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CoLA-GLoRA-p50-seed52
ekiprop/CoLA-GLoRA-p50-seed52
CoLA-GLoRA-p50-seed52 is a machine learning model from ekiprop. 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 peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- b04776eb
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. -->
CoLA-GLoRA-p50-seed52
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4443
- Matthews Correlation: 0.6050
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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.6266 | 0.1866 | 50 | 0.5747 | 0.0 |
| 0.4988 | 0.3731 | 100 | 0.4457 | 0.4848 |
| 0.487 | 0.5597 | 150 | 0.4529 | 0.4755 |
| 0.4501 | 0.7463 | 200 | 0.4881 | 0.5128 |
| 0.4424 | 0.9328 | 250 | 0.5268 | 0.4922 |
| 0.4165 | 1.1194 | 300 | 0.4208 | 0.5517 |
| 0.3869 | 1.3060 | 350 | 0.4558 | 0.5534 |
| 0.3946 | 1.4925 | 400 | 0.4442 | 0.5341 |
| 0.4087 | 1.6791 | 450 | 0.4292 | 0.5577 |
| 0.3607 | 1.8657 | 500 | 0.5630 | 0.5190 |
| 0.3758 | 2.0522 | 550 | 0.4829 | 0.5586 |
| 0.3192 | 2.2388 | 600 | 0.5363 | 0.5508 |
| 0.3369 | 2.4254 | 650 | 0.4332 | 0.5685 |
| 0.3421 | 2.6119 | 700 | 0.4430 | 0.5628 |
| 0.3095 | 2.7985 | 750 | 0.4443 | 0.6050 |
| 0.3078 | 2.9851 | 800 | 0.5026 | 0.5443 |
| 0.297 | 3.1716 | 850 | 0.4220 | 0.5798 |
| 0.2684 | 3.3582 | 900 | 0.4302 | 0.5839 |
| 0.2724 | 3.5448 | 950 | 0.5745 | 0.5418 |
| 0.2696 | 3.7313 | 1000 | 0.4688 | 0.5653 |
| 0.2876 | 3.9179 | 1050 | 0.4576 | 0.5788 |
| 0.2552 | 4.1045 | 1100 | 0.4864 | 0.5658 |
| 0.229 | 4.2910 | 1150 | 0.5040 | 0.5841 |
| 0.2271 | 4.4776 | 1200 | 0.5316 | 0.5735 |
| 0.2488 | 4.6642 | 1250 | 0.5265 | 0.5705 |
| 0.2512 | 4.8507 | 1300 | 0.5143 | 0.5656 |
Framework versions
- PEFT 0.16.0
- Transformers 4.54.1
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.4
- Library
- peft
- License
- mit
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025