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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: answerdotai/ModernBERT-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: edu-modernbert |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# edu-modernbert |
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2864 |
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- Precision: 0.5389 |
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- Recall: 0.3949 |
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- F1: 0.4305 |
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- Accuracy: 0.6820 |
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- Binary Precision: 0.7559 |
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- Binary Recall: 0.4496 |
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- Binary F1: 0.5638 |
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- Binary Accuracy: 0.9373 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 1024 |
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- eval_batch_size: 512 |
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- seed: 0 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Binary Precision | Binary Recall | Binary F1 | Binary Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:----------------:|:-------------:|:---------:|:---------------:| |
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| No log | 0 | 0 | 2.4591 | 0.1116 | 0.1645 | 0.0484 | 0.1386 | 0.0 | 0.0 | 0.0 | 0.9098 | |
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| 0.3196 | 2.4331 | 1000 | 0.3097 | 0.5201 | 0.3691 | 0.3946 | 0.6587 | 0.7614 | 0.3580 | 0.4870 | 0.9320 | |
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| 0.3064 | 4.8662 | 2000 | 0.3067 | 0.5273 | 0.3882 | 0.4154 | 0.6599 | 0.7391 | 0.4375 | 0.5496 | 0.9353 | |
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| 0.3088 | 7.2993 | 3000 | 0.2951 | 0.5353 | 0.3833 | 0.4169 | 0.6744 | 0.7656 | 0.4007 | 0.5261 | 0.9349 | |
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| 0.2991 | 9.7324 | 4000 | 0.2975 | 0.5421 | 0.3921 | 0.4234 | 0.6699 | 0.7316 | 0.4643 | 0.5681 | 0.9363 | |
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| 0.2957 | 12.1655 | 5000 | 0.2920 | 0.5362 | 0.3859 | 0.4207 | 0.6813 | 0.7811 | 0.3953 | 0.5249 | 0.9355 | |
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| 0.2932 | 14.5985 | 6000 | 0.2881 | 0.5364 | 0.3946 | 0.4298 | 0.6824 | 0.7591 | 0.4351 | 0.5532 | 0.9366 | |
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| 0.2862 | 17.0316 | 7000 | 0.2876 | 0.5411 | 0.3850 | 0.4213 | 0.6829 | 0.7713 | 0.4104 | 0.5358 | 0.9359 | |
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| 0.2894 | 19.4647 | 8000 | 0.2864 | 0.5389 | 0.3949 | 0.4305 | 0.6820 | 0.7559 | 0.4496 | 0.5638 | 0.9373 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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