    @kaggle
    Aug 28
    What's one thing you learned on Kaggle? 🌟
    26
  • @kaggle
    Kaggle
    @kaggle
    Aug 20
    Today, we’re excited to launch Adversarial Customer Service on Kaggle Benchmarks, in partnership with @GertLabs. This benchmark is a two-sided security game: one model plays a bank's support agent holding customer records and a verification policy, the other plays a caller who
    The image shows the leaderboard for the Adversarial Customer Service benchmark by Gert Labs and Kaggle. It signals Claude Opus 4.8 and Gemini 3.6 Flash in the first place, followed by Gemini 3.5 Flash in the second place, and Gemini 3.5 Flash in the third one. You can find the source at the bottom of the picture, the URL in the post.
    7
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