# Domain-Specific BERT

This is a sentence transformer model fine-tuned on Alibaba NLP/gte-modernbert-base, specifically designed for semantic text similarity calculation in the medical field, supporting semantic caching functionality.

This is a sentence transformer model based on mBERT, specifically optimized for the French legal domain, capable of converting legal texts into 768-dimensional vector representations.

BiomedBERT is a large-scale language model pretrained from scratch on PubMed abstracts, specifically designed to enhance performance in biomedical natural language processing tasks.

Biomedical sentence embedding model based on BioLinkBERT, specifically designed for biomedical text similarity calculation

A Patent Problem and Solution Sentence Identification Model based on PatentSBERTa, which maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

This is a text classification model based on BatteryOnlyBERT-uncased, specifically designed for classifying abstracts of battery-related research papers.

BatteryBERT-cased is a pre-trained language model specifically designed for classifying battery-related paper abstracts. Based on the BERT architecture, it is optimized for battery domain texts.

A domain-adapted pre-trained model for agriculture based on the SciBERT checkpoint, optimized for processing agricultural texts.

MatSciBERT is a pre-trained language model based on the BERT architecture, specifically optimized for text mining and information extraction tasks in the field of materials science.

A BERT model specialized for the legal domain, optimized for European Court of Human Rights cases, excelling in legal text processing tasks

A domain-specific language model fine-tuned on 13K chemistry and 14K pharmaceutical Wikipedia article paragraphs for distinguishing between pharmaceutical and chemical domain texts.

This is a BERT model pre-trained on Japanese text, specifically optimized for the financial domain.

This is a sentence-transformers-based model optimized specifically for agricultural domain texts, capable of mapping sentences and paragraphs into a 512-dimensional vector space, suitable for tasks like clustering and semantic search.