---
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