ModernBERT Base Alternatives

ModernBERT-large is a BERT-based language model hosted on Hugging Face. It is designed for fill-mask tasks and supports long-context processing in English. The model carries the Apache 2.0 license and is published by Answer.AI under the identifier answerdotai/ModernBERT-large. It belongs to the class of masked language models within the Transformers framework. The model is provided in PyTorch, ONNX, and Safetensors formats. Its parameter count stands at 395881664 in F32 precision, with a total file size of 1583544840 bytes when stored in Safetensors. The associated paper is listed under arXiv 2412.13663. The model is available for use through the Hugging Face ecosystem, including libraries such as Transformers. It can be deployed via inference providers and supports local applications and notebooks. Community discussions around the model total 15, with both open and closed threads. ModernBERT-large is intended for developers and researchers working on natural language processing applications that require masked token prediction or extended context handling.

gte-modernbert-base is an embedding model from Alibaba's NLP team that leverages the ModernBERT architecture for superior performance on text embedding tasks. It excels at sentence similarity, semantic search, and retrieval-augmented generation applications. The model is compatible with both the Transformers library and the sentence-transformers framework.

modernbert-embed-base is a sentence-transformers compatible embedding model built on the ModernBERT architecture by Nomic AI. It excels on the MTEB benchmark for tasks including semantic similarity, clustering, and information retrieval. Supports Transformers.js and can be used for building semantic search systems or RAG applications.

Alibaba-NLP/gte-reranker-modernbert-base is an open-source transformer model optimized for text ranking and classification tasks. It supports integration with sentence-transformers and ONNX, making it suitable for search, retrieval, and NLP applications. Designed for developers and researchers in information retrieval.

mdeberta-v3-base is an open-source multilingual transformer model developed by Microsoft for natural language understanding tasks. It supports a wide range of languages and is suitable for applications such as text classification, embeddings, and semantic analysis.

BioBERT is a pre-trained language model initialized from BERT and further trained on large-scale biomedical corpora including PubMed abstracts and PMC full-text articles. It is used for named entity recognition, relation extraction, and question answering in the biomedical domain. The model is widely adopted in medical AI research and available via the Transformers library.

GTE-ModernColBERT-v1 is a ColBERT-style embedding model based on ModernBERT, developed by LightOn AI. It produces token-level embeddings optimized for late-interaction retrieval, enabling highly accurate semantic search over documents. The model supports both query and document encoding and is compatible with the PyLate and sentence-transformers libraries.

bert-base-japanese-v3 is a BERT-base model pretrained on Japanese text from CC-100 and jawiki using whole word masking with the unidic-lite tokenizer. It serves as a strong foundation for Japanese natural language processing tasks such as classification, named entity recognition, and question answering. The model is distributed via the Hugging Face Transformers library.