edu-modernbert / README.md
staghado's picture
Update README.md
16c2c7e verified
metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: edu-modernbert
    results: []

edu-modernbert

This model is a fine-tuned version of answerdotai/ModernBERT-base on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2453
  • Precision: 0.5901
  • Recall: 0.5245
  • F1: 0.5504
  • Accuracy: 0.7508
  • Binary Precision: 0.8168
  • Binary Recall: 0.6856
  • Binary F1: 0.7455
  • Binary Accuracy: 0.9578
Note: the binary classification score is calculated by thresholding at 3 i.e (0-2 -> 0, 3-5 -> 1).

In comparison the reproduced version of HuggingFaceFW/fineweb-edu-classifier achieves:

  • Loss: 0.2475
  • Precision: 0.5595
  • Recall: 0.4360
  • F1: 0.4704
  • Accuracy: 0.7123
  • Binary Precision: 0.7781
  • Binary Recall: 0.5566
  • Binary F1: 0.6490
  • Binary Accuracy: 0.9457
Note: one difference is that ModernBERT-base is fully trained while the original classifier trains only the regression head..

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 256
  • eval_batch_size: 256
  • seed: 0
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20(totally not needed, 3 epochs already achieve great results)

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0