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cellate2.0-tapt_base-LR_5e-05
Mardiyyah/cellate2.0-tapt_base-LR_5e-05
cellate2.0-tapt_base-LR_5e-05 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 mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Feb 24, 2026
- Updated
- Feb 24, 2026
- SHA
- 3e87cd33
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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. -->
cellate2.0-tapt_base-LR_5e-05
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1527
- Accuracy: 0.7405
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: 5e-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: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.385 | 1.0 | 4 | 1.3146 | 0.7350 |
| 1.4161 | 2.0 | 8 | 1.2337 | 0.7449 |
| 1.3044 | 3.0 | 12 | 1.2215 | 0.7331 |
| 1.3033 | 4.0 | 16 | 1.3410 | 0.7153 |
| 1.2797 | 5.0 | 20 | 1.2776 | 0.7228 |
| 1.265 | 6.0 | 24 | 1.2818 | 0.7273 |
| 1.2078 | 7.0 | 28 | 1.2216 | 0.7442 |
| 1.1611 | 8.0 | 32 | 1.3203 | 0.7321 |
| 1.1928 | 9.0 | 36 | 1.2691 | 0.7335 |
| 1.1883 | 10.0 | 40 | 1.2359 | 0.7408 |
| 1.1549 | 11.0 | 44 | 1.2654 | 0.7350 |
| 1.1768 | 12.0 | 48 | 1.2919 | 0.7224 |
| 1.1169 | 13.0 | 52 | 1.2514 | 0.7263 |
| 1.1635 | 14.0 | 56 | 1.2352 | 0.7324 |
| 1.0975 | 15.0 | 60 | 1.2906 | 0.7385 |
| 1.0897 | 16.0 | 64 | 1.2099 | 0.7337 |
| 0.9993 | 17.0 | 68 | 1.2057 | 0.7395 |
| 1.0331 | 18.0 | 72 | 1.2895 | 0.7347 |
| 1.1321 | 19.0 | 76 | 1.2824 | 0.7386 |
| 1.083 | 20.0 | 80 | 1.1527 | 0.7405 |
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
- mit
- Created
- Feb 24, 2026
- Updated
- Feb 24, 2026