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CoLA-Fisher-GLoRA-p10-seed10
ekiprop/CoLA-Fisher-GLoRA-p10-seed10
CoLA-Fisher-GLoRA-p10-seed10 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 · 58%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- 925f4d4d
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-Fisher-GLoRA-p10-seed10
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4967
- Matthews Correlation: 0.3562
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 |
|---|---|---|---|---|
| 0.6354 | 0.1866 | 50 | 0.6130 | 0.0 |
| 0.6033 | 0.3731 | 100 | 0.6118 | 0.0 |
| 0.5973 | 0.5597 | 150 | 0.6021 | 0.0 |
| 0.5855 | 0.7463 | 200 | 0.5990 | 0.0 |
| 0.5928 | 0.9328 | 250 | 0.5902 | 0.0 |
| 0.59 | 1.1194 | 300 | 0.5862 | 0.0 |
| 0.5722 | 1.3060 | 350 | 0.5650 | 0.0976 |
| 0.561 | 1.4925 | 400 | 0.5635 | 0.1039 |
| 0.5584 | 1.6791 | 450 | 0.5402 | 0.1699 |
| 0.5426 | 1.8657 | 500 | 0.5514 | 0.1420 |
| 0.5396 | 2.0522 | 550 | 0.5222 | 0.2615 |
| 0.5437 | 2.2388 | 600 | 0.5263 | 0.2163 |
| 0.5243 | 2.4254 | 650 | 0.5470 | 0.1881 |
| 0.522 | 2.6119 | 700 | 0.5292 | 0.2538 |
| 0.5083 | 2.7985 | 750 | 0.5482 | 0.2279 |
| 0.5217 | 2.9851 | 800 | 0.5068 | 0.3101 |
| 0.5139 | 3.1716 | 850 | 0.4967 | 0.3562 |
| 0.5155 | 3.3582 | 900 | 0.5071 | 0.3096 |
| 0.5087 | 3.5448 | 950 | 0.5648 | 0.2327 |
| 0.5124 | 3.7313 | 1000 | 0.5197 | 0.2943 |
| 0.5216 | 3.9179 | 1050 | 0.5231 | 0.2923 |
| 0.4978 | 4.1045 | 1100 | 0.5130 | 0.3114 |
| 0.4935 | 4.2910 | 1150 | 0.5099 | 0.3143 |
| 0.5007 | 4.4776 | 1200 | 0.5042 | 0.3168 |
| 0.5035 | 4.6642 | 1250 | 0.5170 | 0.3185 |
| 0.4941 | 4.8507 | 1300 | 0.5207 | 0.3121 |
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