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rcds/MiniLM-swiss_citation_extraction-de-fr-it
MiniLM-swiss_citation_extraction-de-fr-it is a token classification model from rcds. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-sa-4.0.
- O - B-CITATION - I-CITATION - B-LAW - I-LAW
Downloads · 30 days
9
1% of all-time downloads
All-time downloads
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.json978 MB · 61%
How the weights are stored.
F16118M · 100%
From the Hugging Face model README
| metric | score |
|---|---|
| de_predict/_CITATION_f1 | 97.93 |
| de_predict/_CITATION_precision | 98.53 |
| de_predict/_CITATION_recall | 97.34 |
| de_predict/_LAW_f1 | 92.08 |
| de_predict/_LAW_precision | 85.99 |
| de_predict/_LAW_recall | 99.1 |
| de_predict/_accuracy_normalized | 98.8 |
| de_predict/_macro-f1 | 95.04 |
| de_predict/_macro-precision | 98.22 |
| de_predict/_macro-recall | 92.32 |
| de_predict/_micro-f1 | 94.06 |
| de_predict/_micro-precision | 98.49 |
| de_predict/_micro-recall | 90.01 |
| de_predict/_steps_per_second | 54.9 |
| de_predict/_weighted-f1 | 93.97 |
| de_predict/_weighted-precision | 98.55 |
| de_predict/_weighted-recall | 90.01 |
| fr_predict/_CITATION_f1 | 95.55 |
| fr_predict/_CITATION_precision | 96.85 |
| fr_predict/_CITATION_recall | 94.28 |
| fr_predict/_LAW_f1 | 91.01 |
| fr_predict/_LAW_precision | 83.67 |
| fr_predict/_LAW_recall | 99.76 |
| fr_predict/_accuracy_normalized | 98.31 |
| fr_predict/_macro-f1 | 93.3 |
| fr_predict/_macro-precision | 97.02 |
| fr_predict/_macro-recall | 90.3 |
| fr_predict/_micro-f1 | 92.06 |
| fr_predict/_micro-precision | 98.42 |
| fr_predict/_micro-recall | 86.47 |
| fr_predict/_steps_per_second | 59.3 |
| fr_predict/_weighted-f1 | 91.99 |
| fr_predict/_weighted-precision | 98.62 |
| fr_predict/_weighted-recall | 86.47 |
| it_predict/_CITATION_f1 | 97.04 |
| it_predict/_CITATION_precision | 97.7 |
| it_predict/_CITATION_recall | 96.39 |
| it_predict/_LAW_f1 | 90.99 |
| it_predict/_LAW_precision | 84.23 |
| it_predict/_LAW_recall | 98.94 |
| it_predict/_accuracy_normalized | 98.92 |
| it_predict/_macro-f1 | 94.13 |
| it_predict/_macro-precision | 97.66 |
| it_predict/_macro-recall | 91.2 |
| it_predict/_micro-f1 | 93.11 |
| it_predict/_micro-precision | 98.03 |
| it_predict/_micro-recall | 88.67 |
| it_predict/_steps_per_second | 56.3 |
| it_predict/_weighted-f1 | 93 |
| it_predict/_weighted-precision | 98.13 |
| it_predict/_weighted-recall | 88.67 |
| predict/_CITATION_f1 | 97.36 |
| predict/_CITATION_precision | 98.11 |
| predict/_CITATION_recall | 96.62 |
| predict/_LAW_f1 | 91.68 |
| predict/_LAW_precision | 85.15 |
| predict/_LAW_recall | 99.3 |
| predict/_accuracy_normalized | 98.68 |
| predict/_macro-f1 | 94.56 |
| predict/_macro-precision | 97.96 |
| predict/_macro-recall | 91.7 |
| predict/_micro-f1 | 93.43 |
| predict/_micro-precision | 98.45 |
| predict/_micro-recall | 88.91 |
| predict/_steps_per_second | 55.7 |
| predict/_weighted-f1 | 93.34 |
| predict/_weighted-precision | 98.54 |
| predict/_weighted-recall | 88.91 |
| predict_samples | 28218 |