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CeLLaTe3.1_Base_CRF_no_vague_adapted_pubmed
OTAR3088/CeLLaTe3.1_Base_CRF_no_vague_adapted_pubmed
CeLLaTe3.1_Base_CRF_no_vague_adapted_pubmed is a token classification model from OTAR3088. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Apr 13, 2026
- Updated
- Apr 13, 2026
- SHA
- 4315168a
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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. -->
CeLLaTe3.1_Base_CRF_no_vague_adapted_pubmed
This model is a fine-tuned version of Mardiyyah/cellate2.0-tapt_base-LR_5e-05 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1429
- Precision: 0.6820
- Recall: 0.8154
- F1: 0.7428
- Accuracy: 0.9829
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: 32
- eval_batch_size: 16
- seed: 3407
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.01
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 40.5714 | 0.5848 | 100 | 8.0047 | 0.0776 | 0.0808 | 0.0792 | 0.9516 |
| 7.6643 | 1.1696 | 200 | 3.4953 | 0.6548 | 0.5275 | 0.5843 | 0.9804 |
| 4.1729 | 1.7544 | 300 | 2.9402 | 0.6559 | 0.7255 | 0.6889 | 0.9810 |
| 3.0949 | 2.3392 | 400 | 2.9402 | 0.6412 | 0.8171 | 0.7186 | 0.9810 |
| 2.2148 | 2.9240 | 500 | 2.8872 | 0.6915 | 0.7729 | 0.7299 | 0.9820 |
| 2.0593 | 3.5088 | 600 | 2.7382 | 0.6760 | 0.7893 | 0.7283 | 0.9824 |
| 1.7345 | 4.0936 | 700 | 2.9636 | 0.6863 | 0.7709 | 0.7262 | 0.9832 |
| 1.4038 | 4.6784 | 800 | 3.1429 | 0.6820 | 0.8154 | 0.7428 | 0.9829 |
| 1.1723 | 5.2632 | 900 | 4.0733 | 0.6382 | 0.7526 | 0.6907 | 0.9804 |
| 1.3177 | 5.8480 | 1000 | 4.8858 | 0.5487 | 0.7507 | 0.6340 | 0.9737 |
| 0.9404 | 6.4327 | 1100 | 4.0962 | 0.5945 | 0.7893 | 0.6782 | 0.9775 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 13, 2026
- Updated
- Apr 13, 2026