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rajevan123/STS-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-r-mid
STS-Lora-Fine-Tuning-Capstone-roberta-base-filtered-137-with-higher-r-mid is a machine learning model from rajevan123. 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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From the Hugging Face model README
This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 113 | 1.0392 | 0.4551 |
| No log | 2.0 | 226 | 1.0228 | 0.4944 |
| No log | 3.0 | 339 | 0.9681 | 0.5225 |
| No log | 4.0 | 452 | 0.8498 | 0.6404 |
| 0.8868 | 5.0 | 565 | 0.7953 | 0.6704 |
| 0.8868 | 6.0 | 678 | 0.7626 | 0.6723 |
| 0.8868 | 7.0 | 791 | 0.7913 | 0.6704 |
| 0.8868 | 8.0 | 904 | 0.7607 | 0.6723 |
| 0.6501 | 9.0 | 1017 | 0.7982 | 0.6873 |
| 0.6501 | 10.0 | 1130 | 0.7419 | 0.6685 |
| 0.6501 | 11.0 | 1243 | 0.7451 | 0.6873 |
| 0.6501 | 12.0 | 1356 | 0.7471 | 0.6760 |
| 0.6501 | 13.0 | 1469 | 0.7549 | 0.6816 |
| 0.5977 | 14.0 | 1582 | 0.7364 | 0.6835 |
| 0.5977 | 15.0 | 1695 | 0.7431 | 0.6760 |
| 0.5977 | 16.0 | 1808 | 0.7545 | 0.6742 |
| 0.5977 | 17.0 | 1921 | 0.7556 | 0.6873 |
| 0.5673 | 18.0 | 2034 | 0.7427 | 0.6873 |
| 0.5673 | 19.0 | 2147 | 0.7442 | 0.6873 |
| 0.5673 | 20.0 | 2260 | 0.7600 | 0.6798 |
| 0.5673 | 21.0 | 2373 | 0.7381 | 0.6854 |
| 0.5673 | 22.0 | 2486 | 0.7480 | 0.6873 |
| 0.5561 | 23.0 | 2599 | 0.7489 | 0.6854 |
| 0.5561 | 24.0 | 2712 | 0.7481 | 0.6873 |
| 0.5561 | 25.0 | 2825 | 0.7470 | 0.6873 |
| 0.5561 | 26.0 | 2938 | 0.7530 | 0.6891 |
| 0.5381 | 27.0 | 3051 | 0.7455 | 0.6854 |
| 0.5381 | 28.0 | 3164 | 0.7478 | 0.6891 |
| 0.5381 | 29.0 | 3277 | 0.7483 | 0.6891 |
| 0.5381 | 30.0 | 3390 | 0.7478 | 0.6891 |