ModernBERT-Large-Instruct-ONNX

ModernBERT-Large-Instruct (ONNX)#

This is an ONNX version of answerdotai/ModernBERT-Large-Instruct. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js#

See the pipeline documentation for fill-mask: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline

ModernBERT-Large-Instruct#

Table of Contents#

1. Model Summary
2. Usage
3. Evaluation
4. Limitations
5. Training
6. License
7. Citation

Model Summary#

ModernBERT-Instruct-Large is a lightly instruction-tuned version of ModernBERT-large, trained using a mixed-objective (Answer Token Prediction & Dummy MLM) on 20M examples sampled from the FLAN collection.

Despite a very straightforward pre-training and inference pipeline, this model proves to be a very strong model in a variety of contexts, in both zero-shot and fully-finetuned settings.

For more details, we recommend checking out the TIL Blog Post, the mini cookbook GitHub repository or the Technical Report.

Usage#

In order to use ModernBERT-Large-Instruct, you need to install a version of transformers which natively supports ModernBERT (4.48+):

pip install -U transformers>=4.48.0

⚠️ If your GPU supports it, we recommend using ModernBERT with Flash Attention 2 to reach the highest efficiency. To do so, install Flash Attention as follows, then use the model as normal:

pip install flash-attn

All tasks are then performed using the Model's Masked Language Modelling head, load via AutoModelForMaskedLM. Here is an example to answer an MMLU question:

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

# Load model and tokenizer
model_name = "answerdotai/ModernBERT-Large-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device == 'cuda':
    model = AutoModelForMaskedLM.from_pretrained(model_name, attn_implementation="flash_attention_2")
else:
    model = AutoModelForMaskedLM.from_pretrained(model_name)

model.to(device)

# Format input for classification or multiple choice. This is a random example from MMLU.
text = """You will be given a question and options. Select the right answer.
QUESTION: If (G, .) is a group such that (ab)^-1 = a^-1b^-1, for all a, b in G, then G is a/an
CHOICES:
- A: commutative semi group
- B: abelian group
- C: non-abelian group
- D: None of these
ANSWER: [unused0] [MASK]"""

# Get prediction
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model(**inputs)
mask_idx = (inputs.input_ids == tokenizer.mask_token_id).nonzero()[0, 1]
pred_id = outputs.logits[0, mask_idx].argmax()
answer = tokenizer.decode(pred_id)
print(f"Predicted answer: {answer}")  # Outputs: B

Evaluation#

Results are taken from the technical report. Results for MMLU and MMLU-Pro are taken from SmolLM2 (†) and the MMLU-Pro leaderboard (‡) whenever possible.

Zero-Shot#

Fine-Tuned#

Limitations#

ModernBERT’s training data is primarily English and code, so performance is best on these languages. ModernBERT-Large-Instruct is a first version, demonstrating the strong potential of using the MLM head for downstream tasks without complex pipelines. However, it is very likely to have failure cases and it could be improved further.

License#

Apache 2.0

Citation#

If you use ModernBERT-Large-Instruct in your work, please cite:

@misc{clavié2025itsmasksimpleinstructiontuning,
      title={It's All in The [MASK]: Simple Instruction-Tuning Enables BERT-like Masked Language Models As Generative Classifiers}, 
      author={Benjamin Clavié and Nathan Cooper and Benjamin Warner},
      year={2025},
      eprint={2502.03793},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.03793}, 
}