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CoLA-GLoRA-p30-seed30
ekiprop/CoLA-GLoRA-p30-seed30
CoLA-GLoRA-p30-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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.json4.4 MB · 49%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- 04cbdf76
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-p30-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: 0.4153
- Matthews Correlation: 0.5863
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 |
|---|---|---|---|---|
| 0.6223 | 0.1866 | 50 | 0.5847 | 0.0 |
| 0.5125 | 0.3731 | 100 | 0.4436 | 0.4982 |
| 0.4723 | 0.5597 | 150 | 0.5305 | 0.4553 |
| 0.4684 | 0.7463 | 200 | 0.4664 | 0.5106 |
| 0.4379 | 0.9328 | 250 | 0.6442 | 0.4172 |
| 0.4497 | 1.1194 | 300 | 0.4611 | 0.5306 |
| 0.4179 | 1.3060 | 350 | 0.5540 | 0.5078 |
| 0.4244 | 1.4925 | 400 | 0.4447 | 0.4943 |
| 0.4439 | 1.6791 | 450 | 0.4487 | 0.5532 |
| 0.3954 | 1.8657 | 500 | 0.5875 | 0.4831 |
| 0.3883 | 2.0522 | 550 | 0.4997 | 0.5316 |
| 0.3541 | 2.2388 | 600 | 0.4778 | 0.5610 |
| 0.3673 | 2.4254 | 650 | 0.4607 | 0.5528 |
| 0.3549 | 2.6119 | 700 | 0.4455 | 0.5709 |
| 0.3622 | 2.7985 | 750 | 0.4153 | 0.5863 |
| 0.3659 | 2.9851 | 800 | 0.4538 | 0.5573 |
| 0.3384 | 3.1716 | 850 | 0.4262 | 0.5667 |
| 0.3272 | 3.3582 | 900 | 0.4302 | 0.5720 |
| 0.3525 | 3.5448 | 950 | 0.5232 | 0.5286 |
| 0.3309 | 3.7313 | 1000 | 0.4471 | 0.5704 |
| 0.3543 | 3.9179 | 1050 | 0.4415 | 0.5677 |
| 0.3269 | 4.1045 | 1100 | 0.4394 | 0.5701 |
| 0.3025 | 4.2910 | 1150 | 0.4466 | 0.5754 |
| 0.3156 | 4.4776 | 1200 | 0.4378 | 0.5717 |
| 0.3068 | 4.6642 | 1250 | 0.4671 | 0.5602 |
| 0.3074 | 4.8507 | 1300 | 0.4795 | 0.5625 |
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