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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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model-index: |
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- name: nci-technique-classifier-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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# nci-technique-classifier-v2 |
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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: 0.0233 |
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- Micro F1: 0.8017 |
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- Macro F1: 0.6272 |
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- Micro Precision: 0.8311 |
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- Micro Recall: 0.7743 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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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: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 | Micro Precision | Micro Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:| |
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| No log | 0.1634 | 200 | 0.0350 | 0.6311 | 0.1526 | 0.7644 | 0.5373 | |
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| No log | 0.3268 | 400 | 0.0305 | 0.6658 | 0.1814 | 0.8020 | 0.5692 | |
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| 0.0552 | 0.4902 | 600 | 0.0282 | 0.7023 | 0.2044 | 0.8244 | 0.6117 | |
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| 0.0552 | 0.6536 | 800 | 0.0263 | 0.7268 | 0.2181 | 0.8509 | 0.6343 | |
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| 0.0273 | 0.8170 | 1000 | 0.0256 | 0.7497 | 0.2610 | 0.8305 | 0.6832 | |
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| 0.0273 | 0.9804 | 1200 | 0.0249 | 0.7462 | 0.2371 | 0.8740 | 0.6510 | |
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| 0.0273 | 1.1438 | 1400 | 0.0245 | 0.7626 | 0.2862 | 0.8450 | 0.6949 | |
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| 0.0231 | 1.3072 | 1600 | 0.0242 | 0.7583 | 0.2371 | 0.8582 | 0.6793 | |
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| 0.0231 | 1.4706 | 1800 | 0.0238 | 0.7650 | 0.3155 | 0.8457 | 0.6984 | |
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| 0.0226 | 1.6340 | 2000 | 0.0238 | 0.7624 | 0.3074 | 0.8542 | 0.6885 | |
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| 0.0226 | 1.7974 | 2200 | 0.0230 | 0.7626 | 0.3634 | 0.8681 | 0.68 | |
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| 0.0226 | 1.9608 | 2400 | 0.0223 | 0.7747 | 0.4246 | 0.8675 | 0.6998 | |
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| 0.0214 | 2.1242 | 2600 | 0.0225 | 0.7731 | 0.4412 | 0.8752 | 0.6924 | |
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| 0.0214 | 2.2876 | 2800 | 0.0221 | 0.7775 | 0.4101 | 0.8733 | 0.7005 | |
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| 0.0189 | 2.4510 | 3000 | 0.0219 | 0.7819 | 0.4757 | 0.8414 | 0.7303 | |
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| 0.0189 | 2.6144 | 3200 | 0.0224 | 0.7796 | 0.4224 | 0.8606 | 0.7126 | |
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| 0.0189 | 2.7778 | 3400 | 0.0217 | 0.7922 | 0.5512 | 0.8389 | 0.7504 | |
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| 0.0187 | 2.9412 | 3600 | 0.0217 | 0.7813 | 0.4680 | 0.8610 | 0.7150 | |
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| 0.0187 | 3.1046 | 3800 | 0.0224 | 0.7912 | 0.5458 | 0.8341 | 0.7526 | |
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| 0.0155 | 3.2680 | 4000 | 0.0231 | 0.7922 | 0.5455 | 0.8475 | 0.7437 | |
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| 0.0155 | 3.4314 | 4200 | 0.0231 | 0.7996 | 0.5843 | 0.8295 | 0.7717 | |
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| 0.0155 | 3.5948 | 4400 | 0.0223 | 0.8004 | 0.5706 | 0.8398 | 0.7646 | |
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| 0.0148 | 3.7582 | 4600 | 0.0228 | 0.8096 | 0.6067 | 0.8527 | 0.7706 | |
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| 0.0148 | 3.9216 | 4800 | 0.0229 | 0.8135 | 0.6228 | 0.8457 | 0.7837 | |
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| 0.0126 | 4.0850 | 5000 | 0.0255 | 0.8095 | 0.6251 | 0.8379 | 0.7830 | |
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| 0.0126 | 4.2484 | 5200 | 0.0267 | 0.8061 | 0.6223 | 0.8325 | 0.7812 | |
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| 0.0126 | 4.4118 | 5400 | 0.0261 | 0.8081 | 0.6338 | 0.8372 | 0.7809 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.9.1+cu128 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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