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CoLA-GLoRA-p50-seed30
ekiprop/CoLA-GLoRA-p50-seed30
CoLA-GLoRA-p50-seed30 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
- 3137d6e2
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-seed30
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4442
- Matthews Correlation: 0.6083
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 |
|---|---|---|---|---|
| 0.6297 | 0.1866 | 50 | 0.5697 | 0.0 |
| 0.5357 | 0.3731 | 100 | 0.4897 | 0.3920 |
| 0.4776 | 0.5597 | 150 | 0.5203 | 0.4668 |
| 0.4538 | 0.7463 | 200 | 0.5058 | 0.4748 |
| 0.4398 | 0.9328 | 250 | 0.5014 | 0.4524 |
| 0.4184 | 1.1194 | 300 | 0.4886 | 0.5019 |
| 0.3972 | 1.3060 | 350 | 0.4702 | 0.5528 |
| 0.3917 | 1.4925 | 400 | 0.4335 | 0.5626 |
| 0.4045 | 1.6791 | 450 | 0.4313 | 0.5783 |
| 0.3646 | 1.8657 | 500 | 0.5012 | 0.5264 |
| 0.3538 | 2.0522 | 550 | 0.5180 | 0.5496 |
| 0.3124 | 2.2388 | 600 | 0.5117 | 0.5547 |
| 0.3341 | 2.4254 | 650 | 0.4402 | 0.5713 |
| 0.3275 | 2.6119 | 700 | 0.4720 | 0.5523 |
| 0.2956 | 2.7985 | 750 | 0.4536 | 0.5758 |
| 0.3142 | 2.9851 | 800 | 0.4608 | 0.5702 |
| 0.2982 | 3.1716 | 850 | 0.4332 | 0.6027 |
| 0.2741 | 3.3582 | 900 | 0.4357 | 0.5987 |
| 0.2702 | 3.5448 | 950 | 0.6204 | 0.5217 |
| 0.2683 | 3.7313 | 1000 | 0.4442 | 0.6083 |
| 0.2893 | 3.9179 | 1050 | 0.4678 | 0.5912 |
| 0.2613 | 4.1045 | 1100 | 0.5003 | 0.5728 |
| 0.228 | 4.2910 | 1150 | 0.5208 | 0.5887 |
| 0.2321 | 4.4776 | 1200 | 0.5419 | 0.5855 |
| 0.2455 | 4.6642 | 1250 | 0.5248 | 0.5907 |
| 0.2356 | 4.8507 | 1300 | 0.5356 | 0.5702 |
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