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jacquesle/bert-base-cased-NER-favsbot
bert-base-cased-NER-favsbot is a token classification model from jacquesle. 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
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From the Hugging Face model README
This model is a fine-tuned version of bert-base-cased on the favsbot 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 | 10 | 1.7643 | 0.0 | 0.0 | 0.0 | 0.5694 |
| No log | 2.0 | 20 | 1.1420 | 0.0 | 0.0 | 0.0 | 0.5833 |
| No log | 3.0 | 30 | 0.7946 | 0.9375 | 0.6 | 0.7317 | 0.8056 |
| No log | 4.0 | 40 | 0.5625 | 0.8182 | 0.72 | 0.7660 | 0.8611 |
| No log | 5.0 | 50 | 0.4217 | 0.8148 | 0.88 | 0.8462 | 0.9306 |
| No log | 6.0 | 60 | 0.3082 | 0.8519 | 0.92 | 0.8846 | 0.9444 |
| No log | 7.0 | 70 | 0.2386 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 8.0 | 80 | 0.1965 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 9.0 | 90 | 0.1626 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 10.0 | 100 | 0.1465 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 11.0 | 110 | 0.1314 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 12.0 | 120 | 0.1215 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 13.0 | 130 | 0.1160 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 14.0 | 140 | 0.1104 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 15.0 | 150 | 0.1050 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 16.0 | 160 | 0.1012 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 17.0 | 170 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 18.0 | 180 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 19.0 | 190 | 0.0995 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 20.0 | 200 | 0.0992 | 0.8571 | 0.96 | 0.9057 | 0.9583 |