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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-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: Assignment4_Finetuned_ModernBERT_V2 |
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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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# Assignment4_Finetuned_ModernBERT_V2 |
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This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1926 |
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- Accuracy: 0.9713 |
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- F1: 0.9709 |
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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: 6e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
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| 2.5497 | 0.2096 | 100 | 0.7948 | 0.7981 | 0.7867 | |
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| 0.514 | 0.4193 | 200 | 0.5135 | 0.8674 | 0.8618 | |
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| 0.3511 | 0.6289 | 300 | 0.4394 | 0.9 | 0.8968 | |
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| 0.2795 | 0.8386 | 400 | 0.3201 | 0.9258 | 0.9241 | |
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| 0.2055 | 1.0482 | 500 | 0.3262 | 0.9258 | 0.9248 | |
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| 0.1421 | 1.2579 | 600 | 0.3060 | 0.94 | 0.9391 | |
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| 0.1235 | 1.4675 | 700 | 0.3153 | 0.9352 | 0.9357 | |
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| 0.1166 | 1.6771 | 800 | 0.2892 | 0.9432 | 0.9427 | |
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| 0.0941 | 1.8868 | 900 | 0.2639 | 0.9513 | 0.9513 | |
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| 0.0832 | 2.0964 | 1000 | 0.2272 | 0.9587 | 0.9584 | |
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| 0.0331 | 2.3061 | 1100 | 0.2210 | 0.96 | 0.9596 | |
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| 0.0509 | 2.5157 | 1200 | 0.2112 | 0.9587 | 0.9582 | |
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| 0.023 | 2.7254 | 1300 | 0.2087 | 0.9597 | 0.9590 | |
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| 0.0206 | 2.9350 | 1400 | 0.2072 | 0.9645 | 0.9638 | |
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| 0.0194 | 3.1447 | 1500 | 0.1981 | 0.9639 | 0.9635 | |
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| 0.0101 | 3.3543 | 1600 | 0.1958 | 0.9697 | 0.9693 | |
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| 0.0052 | 3.5639 | 1700 | 0.2033 | 0.9687 | 0.9683 | |
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| 0.0056 | 3.7736 | 1800 | 0.1985 | 0.97 | 0.9696 | |
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| 0.0117 | 3.9832 | 1900 | 0.1914 | 0.9716 | 0.9713 | |
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| 0.0021 | 4.1929 | 2000 | 0.1910 | 0.9719 | 0.9716 | |
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| 0.0015 | 4.4025 | 2100 | 0.1916 | 0.9716 | 0.9712 | |
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| 0.0024 | 4.6122 | 2200 | 0.1926 | 0.9713 | 0.9709 | |
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| 0.0008 | 4.8218 | 2300 | 0.1926 | 0.9713 | 0.9709 | |
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### Framework versions |
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- Transformers 4.57.0 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
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