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nci-scorer-nci-only
synapti/nci-scorer-nci-only
nci-scorer-nci-only is a machine learning model from synapti. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- nci_only
- Access
- Public
- Created
- Dec 13, 2025
- Updated
- Oct 14, 2026
- SHA
- 902a8752
Model card
From the Hugging Face model README
<!-- 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-scorer-nci-only-staging
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0141
- Nci Mae: 0.2892
- Nci Acc Within 1: 0.9475
- Nci Labelled Share: 0.8551
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_steps: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Nci Mae | Nci Acc Within 1 | Nci Labelled Share |
|---|---|---|---|---|---|---|
| No log | 0.6780 | 200 | 0.0332 | 0.6290 | 0.8858 | 0.8550 |
| No log | 1.3559 | 400 | 0.0210 | 0.4261 | 0.9190 | 0.8550 |
| 0.0585 | 2.0339 | 600 | 0.0184 | 0.3806 | 0.9302 | 0.8550 |
| 0.0585 | 2.7119 | 800 | 0.0170 | 0.3440 | 0.9372 | 0.8550 |
| 0.0181 | 3.3898 | 1000 | 0.0191 | 0.3441 | 0.9285 | 0.8550 |
| 0.0181 | 4.0678 | 1200 | 0.0159 | 0.3232 | 0.9413 | 0.8550 |
| 0.0181 | 4.7458 | 1400 | 0.0157 | 0.3172 | 0.9432 | 0.8550 |
| 0.0137 | 5.4237 | 1600 | 0.0157 | 0.3149 | 0.9413 | 0.8550 |
| 0.0137 | 6.1017 | 1800 | 0.0149 | 0.3059 | 0.9455 | 0.8550 |
| 0.0114 | 6.7797 | 2000 | 0.0149 | 0.3038 | 0.9458 | 0.8550 |
| 0.0114 | 7.4576 | 2200 | 0.0150 | 0.3025 | 0.9459 | 0.8550 |
| 0.0114 | 8.1356 | 2400 | 0.0146 | 0.3004 | 0.9476 | 0.8550 |
| 0.0101 | 8.8136 | 2600 | 0.0143 | 0.2991 | 0.9473 | 0.8550 |
| 0.0101 | 9.4915 | 2800 | 0.0142 | 0.2969 | 0.9488 | 0.8550 |
| 0.0101 | 10.0 | 2950 | 0.0143 | 0.2969 | 0.9484 | 0.8550 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.13.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
- Library
- transformers
- Type
- nci_only
- Created
- Dec 13, 2025
- Updated
- Oct 14, 2026