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Clinical ModernBERT is a state-of-the-art encoder-based transformer tailored specifically for biomedical and clinical text handling context length up to **8192 tokens**. Building on the innovations introduced by ModernBERT, this model extends the context window to 8,192 tokens and incorporates domain-specific vocabulary refinements. It is designed to produce semantically rich representations that capture both the nuanced syntax of biomedical literature and the intricate semantics of clinical narratives. Pretrained model weights and tokenizer artifacts are provided to facilitate easy integration with your downstream biomedical NLP tasks: This model leverages a suite of modern architectural advancements including rotary positional embeddings (RoPE), Flash Attention for near-linear memory usage with extended contexts, and GeGLU activation layers that enhance representational capacity by integrating smooth gating mechanisms. By initializing from a ModernBERT-base checkpoint and applying domain-specific pre-training on approximately 40 million PubMed abstracts combined with MIMIC-IV clinical notes, Clinical ModernBERT is optimized to serve in tasks such as retrieval-augmented generation, fine-grained text classification, and domain-specific entity extraction.

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News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.

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News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.

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