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CeLLaTe-tapt-biofomer16l-tokenizer-adapted-baseline
Mardiyyah/CeLLaTe-tapt-biofomer16l-tokenizer-adapted-baseline
CeLLaTe-tapt-biofomer16l-tokenizer-adapted-baseline is a fill-mask model from Mardiyyah. Use it when you need the model to fill a missing word. 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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At a glance
- Task
- Fill-Mask
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jul 25, 2026
- Updated
- Jul 25, 2026
- SHA
- c9adb33c
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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. -->
CeLLaTe-tapt-biofomer16l-tokenizer-adapted-baseline
This model is a fine-tuned version of bioformers/bioformer-16l on the Mardiyyah/TAPT_CeLLaTe2.0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.3781
- Accuracy: 0.7283
- Perplexity: 3.9672
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3407
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity |
|---|---|---|---|---|---|
| 1.9156 | 1.0 | 15 | 1.6188 | 0.7111 | 5.0472 |
| 1.8799 | 2.0 | 30 | 1.5831 | 0.7163 | 4.8698 |
| 1.801 | 3.0 | 45 | 1.5290 | 0.7222 | 4.6138 |
| 1.7444 | 4.0 | 60 | 1.4856 | 0.7248 | 4.4177 |
| 1.7086 | 5.0 | 75 | 1.4688 | 0.7270 | 4.3441 |
| 1.6495 | 6.0 | 90 | 1.4739 | 0.7238 | 4.3661 |
| 1.6387 | 7.0 | 105 | 1.4182 | 0.7269 | 4.1299 |
| 1.5975 | 8.0 | 120 | 1.4524 | 0.7229 | 4.2735 |
| 1.5862 | 9.0 | 135 | 1.3966 | 0.7306 | 4.0413 |
| 1.5551 | 10.0 | 150 | 1.3948 | 0.7332 | 4.0344 |
| 1.5185 | 11.0 | 165 | 1.3779 | 0.7324 | 3.9666 |
| 1.508 | 12.0 | 180 | 1.3371 | 0.7407 | 3.8080 |
| 1.4904 | 13.0 | 195 | 1.4013 | 0.7317 | 4.0606 |
| 1.4741 | 14.0 | 210 | 1.3646 | 0.7361 | 3.9143 |
| 1.458 | 15.0 | 225 | 1.3648 | 0.7342 | 3.9150 |
| 1.4591 | 16.0 | 240 | 1.3683 | 0.7310 | 3.9286 |
| 1.432 | 17.0 | 255 | 1.3534 | 0.7345 | 3.8704 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0
- Task
- Fill-Mask
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Languages
- en
- Created
- Jul 25, 2026
- Updated
- Jul 25, 2026