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edu-modernbert – AI Model by staghado | AlphaNeural AI
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staghado
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edu-modernbert
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transformers
safetensors
modernbert
text-classification
generated_from_trainer
answerdotai/ModernBERT-base
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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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
