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muhtasham/tajberto-ner
tajberto-ner is a token classification model from muhtasham. Use it when you need labels on individual words, such as names. It is set up for transformers.
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 muhtasham/TajBERTo on the wikiann dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 2.0 | 50 | 0.6171 | 0.1667 | 0.2885 | 0.2113 | 0.7646 |
| No log | 4.0 | 100 | 0.4733 | 0.2824 | 0.4615 | 0.3504 | 0.8344 |
| No log | 6.0 | 150 | 0.3857 | 0.3372 | 0.5577 | 0.4203 | 0.8589 |
| No log | 8.0 | 200 | 0.4523 | 0.4519 | 0.5865 | 0.5105 | 0.8765 |
| No log | 10.0 | 250 | 0.3870 | 0.44 | 0.6346 | 0.5197 | 0.8834 |
| No log | 12.0 | 300 | 0.4512 | 0.5267 | 0.6635 | 0.5872 | 0.8865 |
| No log | 14.0 | 350 | 0.4934 | 0.4789 | 0.6538 | 0.5528 | 0.8819 |
| No log | 16.0 | 400 | 0.4924 | 0.4783 | 0.6346 | 0.5455 | 0.8842 |
| No log | 18.0 | 450 | 0.5355 | 0.4595 | 0.6538 | 0.5397 | 0.8788 |
| 0.1682 | 20.0 | 500 | 0.5440 | 0.5547 | 0.6827 | 0.6121 | 0.8942 |
| 0.1682 | 22.0 | 550 | 0.5299 | 0.5794 | 0.7019 | 0.6348 | 0.9003 |
| 0.1682 | 24.0 | 600 | 0.5735 | 0.5691 | 0.6731 | 0.6167 | 0.8926 |
| 0.1682 | 26.0 | 650 | 0.6027 | 0.5833 | 0.6731 | 0.6250 | 0.8796 |
| 0.1682 | 28.0 | 700 | 0.6119 | 0.568 | 0.6827 | 0.6201 | 0.8934 |
| 0.1682 | 30.0 | 750 | 0.6098 | 0.5635 | 0.6827 | 0.6174 | 0.8911 |
| 0.1682 | 32.0 | 800 | 0.6237 | 0.5469 | 0.6731 | 0.6034 | 0.8834 |
| 0.1682 | 34.0 | 850 | 0.6215 | 0.5530 | 0.7019 | 0.6186 | 0.8842 |
| 0.1682 | 36.0 | 900 | 0.6179 | 0.5802 | 0.7308 | 0.6468 | 0.8888 |
| 0.1682 | 38.0 | 950 | 0.6201 | 0.5373 | 0.6923 | 0.6050 | 0.8873 |
| 0.0007 | 40.0 | 1000 | 0.6114 | 0.5952 | 0.7212 | 0.6522 | 0.8911 |
| 0.0007 | 42.0 | 1050 | 0.6073 | 0.5625 | 0.6923 | 0.6207 | 0.8896 |
| 0.0007 | 44.0 | 1100 | 0.6327 | 0.5620 | 0.6538 | 0.6044 | 0.8896 |
| 0.0007 | 46.0 | 1150 | 0.6129 | 0.576 | 0.6923 | 0.6288 | 0.8934 |