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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.
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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.
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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.