ModernBERT Base

ModernBERT ## Table of Contents 1. 2. 3. 4. 5. 6. 7. ## Model Summary 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: - for long-context support. - for efficiency on long inputs. - for efficient inference. 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: - - 22 layers, 149 million parameters - - 28 layers, 395 million parameters For more information about ModernBERT, we recommend our for a high-level overview, and our for in-depth information. ## Usage You can use these models directly with the library starting from v4.48.0: Since ModernBERT is a Masked Language Model (MLM), you can use the pipeline or load it via . To use ModernBERT for downstream tasks like classification, retrieval, or QA, fine-tune it following standard BERT fine-tuning recipes. Using : Using a pipeline: ModernBERT does not use token type IDs, unlike some earlier BERT models. Most downstream usage is identical to standard BERT models on the Hugging Face Hub, except you can omit the parameter. ## Evaluation We evaluate ModernBERT across a range of tasks, including natural language understanding (GLUE), general retrieval (BEIR), long-context retrieval (MLDR), and code retrieval (CodeSearchNet and StackQA). - On GLUE, ModernBERT-base surpasses other similarly-sized encoder models, and ModernBERT-large is second only to Deberta-v3-large. - For general retrieval tasks, ModernBERT performs well on BEIR in both single-vector (DPR-style) and multi-vector (ColBERT-style) settings. - Thanks to the inclusion of code data in its training mixture, ModernBERT as a backbone also achieves new state-of-the-art code retrieval results on CodeSearchNet and StackQA. ### Base Models | Model | IR (DPR) | IR (DPR) | IR (DPR) | IR (ColBERT) | IR (ColBERT) | NLU | Code | Code | |-------------|--------------|--------------|--------------|---------------|---------------|------|------|------| | | BEIR | MLDR_OOD | MLDR_ID | BEIR | MLDR_OOD | GLUE | CSN | SQA | | BERT | 38.9 | 23.9 | 32.2 | 49.0 | 28.1 | 84.7 | 41.2 | 59.5 | | RoBERTa | 37.7 | 22.9 | 32.8 | 48.7 | 28.2 | 86.4 | 44.3 | 59.6 | | DeBERTaV3 | 20.2 | 5.4 | 13.4 | 47.1 | 21.9 | 88.1 | 17.5 | 18.6 | | NomicBERT | 41.0 | 26.7 | 30.3 | 49.9 | 61.3 | 84.0 | 41.6 | 61.4 | | GTE-en-MLM | 41.4 | | | 48.2 | 69.3 | 85.6 | 44.9 | 71.4 | | ModernBERT | | 27.4 | 44.0 | | | | || --- ### Large Models | Model | IR (DPR) | IR (DPR) | IR (DPR) | IR (ColBERT) | IR (ColBERT) | NLU | Code | Code | |-------------|--------------|--------------|--------------|---------------|---------------|------|------|------| | | BEIR | MLDR_OOD | MLDR_ID | BEIR | MLDR_OOD | GLUE | CSN | SQA | | BERT | 38.9 | 23.3 | 31.7 | 49.5 | 28.5 | 85.2 | 41.6 | 60.8 | | RoBERTa | 41.4 | 22.6 | 36.1 | 49.8 | 28.8 | 88.9 | 47.3 | 68.1 | | DeBERTaV3 | 25.6 | 7.1 | 19.2 | 46.7 | 23.0 | | 21.2 | 19.7 | | GTE-en-MLM | 42.5 | | | 50.7 | 71.3 | 87.6 | 40.5 | 66.9 | | ModernBERT | | 34.3 | 48.6 | | | 90.4 | || ModernBERT’s strong results, coupled with its efficient runtime on long-context inputs, demonstrate that encoder-only models can be significantly improved through modern architectural choices and extensive pretraining on diversified data sources. ## Limitations ModernBERT’s training data is primarily English and code, so performance may be lower for other languages. While it can handle long sequences efficiently, using the full 8,192 tokens window may be slower than short-context inference. Like any large language model, ModernBERT may produce representations that reflect biases present in its training data. Verify critical or sensitive outputs before relying on them. ## Training - Architecture: Encoder-only, Pre-Norm Transformer with GeGLU activations. - Sequence Length: Pre-trained up to 1,024 tokens, then extended to 8,192 tokens. - Data: 2 trillion tokens of English text and code. - Optimizer: StableAdamW with trapezoidal LR scheduling and 1-sqrt decay. - Hardware: Trained on 8x H100 GPUs. See the paper for more details. ## License We release the ModernBERT model architectures, model weights, training codebase under the Apache 2.0 license. ## Citation If you use ModernBERT in your work, please cite: