Instructions to use Wb-az/peft-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Wb-az/peft-modernbert-base with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base") model = PeftModel.from_pretrained(base_model, "Wb-az/peft-modernbert-base") - Transformers
How to use Wb-az/peft-modernbert-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Wb-az/peft-modernbert-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: apache-2.0 | |
| base_model: answerdotai/ModernBERT-base | |
| tags: | |
| - base_model:adapter:answerdotai/ModernBERT-base | |
| - lora | |
| - transformers | |
| metrics: | |
| - accuracy | |
| - matthews_correlation | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: peft-modernbert-base | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # peft-modernbert-base | |
| This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0410 | |
| - Accuracy: 0.9887 | |
| - Matthews Correlation: 0.9850 | |
| - F1: 0.9760 | |
| - Precision: 0.9730 | |
| - Recall: 0.9792 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0001 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 32 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Matthews Correlation | F1 | Precision | Recall | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------------------:|:------:|:---------:|:------:| | |
| | 0.5348 | 0.1977 | 1400 | 0.0968 | 0.9722 | 0.9631 | 0.9568 | 0.9528 | 0.9609 | | |
| | 0.2975 | 0.3954 | 2800 | 0.0728 | 0.9808 | 0.9745 | 0.9637 | 0.9559 | 0.9725 | | |
| | 0.2385 | 0.5931 | 4200 | 0.0518 | 0.9865 | 0.9821 | 0.9731 | 0.9685 | 0.9780 | | |
| | 0.2500 | 0.7908 | 5600 | 0.0443 | 0.9882 | 0.9843 | 0.9752 | 0.9709 | 0.9803 | | |
| | 0.1968 | 0.9885 | 7000 | 0.0410 | 0.9887 | 0.9850 | 0.9760 | 0.9730 | 0.9792 | | |
| ### Framework versions | |
| - PEFT 0.18.1 | |
| - Transformers 5.2.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 |