Downloads · 30 days
9
4% of all-time downloads
wdevinsp/indobert-base-uncased-finetuned-digestive-qna
indobert-base-uncased-finetuned-digestive-qna is a question answering model from wdevinsp. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as mit.
probably proofread and complete it, then remove this comment. --
Downloads · 30 days
9
4% of all-time downloads
All-time downloads
253
Public
Repo size
32.6 GB
Likes
0
Public
Click a slice to open those files.
.h5440 MB · 100%
From the Hugging Face model README
This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown 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:
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch |
|---|---|---|---|
| 3.1872 | 0.3564 | 0.3733 | 0 |
| 2.1076 | 0.4189 | 0.4088 | 1 |
| 1.6782 | 0.4443 | 0.5507 | 2 |
| 1.1711 | 0.5507 | 0.7111 | 3 |
| 0.6231 | 0.7720 | 0.8429 | 4 |
| 0.4296 | 0.8463 | 0.8902 | 5 |
| 0.2544 | 0.9189 | 0.9409 | 6 |
| 0.1853 | 0.9358 | 0.9510 | 7 |
| 0.1411 | 0.9544 | 0.9578 | 8 |
| 0.1215 | 0.9493 | 0.9662 | 9 |
| 0.1141 | 0.9645 | 0.9628 | 10 |
| 0.1195 | 0.9561 | 0.9696 | 11 |
| 0.0851 | 0.9662 | 0.9645 | 12 |
| 0.1255 | 0.9527 | 0.9493 | 13 |
| 0.0897 | 0.9595 | 0.9730 | 14 |
| 0.0860 | 0.9578 | 0.9696 | 15 |
| 0.0710 | 0.9595 | 0.9713 | 16 |
| 0.0669 | 0.9628 | 0.9713 | 17 |
| 0.0634 | 0.9797 | 0.9730 | 18 |
| 0.0765 | 0.9662 | 0.9814 | 19 |
| 0.0732 | 0.9679 | 0.9730 | 20 |
| 0.0586 | 0.9696 | 0.9713 | 21 |
| 0.0572 | 0.9679 | 0.9764 | 22 |
| 0.0518 | 0.9747 | 0.9764 | 23 |
| 0.0500 | 0.9713 | 0.9764 | 24 |
| 0.0464 | 0.9696 | 0.9713 | 25 |
| 0.0470 | 0.9814 | 0.9730 | 26 |
| 0.0575 | 0.9797 | 0.9764 | 27 |
| 0.0660 | 0.9679 | 0.9696 | 28 |
| 0.0576 | 0.9713 | 0.9696 | 29 |
| 0.0515 | 0.9696 | 0.9747 | 30 |
| 0.0547 | 0.9780 | 0.9696 | 31 |
| 0.0430 | 0.9730 | 0.9780 | 32 |
| 0.0408 | 0.9814 | 0.9747 | 33 |
| 0.0475 | 0.9747 | 0.9747 | 34 |
| 0.0536 | 0.9764 | 0.9797 | 35 |
| 0.0494 | 0.9696 | 0.9730 | 36 |
| 0.0391 | 0.9814 | 0.9814 | 37 |