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eternis_router_encoder_sft_11Sep
eternis/eternis_router_encoder_sft_11Sep
eternis_router_encoder_sft_11Sep is a machine learning model from eternis. 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. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Sep 12, 2025
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Sep 12, 2025
- Updated
- Sep 12, 2025
- SHA
- d4ce2bee
Base models
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. -->
eternis_router_encoder_sft_11Sep
This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4944
- Model Accuracy: 0.7861
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Accuracy |
|---|---|---|---|---|
| 0.535 | 1.0602 | 300 | 0.5205 | 0.7730 |
| 0.5295 | 2.1204 | 600 | 0.5053 | 0.7764 |
| 0.5166 | 3.1805 | 900 | 0.5038 | 0.7811 |
| 0.5138 | 4.2407 | 1200 | 0.5009 | 0.7807 |
| 0.5196 | 5.3009 | 1500 | 0.5048 | 0.7761 |
| 0.4977 | 6.3611 | 1800 | 0.5034 | 0.7795 |
| 0.4924 | 7.4212 | 2100 | 0.4970 | 0.7803 |
| 0.4915 | 8.4814 | 2400 | 0.5084 | 0.7749 |
| 0.4903 | 9.5416 | 2700 | 0.4960 | 0.7830 |
| 0.4942 | 10.6018 | 3000 | 0.4945 | 0.7873 |
| 0.4926 | 11.6619 | 3300 | 0.4955 | 0.7807 |
| 0.5057 | 12.7221 | 3600 | 0.4929 | 0.7826 |
| 0.4916 | 13.7823 | 3900 | 0.4939 | 0.7838 |
| 0.4953 | 14.8425 | 4200 | 0.4948 | 0.7807 |
| 0.4919 | 15.9027 | 4500 | 0.4947 | 0.7834 |
| 0.5043 | 16.9628 | 4800 | 0.4944 | 0.7854 |
| 0.4768 | 18.0212 | 5100 | 0.4944 | 0.7857 |
| 0.4879 | 19.0814 | 5400 | 0.4944 | 0.7861 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
- Library
- transformers
- License
- apache-2.0
- Created
- Sep 12, 2025
- Updated
- Sep 12, 2025