Downloads · 30 days
5
23% of all-time downloads
CoLA-GLoRA-p40-seed52
ekiprop/CoLA-GLoRA-p40-seed52
CoLA-GLoRA-p40-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. --
All-time downloads
22
Public
Repo size
68 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors4.9 MB · 50%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- 9c0bf396
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-p40-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: 0.4511
- Matthews Correlation: 0.5916
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 |
|---|---|---|---|---|
| 0.6207 | 0.1866 | 50 | 0.5858 | 0.0464 |
| 0.5367 | 0.3731 | 100 | 0.6239 | 0.3701 |
| 0.4737 | 0.5597 | 150 | 0.5161 | 0.4436 |
| 0.4654 | 0.7463 | 200 | 0.4826 | 0.4918 |
| 0.4268 | 0.9328 | 250 | 0.5026 | 0.4664 |
| 0.4148 | 1.1194 | 300 | 0.4366 | 0.5240 |
| 0.3918 | 1.3060 | 350 | 0.4665 | 0.5717 |
| 0.3967 | 1.4925 | 400 | 0.4509 | 0.5573 |
| 0.4111 | 1.6791 | 450 | 0.4156 | 0.5576 |
| 0.3695 | 1.8657 | 500 | 0.5466 | 0.4919 |
| 0.369 | 2.0522 | 550 | 0.5299 | 0.5390 |
| 0.3317 | 2.2388 | 600 | 0.4652 | 0.5576 |
| 0.3468 | 2.4254 | 650 | 0.4511 | 0.5916 |
| 0.3402 | 2.6119 | 700 | 0.4724 | 0.5600 |
| 0.3214 | 2.7985 | 750 | 0.4392 | 0.5834 |
| 0.3249 | 2.9851 | 800 | 0.4432 | 0.5660 |
| 0.3062 | 3.1716 | 850 | 0.4139 | 0.5725 |
| 0.287 | 3.3582 | 900 | 0.3998 | 0.5785 |
| 0.2906 | 3.5448 | 950 | 0.5920 | 0.5475 |
| 0.2881 | 3.7313 | 1000 | 0.4092 | 0.5823 |
| 0.312 | 3.9179 | 1050 | 0.4422 | 0.5785 |
| 0.2852 | 4.1045 | 1100 | 0.4741 | 0.5653 |
| 0.2325 | 4.2910 | 1150 | 0.5053 | 0.5735 |
| 0.2711 | 4.4776 | 1200 | 0.4919 | 0.5684 |
| 0.2655 | 4.6642 | 1250 | 0.5020 | 0.5628 |
| 0.2576 | 4.8507 | 1300 | 0.4970 | 0.5677 |
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