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davanstrien/deberta-v3-base_fine_tuned_food_ner
deberta-v3-base_fine_tuned_food_ner is a token classification model from davanstrien. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
Downloads · 30 days
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From the Hugging Face model README
This model is a fine-tuned version of microsoft/deberta-v3-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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 40 | 0.8425 | 0.8323 | 0.8323 | 0.8323 | 0.8073 |
| No log | 2.0 | 80 | 0.5533 | 0.8703 | 0.8941 | 0.8820 | 0.8731 |
| No log | 3.0 | 120 | 0.4855 | 0.8771 | 0.9109 | 0.8937 | 0.8797 |
| No log | 4.0 | 160 | 0.4238 | 0.8949 | 0.9222 | 0.9083 | 0.8964 |
| No log | 5.0 | 200 | 0.4176 | 0.9048 | 0.9302 | 0.9173 | 0.9008 |
| No log | 6.0 | 240 | 0.4127 | 0.9065 | 0.9342 | 0.9202 | 0.9004 |
| No log | 7.0 | 280 | 0.4409 | 0.9294 | 0.9302 | 0.9298 | 0.9043 |
| No log | 8.0 | 320 | 0.3971 | 0.9129 | 0.9334 | 0.9230 | 0.9061 |
| No log | 9.0 | 360 | 0.3941 | 0.9112 | 0.9390 | 0.9249 | 0.9061 |
| No log | 10.0 | 400 | 0.4069 | 0.9233 | 0.9366 | 0.9299 | 0.9148 |
| No log | 11.0 | 440 | 0.4039 | 0.9213 | 0.9390 | 0.9300 | 0.9162 |
| No log | 12.0 | 480 | 0.4000 | 0.9126 | 0.9470 | 0.9295 | 0.9113 |
| 0.3799 | 13.0 | 520 | 0.4126 | 0.9323 | 0.9390 | 0.9356 | 0.9179 |
| 0.3799 | 14.0 | 560 | 0.4076 | 0.9272 | 0.9398 | 0.9334 | 0.9140 |
| 0.3799 | 15.0 | 600 | 0.4129 | 0.9317 | 0.9414 | 0.9365 | 0.9188 |
| 0.3799 | 16.0 | 640 | 0.4000 | 0.9239 | 0.9446 | 0.9341 | 0.9162 |
| 0.3799 | 17.0 | 680 | 0.4098 | 0.9267 | 0.9438 | 0.9352 | 0.9179 |
| 0.3799 | 18.0 | 720 | 0.4110 | 0.9232 | 0.9454 | 0.9342 | 0.9188 |
| 0.3799 | 19.0 | 760 | 0.4202 | 0.9275 | 0.9446 | 0.9360 | 0.9183 |
| 0.3799 | 20.0 | 800 | 0.4164 | 0.9268 | 0.9446 | 0.9356 | 0.9197 |