---
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2864
- Precision: 0.5389
- Recall: 0.3949
- F1: 0.4305
- Accuracy: 0.6820
- Binary Precision: 0.7559
- Binary Recall: 0.4496
- Binary F1: 0.5638
- Binary Accuracy: 0.9373

## 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: 0.0003
- train_batch_size: 1024
- eval_batch_size: 512
- seed: 0
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Binary Precision | Binary Recall | Binary F1 | Binary Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:----------------:|:-------------:|:---------:|:---------------:|
| No log        | 0       | 0    | 2.4591          | 0.1116    | 0.1645 | 0.0484 | 0.1386   | 0.0              | 0.0           | 0.0       | 0.9098          |
| 0.3196        | 2.4331  | 1000 | 0.3097          | 0.5201    | 0.3691 | 0.3946 | 0.6587   | 0.7614           | 0.3580        | 0.4870    | 0.9320          |
| 0.3064        | 4.8662  | 2000 | 0.3067          | 0.5273    | 0.3882 | 0.4154 | 0.6599   | 0.7391           | 0.4375        | 0.5496    | 0.9353          |
| 0.3088        | 7.2993  | 3000 | 0.2951          | 0.5353    | 0.3833 | 0.4169 | 0.6744   | 0.7656           | 0.4007        | 0.5261    | 0.9349          |
| 0.2991        | 9.7324  | 4000 | 0.2975          | 0.5421    | 0.3921 | 0.4234 | 0.6699   | 0.7316           | 0.4643        | 0.5681    | 0.9363          |
| 0.2957        | 12.1655 | 5000 | 0.2920          | 0.5362    | 0.3859 | 0.4207 | 0.6813   | 0.7811           | 0.3953        | 0.5249    | 0.9355          |
| 0.2932        | 14.5985 | 6000 | 0.2881          | 0.5364    | 0.3946 | 0.4298 | 0.6824   | 0.7591           | 0.4351        | 0.5532    | 0.9366          |
| 0.2862        | 17.0316 | 7000 | 0.2876          | 0.5411    | 0.3850 | 0.4213 | 0.6829   | 0.7713           | 0.4104        | 0.5358    | 0.9359          |
| 0.2894        | 19.4647 | 8000 | 0.2864          | 0.5389    | 0.3949 | 0.4305 | 0.6820   | 0.7559           | 0.4496        | 0.5638    | 0.9373          |


### Framework versions

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