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67d504f 8e194f3 850230a e3a2d6c 67d504f 8e194f3 67d504f e3a2d6c 67d504f e3a2d6c 67d504f e3a2d6c 67d504f 614d0aa e3a2d6c 67d504f 57a4be3 67d504f 8e194f3 67d504f 17476f2 67d504f 8e194f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | ---
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 |