| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-multilingual-cased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: custom_BERT_NER |
| | results: [] |
| | datasets: |
| | - jamie613/custom_NER |
| | widget: |
| | - text: >- |
| | 20世紀以來作曲家們積極拓展器樂演奏的極限，開發新的樂器演奏方式與音色，形成新的音響體驗。本次音樂會以「日本」為主題，選擇演出多位日裔作曲家的作品，也納入俄國作曲家Tchesnokov的《日本狂想曲》，和日治時期臺灣作曲家江文也的《慶典奏鳴曲》。每首作品使用不同的演奏技巧，呈現長笛演奏的豐富多樣性，以及演奏家們的極佳詮釋能力和長年合作的默契。 |
| | - text: >- |
| | 作為磨練技巧的工具，練習曲用不同方式，重複讓彈奏者練習特定技巧。聽起來是枯燥的苦功，即便如此，許多題為「練習曲」的作品，已離開琴房，成為音樂會中的精彩曲目。鋼琴博士林聖縈對於練習曲這獨特的現象感到有趣，因此規劃本次節目，以德布西的十二首鋼琴練習曲為主，穿插其他偉大鋼琴作曲家的練習曲，這些不寫情、不畫景的鋼琴獨奏作品，勾勒出鋼琴獨奏會另一種風情。 |
| | 演出曲目： 巴赫 / 布梭尼：D小調觸技曲與賦格，作品565 Bach / Busoni: Toccata and Fugue in D Minor, |
| | BWV 565 徹爾尼：C大調練習曲，作品299之9 Czerny: The School of Velocity, Op. 299, No. 9 in |
| | C Major 克拉莫：E大調練習曲，選自84首鋼琴練習曲，作品30之41 Cramer: 84 Etudes for Piano, Op. 30, |
| | No. 41 in E Major 德布西：12首練習曲 Debussy: Douze Études 斯克里亞賓：升C小調練習曲，作品2之1 |
| | Scriabin: Étude in C-sharp Minor, Op. 2, No.1 李斯特：E大調練習曲，選自帕格尼尼練習曲，作品141之4 |
| | Liszt: Grandes Études de Paganini, S. 141, No. 4 in E Major |
| | 蕭邦：降A大調練習曲，作品25之1 Chopin: Étude in A-flat Major, Op. 25, No. 1 |
| | - text: >- |
| | 鋼琴家列夫席茲（Konstantin Lifschitz）五歲時，父母將他送到著名的莫斯科格涅辛音樂中學的特殊班（Moscow Gnessin |
| | Special Middle School of Music），向柴琳克曼（Tatiana |
| | Zelikman）學習鋼琴。之後列夫席茲曾經向顧德曼（Theodor Gutmann）、特洛普（Vladimir Tropp）、布蘭德爾（Alfred |
| | Brendel）、傅聰（Fou T'song）、富萊雪（Leon Fleisher）、杜蕾克（Rosalyn |
| | Tureck）等鋼琴家學習。1994年，列夫席茲從格涅辛學校畢業，他在畢業音樂會上彈奏了巴赫的《郭德堡變奏曲》，日本Denon哥倫比亞唱片公司聽到這位當時17歲小夥子彈奏出情感詮釋相當纖細的巴赫，大為驚艷，立即將這份演奏灌錄成唱片。這份錄音在1996年發行，立即入圍當年的葛萊美獎，《紐約時報》的樂評羅斯史坦（Edward |
| | Rothstein）更是大為讚揚列夫席茲的演奏：「這是繼顧爾德之後，最具影響力的《郭德堡變奏曲》鋼琴詮釋。」9月26日貝多芬：f小調第一號鋼琴奏鳴曲，作品2之1 |
| | L. v. Beethoven: Piano Sonata No . 1 in f minor, Op. 2 No. 1 |
| | 貝多芬：A大調第二號鋼琴奏鳴曲，作品2之2 L. v. Beethoven: Piano Sonata No. 2 in A Major, Op. 2 |
| | No. 2 ── 中 場 休 息 ── 貝多芬：C大調第三號鋼琴奏鳴曲，作品2之3 L. v. Beethoven: Piano Sonata No. |
| | 3 in C Major, Op. 2 No. 3 貝多芬：降E大調第四號鋼琴奏鳴曲《大奏鳴曲》，作品7 L. v. Beethoven: Piano |
| | Sonata No. 4 in E-flat Major 'Grand Sonata', Op. 7 |
| | language: |
| | - zh |
| | --- |
| | |
| | # custom_BERT_NER |
| |
|
| | This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/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: 0.933333 |
| | - Inst R: 0.875000 |
| | - Comp P: 0.962617 |
| | - Comp R: 0.865546 |
| | - Precision: 0.862745 |
| | - Recall: 0.846154 |
| | - F1: 0.854369 |
| | - Accuracy: 0.952260 |
| |
|
| | ## Model description |
| |
|
| | 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> |
| |
|
| | ## Training and evaluation data |
| |
|
| | This model is trained ane evaluated on a custome dataset: [jamie613/custom_NER](https://huggingface.co/datasets/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). |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 50 |
| | - metric_for_best_model = 'eval_f1' |
| | - greater_is_better = True |
| | - load_best_model_at_end = True |
| | - early_stoping_patience = 3 |
| |
|
| | ### Training results |
| |
|
| | | 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 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.40.0 |
| | - Pytorch 2.2.1+cu121 |
| | - Datasets 2.19.0 |
| | - Tokenizers 0.19.1 |
