answerdotai/ModernBERT-base
fill-mask model by answerdotai
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Model card & documentation
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ModernBERT is a modernized bidirectional encoder-only Transformer model (BERT-style) pre-trained on 2 trillion tokens of English and code data with a native context length of up to 8,192 tokens. ModernBERT leverages recent architectural improvements such as: ModernBERT’s native long context length makes it ideal for tasks that require processing long documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a large corpus of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search. It is available in the following sizes: For more information about ModernBERT, we recommend our release blog post for a high-level overview, and our arXiv pre-print for in-depth information. ModernBERT is a collaboration between Answer.AI, LightOn, and friends. You can use these models directly with the transformers library starting from v4.48.0: Since ModernBERT is a Masked Language…
Read the full model card ↗ · Preview checked 2026-10-28T23:43:41.471Z
Inside the original model card — Document outline
- ModernBERT
- Table of Contents
- Model Summary
- Usage
- Evaluation
- Base Models
- Large Models
- Limitations
- Training
- License
- Citation
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Model overview
answerdotai/ModernBERT-base is a fill-mask model repository published by answerdotai on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- fill-mask
- Library
- transformers
- Recent downloads (30 days)
- 3,973,079
- Cumulative likes
- 1,135
- Hugging Face trending score
- 16
- Reported safetensors parameters
- 149,655,232
- Architecture
- ModernBertForMaskedLM
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2024-12-11T11:38:06.000Z
- Last modified
- 2025-01-15T20:11:48.000Z
Compatibility and lineage
Reported languages: en
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-16T06:41:25.147Z. Daily imports are snapshots, not real-time monitoring.
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