Downloads · 30 days
25
8% of all-time downloads
Baljinnyam/bert-base-multilingual-uncased-mongolian-ner
bert-base-multilingual-uncased-mongolian-ner is a token classification model from Baljinnyam. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
Downloads · 30 days
25
8% of all-time downloads
All-time downloads
308
Public
Repo size
14.5 GB
Likes
0
Public
Click a slice to open those files.
.bin709 MB · 99%
From the Hugging Face model README
This model is a fine-tuned version of bert-base-multilingual-cased 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1758 | 1.0 | 477 | 0.1095 | 0.8593 | 0.8850 | 0.8720 | 0.9648 |
| 0.0863 | 2.0 | 954 | 0.1019 | 0.8855 | 0.9055 | 0.8954 | 0.9718 |
| 0.0601 | 3.0 | 1431 | 0.1063 | 0.8950 | 0.9086 | 0.9018 | 0.9724 |
| 0.04 | 4.0 | 1908 | 0.1050 | 0.8988 | 0.9171 | 0.9078 | 0.9745 |
| 0.0289 | 5.0 | 2385 | 0.1085 | 0.9003 | 0.9185 | 0.9093 | 0.9751 |
| 0.0203 | 6.0 | 2862 | 0.1134 | 0.9052 | 0.9193 | 0.9122 | 0.9747 |
| 0.0139 | 7.0 | 3339 | 0.1314 | 0.9089 | 0.9185 | 0.9137 | 0.9746 |
| 0.0102 | 8.0 | 3816 | 0.1306 | 0.9087 | 0.9199 | 0.9143 | 0.9748 |
| 0.0072 | 9.0 | 4293 | 0.1339 | 0.9091 | 0.9229 | 0.9160 | 0.9758 |
| 0.005 | 10.0 | 4770 | 0.1378 | 0.9094 | 0.9227 | 0.9160 | 0.9758 |