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yanekyuk/bert-uncased-keyword-discriminator
bert-uncased-keyword-discriminator is a token classification model from yanekyuk. 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. --
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From the Hugging Face model README
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
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More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1849 | 1.0 | 1875 | 0.1323 | 0.7039 | 0.7428 | 0.9488 | 0.7228 | 0.7522 | 0.8166 | 0.7831 | 0.6268 | 0.6332 | 0.6300 |
| 0.1357 | 2.0 | 3750 | 0.1132 | 0.7581 | 0.8024 | 0.9592 | 0.7796 | 0.7948 | 0.8785 | 0.8346 | 0.6971 | 0.6895 | 0.6933 |
| 0.0965 | 3.0 | 5625 | 0.1033 | 0.8086 | 0.7980 | 0.9646 | 0.8032 | 0.8410 | 0.8592 | 0.8500 | 0.7560 | 0.7071 | 0.7307 |
| 0.0713 | 4.0 | 7500 | 0.1040 | 0.8181 | 0.8435 | 0.9683 | 0.8306 | 0.8526 | 0.8906 | 0.8712 | 0.7652 | 0.7736 | 0.7694 |
| 0.0525 | 5.0 | 9375 | 0.1126 | 0.8150 | 0.8633 | 0.9689 | 0.8385 | 0.8495 | 0.9064 | 0.8770 | 0.7629 | 0.7993 | 0.7807 |
| 0.0386 | 6.0 | 11250 | 0.1183 | 0.8374 | 0.8678 | 0.9719 | 0.8523 | 0.8709 | 0.9020 | 0.8862 | 0.7877 | 0.8170 | 0.8021 |
| 0.03 | 7.0 | 13125 | 0.1237 | 0.8369 | 0.8707 | 0.9723 | 0.8535 | 0.8657 | 0.9079 | 0.8863 | 0.7934 | 0.8155 | 0.8043 |
| 0.0244 | 8.0 | 15000 | 0.1296 | 0.8439 | 0.8722 | 0.9727 | 0.8578 | 0.8723 | 0.9077 | 0.8896 | 0.8010 | 0.8196 | 0.8102 |