Downloads · 30 days
7
12% of all-time downloads
jamie613/custom_BERT_NER
custom_BERT_NER is a token classification model from jamie613. 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.
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.207071 - Perf P: 0.829268 - Perf R: 0.944444 - Inst P: 0.933…
Downloads · 30 days
7
12% of all-time downloads
All-time downloads
58
Public
Parameters
177M
21.3 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors709 MB · 99%
From the Hugging Face model README
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
This model is for identifying performers, instrumentation, and composers of the music played in the concert from a brief introduction of a concert.
Tags:<br> <b>PERF</b>: Performer(s)<br> <b>INST</b>: Instrumentation<br> <b>COMP</b>: Composer(s)<br> <b>MUSIC</b>: Music title(s)<br> <b>PER</b>: Other name(s)<br> <b>OTH</b>: Other instrument(s)<br> <b>OTHP</b>: Other music title(s)<br> <b>ORG</b>: Companies, festivals, orchetras, ensembles, etc.<br> <b>LOC</b>: Country names, halls, etc.<br> <b>MISC</b>: Other miscellaneous nouns, including competitions.<br>
This model is trained ane evaluated on a custome dataset: jamie613/custom_NER<br> The set contains 150 samples of concert introductions in Mandarine.<br> The dataset is divide into training set (135 samples) and evaluation set (15 samples).
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Perf P | Perf R | Inst P | Inst R | Comp P | Comp R | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8629 | 1.0 | 135 | 0.3555 | 0.6951 | 0.7917 | 0.5176 | 0.6875 | 0.8455 | 0.7815 | 0.6913 | 0.6095 | 0.6478 | 0.8848 |
| 0.2867 | 2.0 | 270 | 0.2387 | 0.6275 | 0.8889 | 0.7719 | 0.6875 | 0.93 | 0.7815 | 0.7778 | 0.7663 | 0.7720 | 0.9265 |
| 0.1715 | 3.0 | 405 | 0.1832 | 0.8193 | 0.9444 | 0.875 | 0.7656 | 0.8636 | 0.7983 | 0.8186 | 0.8077 | 0.8131 | 0.9446 |
| 0.1027 | 4.0 | 540 | 0.2056 | 0.875 | 0.875 | 0.75 | 0.7969 | 0.9630 | 0.8739 | 0.8254 | 0.8180 | 0.8217 | 0.9441 |
| 0.0707 | 5.0 | 675 | 0.2007 | 0.825 | 0.9167 | 0.9245 | 0.7656 | 0.9423 | 0.8235 | 0.8378 | 0.8328 | 0.8353 | 0.9468 |
| 0.0517 | 6.0 | 810 | 0.2402 | 0.8415 | 0.9583 | 0.8889 | 0.75 | 0.93 | 0.7815 | 0.8311 | 0.8225 | 0.8268 | 0.9403 |
| 0.0359 | 7.0 | 945 | 0.2071 | 0.8293 | 0.9444 | 0.9333 | 0.875 | 0.9626 | 0.8655 | 0.8627 | 0.8462 | 0.8544 | 0.9523 |
| 0.0269 | 8.0 | 1080 | 0.2171 | 0.8415 | 0.9583 | 0.9608 | 0.7656 | 0.9604 | 0.8151 | 0.8411 | 0.8299 | 0.8354 | 0.9486 |
| 0.0196 | 9.0 | 1215 | 0.2317 | 0.8718 | 0.9444 | 0.8788 | 0.9062 | 0.9558 | 0.9076 | 0.8505 | 0.8417 | 0.8461 | 0.9510 |
| 0.0126 | 10.0 | 1350 | 0.2578 | 0.8161 | 0.9861 | 0.8923 | 0.9062 | 0.9537 | 0.8655 | 0.8495 | 0.8432 | 0.8463 | 0.9470 |