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Masu bincike suna gabatar da ƙaramin ƙirar tushe don sarrafa takaddun HTML na Czech

Masu bincike sun gabatar da ƙaƙƙarfan samfurin tushe mai suna HTML-LM, wanda aka ƙera don sarrafa takaddun HTML na Czech. Samfurin yana da sigogi miliyan 154 kuma an horar da shi akan takaddun yanar gizo miliyan 100 ta amfani da maƙasudai da yawa.

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Source-page capture accompanying Researchers introduce compact foundation model for Czech HTML documents processing
Takardun tushe na farkoAn rubuta tushen tushe
Mawallafi
arxiv.org
Tushen hanyar haɗin gwiwa
arxiv.orghttps://arxiv.org/abs/2609.18494
Nau'in tushe
Takardun farko - sanarwar hukuma, takarda, yin rajista, ko shafi na farko da muka karanta kai tsaye.
MaganaFahimtar wannan a cikin daƙiƙa 60

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Samfurin Gidauniya
Babban samfurin da aka riga aka horar wanda za'a iya daidaita shi zuwa ayyuka masu yawa na ƙasa.
Rabewa
Aiki inda samfurin ke sanya shigarwa zuwa ɗaya ko fiye da ƙayyadaddun ƙayyadaddun bayanai.
Saitin bayanai
Tarin misalan da aka tsara ko ba a tsara su da aka yi amfani da su don horo, tabbatarwa, ko gwaji.
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Me ya faru

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.

Bayanan tushe: arxiv.org ↗

Me ya sa yake da mahimmanci

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

Ingantacciyar hanyar sadarwa: Yadda A zahiri yake Aiki

Bincika fasahar da ke bayan wannan ci gaban ta hanyar mu'amala.

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.
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Abin kallo na gaba

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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