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---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
model-index:
- name: nci-technique-classifier-v2
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. -->
# nci-technique-classifier-v2
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: 0.0233
- Micro F1: 0.8017
- Macro F1: 0.6272
- Micro Precision: 0.8311
- Micro Recall: 0.7743
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 | Micro Precision | Micro Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
| No log | 0.1634 | 200 | 0.0350 | 0.6311 | 0.1526 | 0.7644 | 0.5373 |
| No log | 0.3268 | 400 | 0.0305 | 0.6658 | 0.1814 | 0.8020 | 0.5692 |
| 0.0552 | 0.4902 | 600 | 0.0282 | 0.7023 | 0.2044 | 0.8244 | 0.6117 |
| 0.0552 | 0.6536 | 800 | 0.0263 | 0.7268 | 0.2181 | 0.8509 | 0.6343 |
| 0.0273 | 0.8170 | 1000 | 0.0256 | 0.7497 | 0.2610 | 0.8305 | 0.6832 |
| 0.0273 | 0.9804 | 1200 | 0.0249 | 0.7462 | 0.2371 | 0.8740 | 0.6510 |
| 0.0273 | 1.1438 | 1400 | 0.0245 | 0.7626 | 0.2862 | 0.8450 | 0.6949 |
| 0.0231 | 1.3072 | 1600 | 0.0242 | 0.7583 | 0.2371 | 0.8582 | 0.6793 |
| 0.0231 | 1.4706 | 1800 | 0.0238 | 0.7650 | 0.3155 | 0.8457 | 0.6984 |
| 0.0226 | 1.6340 | 2000 | 0.0238 | 0.7624 | 0.3074 | 0.8542 | 0.6885 |
| 0.0226 | 1.7974 | 2200 | 0.0230 | 0.7626 | 0.3634 | 0.8681 | 0.68 |
| 0.0226 | 1.9608 | 2400 | 0.0223 | 0.7747 | 0.4246 | 0.8675 | 0.6998 |
| 0.0214 | 2.1242 | 2600 | 0.0225 | 0.7731 | 0.4412 | 0.8752 | 0.6924 |
| 0.0214 | 2.2876 | 2800 | 0.0221 | 0.7775 | 0.4101 | 0.8733 | 0.7005 |
| 0.0189 | 2.4510 | 3000 | 0.0219 | 0.7819 | 0.4757 | 0.8414 | 0.7303 |
| 0.0189 | 2.6144 | 3200 | 0.0224 | 0.7796 | 0.4224 | 0.8606 | 0.7126 |
| 0.0189 | 2.7778 | 3400 | 0.0217 | 0.7922 | 0.5512 | 0.8389 | 0.7504 |
| 0.0187 | 2.9412 | 3600 | 0.0217 | 0.7813 | 0.4680 | 0.8610 | 0.7150 |
| 0.0187 | 3.1046 | 3800 | 0.0224 | 0.7912 | 0.5458 | 0.8341 | 0.7526 |
| 0.0155 | 3.2680 | 4000 | 0.0231 | 0.7922 | 0.5455 | 0.8475 | 0.7437 |
| 0.0155 | 3.4314 | 4200 | 0.0231 | 0.7996 | 0.5843 | 0.8295 | 0.7717 |
| 0.0155 | 3.5948 | 4400 | 0.0223 | 0.8004 | 0.5706 | 0.8398 | 0.7646 |
| 0.0148 | 3.7582 | 4600 | 0.0228 | 0.8096 | 0.6067 | 0.8527 | 0.7706 |
| 0.0148 | 3.9216 | 4800 | 0.0229 | 0.8135 | 0.6228 | 0.8457 | 0.7837 |
| 0.0126 | 4.0850 | 5000 | 0.0255 | 0.8095 | 0.6251 | 0.8379 | 0.7830 |
| 0.0126 | 4.2484 | 5200 | 0.0267 | 0.8061 | 0.6223 | 0.8325 | 0.7812 |
| 0.0126 | 4.4118 | 5400 | 0.0261 | 0.8081 | 0.6338 | 0.8372 | 0.7809 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
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