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CoLA-Fisher-V_FFN-seed10
ekiprop/CoLA-Fisher-V_FFN-seed10
CoLA-Fisher-V_FFN-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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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- 7edc92b3
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-V_FFN-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: 0.4912
- Matthews Correlation: 0.5918
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 |
|---|---|---|---|---|
| 0.6389 | 0.1866 | 50 | 0.5865 | 0.0 |
| 0.5365 | 0.3731 | 100 | 0.4860 | 0.4283 |
| 0.4805 | 0.5597 | 150 | 0.5114 | 0.4582 |
| 0.478 | 0.7463 | 200 | 0.4744 | 0.4775 |
| 0.4336 | 0.9328 | 250 | 0.5823 | 0.3969 |
| 0.428 | 1.1194 | 300 | 0.4392 | 0.5238 |
| 0.3964 | 1.3060 | 350 | 0.4628 | 0.5130 |
| 0.3891 | 1.4925 | 400 | 0.4544 | 0.5288 |
| 0.4109 | 1.6791 | 450 | 0.4600 | 0.5285 |
| 0.3754 | 1.8657 | 500 | 0.4966 | 0.5259 |
| 0.3691 | 2.0522 | 550 | 0.4552 | 0.5501 |
| 0.3222 | 2.2388 | 600 | 0.5324 | 0.5370 |
| 0.3367 | 2.4254 | 650 | 0.4224 | 0.5570 |
| 0.3504 | 2.6119 | 700 | 0.5123 | 0.5154 |
| 0.322 | 2.7985 | 750 | 0.4676 | 0.5782 |
| 0.3239 | 2.9851 | 800 | 0.4776 | 0.5366 |
| 0.3065 | 3.1716 | 850 | 0.4361 | 0.5664 |
| 0.2837 | 3.3582 | 900 | 0.4228 | 0.5744 |
| 0.2956 | 3.5448 | 950 | 0.5857 | 0.5474 |
| 0.2863 | 3.7313 | 1000 | 0.4549 | 0.5769 |
| 0.2897 | 3.9179 | 1050 | 0.4464 | 0.5728 |
| 0.291 | 4.1045 | 1100 | 0.4580 | 0.5731 |
| 0.2352 | 4.2910 | 1150 | 0.4923 | 0.5848 |
| 0.2451 | 4.4776 | 1200 | 0.5068 | 0.5863 |
| 0.2621 | 4.6642 | 1250 | 0.4912 | 0.5918 |
| 0.2515 | 4.8507 | 1300 | 0.5240 | 0.5781 |
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