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Cilmi baadhayaashu waxay soo bandhigayaan qaabka aasaasiga ah ee is haysta ee farsamaynta dukumentiyada HTML ee Czech

Cilmi-baarayaashu waxay soo bandhigeen qaab aasaasi ah oo is haysta oo loo yaqaan HTML-LM, oo loogu talagalay in lagu farsameeyo dukumeenti HTML ah. Qaabku wuxuu leeyahay 154 milyan oo cabbir waxaana lagu tababaray 100 milyan oo dukumeenti shabakadeed iyadoo la adeegsanayo ujeeddooyin badan.

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Source-page capture accompanying Researchers introduce compact foundation model for Czech HTML documents processing
Dukumeentiga isha aasaasiga ahIsha la duubay
Daabacaha
arxiv.org
Xidhiidhka isha
arxiv.orghttps://arxiv.org/abs/2609.18494
Nooca isha
Dukumeentiga aasaasiga ah - ogeysiis rasmi ah, warqad, xereyn, ama bogga xisbiga koowaad waxaan si toos ah u akhrinay.
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Qaabka Aasaaska
Qaab weyn oo horay loo tababaray oo la qabsan kara hawlo badan oo hoose.
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Hawsha uu qaabku ku meeleeyo gelinta hal ama ka badan oo qaybo hore loo sii qeexay.
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Ururinta tusaalayaal habaysan ama aan qaabaysan loo isticmaalay tababarka, xaqiijinta, ama imtixaanada.
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Maxaa dhacay

Researchers have developed a compact called HTML-LM, which is designed to process Czech HTML documents. The model has 154 million parameters and was trained on 100 million web documents using multiple objectives. It sets a new state-of-the-art for and regression applications in the Czech Internet domain.

The model has 154 million parameters and was trained on 100 million web documents using multiple objectives.

It sets a new state-of-the-art for and regression applications in the Czech Internet domain.

Faahfaahinta isha: arxiv.org ↗

Maxay muhiim u tahay

The development of HTML-LM is significant because it addresses the limitations of existing approaches to processing web documents. It is a compact model that is both performant and economic, making it suitable for high-traffic industrial environments.

The model is trained on a large of web documents, which allows it to learn structural information inherent in HTML.

It uses a ModernBERT-based architecture, which enables it to process real-world web pages effectively.

The model is deployed in production, processing thousands of web documents per second, making it a practical solution for industrial environments.

It is released to the community under the CC BY-NC 4.0 license, making it available for use by others.

The model sets a new state-of-the-art for and regression applications in the Czech Internet domain, surpassing both larger encoders and small-sized LLMs.

Interactive Mechanism

Farsamaynta Is-dhexgalka: Sida Dhabta Ay U Shaqeyso

U baadh tignoolajiyada hoose ee ka dambeeya horumarkan si isdhexgal leh.

Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
Hubinta Fikradda Is-dhexgalka+10 Points
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Maxaa la daawan doona xiga

The development of HTML-LM is a significant step towards creating universal, high-quality representations of web documents in high-traffic industrial environments.

The model's performance and economic viability make it a promising solution for industrial environments.

The model's ability to process real-world web pages effectively makes it a practical solution for a wide range of applications.

The model's release to the community under the CC BY-NC 4.0 license makes it available for use by others.

The model's deployment in production demonstrates its ability to process thousands of web documents per second.

The model's performance in the Czech Internet domain sets a new state-of-the-art, surpassing both larger encoders and small-sized LLMs.

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