---
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: []
---

<!-- 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. -->

# edu-modernbert

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [HuggingFaceFW/fineweb-edu-llama3-annotations](https://huggingface.co/datasets/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

<div class="alert alert-info">
<b>Note:</b> the binary classification score is calculated by thresholding at 3 i.e (0-2 -> 0, 3-5 -> 1).
</div>

In comparison the reproduced version of [HuggingFaceFW/fineweb-edu-classifier](https://huggingface.co/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

<div class="alert alert-info">
<b>Note:</b> one difference is that ModernBERT-base is fully trained while the original classifier trains only the regression head..
</div>


## 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