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WhartonDS_RegressionModel
KanWasTaken/WhartonDS_RegressionModel
WhartonDS_RegressionModel is a machine learning model from KanWasTaken. 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 transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- feature_grouping
- Access
- Public
- Created
- Feb 15, 2025
- Updated
- Feb 17, 2025
- SHA
- acb1c5b5
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. -->
WhartonDS_RegressionModel
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0086
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: 1e-05
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 60
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0523 | 1.0 | 24 | 0.0548 |
| 0.0402 | 2.0 | 48 | 0.0443 |
| 0.0298 | 3.0 | 72 | 0.0396 |
| 0.0218 | 4.0 | 96 | 0.0325 |
| 0.0167 | 5.0 | 120 | 0.0253 |
| 0.0131 | 6.0 | 144 | 0.0204 |
| 0.0112 | 7.0 | 168 | 0.0153 |
| 0.0104 | 8.0 | 192 | 0.0119 |
| 0.0099 | 9.0 | 216 | 0.0110 |
| 0.0097 | 10.0 | 240 | 0.0101 |
| 0.0095 | 11.0 | 264 | 0.0126 |
| 0.0094 | 12.0 | 288 | 0.0097 |
| 0.0094 | 13.0 | 312 | 0.0104 |
| 0.0093 | 14.0 | 336 | 0.0096 |
| 0.0092 | 15.0 | 360 | 0.0095 |
| 0.0093 | 16.0 | 384 | 0.0095 |
| 0.0091 | 17.0 | 408 | 0.0097 |
| 0.0091 | 18.0 | 432 | 0.0091 |
| 0.0091 | 19.0 | 456 | 0.0098 |
| 0.0091 | 20.0 | 480 | 0.0090 |
| 0.0091 | 21.0 | 504 | 0.0092 |
| 0.009 | 22.0 | 528 | 0.0096 |
| 0.009 | 23.0 | 552 | 0.0090 |
| 0.009 | 24.0 | 576 | 0.0097 |
| 0.0089 | 25.0 | 600 | 0.0094 |
| 0.009 | 26.0 | 624 | 0.0091 |
| 0.009 | 27.0 | 648 | 0.0092 |
| 0.0089 | 28.0 | 672 | 0.0091 |
| 0.0088 | 29.0 | 696 | 0.0090 |
| 0.0089 | 30.0 | 720 | 0.0088 |
| 0.0088 | 31.0 | 744 | 0.0089 |
| 0.0089 | 32.0 | 768 | 0.0088 |
| 0.0089 | 33.0 | 792 | 0.0088 |
| 0.0089 | 34.0 | 816 | 0.0089 |
| 0.0089 | 35.0 | 840 | 0.0088 |
| 0.0088 | 36.0 | 864 | 0.0088 |
| 0.0088 | 37.0 | 888 | 0.0088 |
| 0.0088 | 38.0 | 912 | 0.0087 |
| 0.0088 | 39.0 | 936 | 0.0088 |
| 0.0088 | 40.0 | 960 | 0.0090 |
| 0.0088 | 41.0 | 984 | 0.0086 |
| 0.0087 | 42.0 | 1008 | 0.0086 |
| 0.0088 | 43.0 | 1032 | 0.0087 |
| 0.0088 | 44.0 | 1056 | 0.0086 |
| 0.0088 | 45.0 | 1080 | 0.0087 |
| 0.0088 | 46.0 | 1104 | 0.0086 |
| 0.0088 | 47.0 | 1128 | 0.0087 |
| 0.0088 | 48.0 | 1152 | 0.0086 |
| 0.0088 | 49.0 | 1176 | 0.0086 |
| 0.0088 | 50.0 | 1200 | 0.0086 |
| 0.0088 | 51.0 | 1224 | 0.0086 |
| 0.0087 | 52.0 | 1248 | 0.0086 |
| 0.0088 | 53.0 | 1272 | 0.0086 |
| 0.0088 | 54.0 | 1296 | 0.0086 |
| 0.0087 | 55.0 | 1320 | 0.0086 |
| 0.0088 | 56.0 | 1344 | 0.0086 |
| 0.0088 | 57.0 | 1368 | 0.0086 |
| 0.0088 | 58.0 | 1392 | 0.0086 |
| 0.0088 | 59.0 | 1416 | 0.0086 |
| 0.0088 | 60.0 | 1440 | 0.0086 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Library
- transformers
- Type
- feature_grouping
- Created
- Feb 15, 2025
- Updated
- Feb 17, 2025