Downloads · 30 days
3
16% of all-time downloads
CoLA-Fisher-V_FFN-seed20
ekiprop/CoLA-Fisher-V_FFN-seed20
CoLA-Fisher-V_FFN-seed20 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
19
Public
Repo size
57.7 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors4.4 MB · 48%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Aug 7, 2025
- Updated
- Aug 7, 2025
- SHA
- 62bf1a3a
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-seed20
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4758
- Matthews Correlation: 0.5948
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 |
|---|---|---|---|---|
| 0.6456 | 0.1866 | 50 | 0.6013 | 0.0 |
| 0.5532 | 0.3731 | 100 | 0.4724 | 0.4529 |
| 0.4908 | 0.5597 | 150 | 0.5272 | 0.4383 |
| 0.4738 | 0.7463 | 200 | 0.4705 | 0.4747 |
| 0.4427 | 0.9328 | 250 | 0.5902 | 0.3696 |
| 0.4338 | 1.1194 | 300 | 0.4165 | 0.5587 |
| 0.4063 | 1.3060 | 350 | 0.4993 | 0.4857 |
| 0.4007 | 1.4925 | 400 | 0.4360 | 0.5447 |
| 0.4259 | 1.6791 | 450 | 0.4172 | 0.5444 |
| 0.3778 | 1.8657 | 500 | 0.5127 | 0.5128 |
| 0.369 | 2.0522 | 550 | 0.4285 | 0.5699 |
| 0.3323 | 2.2388 | 600 | 0.5259 | 0.5393 |
| 0.3499 | 2.4254 | 650 | 0.4096 | 0.5869 |
| 0.3502 | 2.6119 | 700 | 0.5060 | 0.5475 |
| 0.3297 | 2.7985 | 750 | 0.4342 | 0.5783 |
| 0.3246 | 2.9851 | 800 | 0.4623 | 0.5650 |
| 0.3066 | 3.1716 | 850 | 0.4304 | 0.5790 |
| 0.2901 | 3.3582 | 900 | 0.4201 | 0.5741 |
| 0.2999 | 3.5448 | 950 | 0.5514 | 0.5577 |
| 0.2994 | 3.7313 | 1000 | 0.4279 | 0.5867 |
| 0.2898 | 3.9179 | 1050 | 0.4469 | 0.5912 |
| 0.2989 | 4.1045 | 1100 | 0.4532 | 0.5882 |
| 0.2421 | 4.2910 | 1150 | 0.4758 | 0.5948 |
| 0.2513 | 4.4776 | 1200 | 0.4784 | 0.5890 |
| 0.2655 | 4.6642 | 1250 | 0.4740 | 0.5841 |
| 0.2583 | 4.8507 | 1300 | 0.5149 | 0.5830 |
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