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scenario-kd-po-ner-full_data-univner_full66

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  1. README.md +60 -32
  2. config.json +1 -1
  3. eval_result_ner.json +1 -1
  4. model.safetensors +1 -1
  5. training_args.bin +1 -1
README.md CHANGED
@@ -1,14 +1,14 @@
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  ---
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- base_model: haryoaw/scenario-TCR-NER_data-univner_en
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  library_name: transformers
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  license: mit
 
 
 
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  metrics:
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  - precision
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  - recall
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  - f1
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  - accuracy
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- tags:
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- - generated_from_trainer
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  model-index:
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  - name: scenario-kd-po-ner-full_data-univner_full66
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  results: []
@@ -19,13 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # scenario-kd-po-ner-full_data-univner_full66
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- This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5267
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- - Precision: 0.7744
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- - Recall: 0.7391
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- - F1: 0.7564
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- - Accuracy: 0.9807
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  ## Model description
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@@ -56,29 +56,57 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.8089 | 1.2755 | 500 | 0.7185 | 0.7338 | 0.6791 | 0.7054 | 0.9767 |
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- | 0.4626 | 2.5510 | 1000 | 0.6447 | 0.7127 | 0.7319 | 0.7222 | 0.9787 |
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- | 0.3791 | 3.8265 | 1500 | 0.5975 | 0.7349 | 0.7288 | 0.7318 | 0.9794 |
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- | 0.3262 | 5.1020 | 2000 | 0.5889 | 0.7447 | 0.7277 | 0.7361 | 0.9797 |
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- | 0.2868 | 6.3776 | 2500 | 0.5714 | 0.7427 | 0.7381 | 0.7404 | 0.9799 |
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- | 0.2587 | 7.6531 | 3000 | 0.5688 | 0.7703 | 0.7257 | 0.7473 | 0.9807 |
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- | 0.2389 | 8.9286 | 3500 | 0.5610 | 0.7338 | 0.7246 | 0.7292 | 0.9791 |
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- | 0.2211 | 10.2041 | 4000 | 0.5571 | 0.7719 | 0.7495 | 0.7605 | 0.9800 |
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- | 0.2022 | 11.4796 | 4500 | 0.5692 | 0.776 | 0.7029 | 0.7376 | 0.9799 |
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- | 0.1903 | 12.7551 | 5000 | 0.5554 | 0.7711 | 0.7360 | 0.7532 | 0.9804 |
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- | 0.179 | 14.0306 | 5500 | 0.5411 | 0.7574 | 0.7371 | 0.7471 | 0.9803 |
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- | 0.1688 | 15.3061 | 6000 | 0.5353 | 0.7602 | 0.7516 | 0.7559 | 0.9804 |
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- | 0.1608 | 16.5816 | 6500 | 0.5383 | 0.7748 | 0.7267 | 0.75 | 0.9802 |
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- | 0.1552 | 17.8571 | 7000 | 0.5223 | 0.7716 | 0.7381 | 0.7545 | 0.9800 |
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- | 0.1489 | 19.1327 | 7500 | 0.5300 | 0.7721 | 0.7329 | 0.7520 | 0.9801 |
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- | 0.1439 | 20.4082 | 8000 | 0.5321 | 0.7634 | 0.7246 | 0.7435 | 0.9797 |
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- | 0.1391 | 21.6837 | 8500 | 0.5204 | 0.7798 | 0.7443 | 0.7617 | 0.9805 |
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- | 0.1351 | 22.9592 | 9000 | 0.5251 | 0.7489 | 0.7350 | 0.7419 | 0.9800 |
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- | 0.131 | 24.2347 | 9500 | 0.5164 | 0.7664 | 0.7505 | 0.7584 | 0.9808 |
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- | 0.1291 | 25.5102 | 10000 | 0.5216 | 0.7614 | 0.7236 | 0.7420 | 0.9798 |
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- | 0.1276 | 26.7857 | 10500 | 0.5257 | 0.7739 | 0.7371 | 0.7550 | 0.9804 |
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- | 0.1251 | 28.0612 | 11000 | 0.5156 | 0.7692 | 0.7453 | 0.7571 | 0.9808 |
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- | 0.1241 | 29.3367 | 11500 | 0.5267 | 0.7744 | 0.7391 | 0.7564 | 0.9807 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
 
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  library_name: transformers
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  license: mit
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+ base_model: haryoaw/scenario-TCR-NER_data-univner_half
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+ tags:
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+ - generated_from_trainer
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  metrics:
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  - precision
9
  - recall
10
  - f1
11
  - accuracy
 
 
12
  model-index:
13
  - name: scenario-kd-po-ner-full_data-univner_full66
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  results: []
 
19
 
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  # scenario-kd-po-ner-full_data-univner_full66
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+ This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4139
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+ - Precision: 0.8074
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+ - Recall: 0.7771
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+ - F1: 0.7919
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+ - Accuracy: 0.9789
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.9198 | 0.5828 | 500 | 0.6766 | 0.7412 | 0.7331 | 0.7371 | 0.9745 |
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+ | 0.5493 | 1.1655 | 1000 | 0.5975 | 0.7499 | 0.7560 | 0.7529 | 0.9759 |
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+ | 0.4453 | 1.7483 | 1500 | 0.5731 | 0.7583 | 0.7585 | 0.7584 | 0.9758 |
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+ | 0.3762 | 2.3310 | 2000 | 0.5606 | 0.7824 | 0.7492 | 0.7655 | 0.9764 |
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+ | 0.3436 | 2.9138 | 2500 | 0.5208 | 0.7708 | 0.7697 | 0.7703 | 0.9770 |
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+ | 0.3082 | 3.4965 | 3000 | 0.5114 | 0.7891 | 0.7491 | 0.7686 | 0.9769 |
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+ | 0.2933 | 4.0793 | 3500 | 0.5024 | 0.7873 | 0.7654 | 0.7762 | 0.9774 |
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+ | 0.2672 | 4.6620 | 4000 | 0.4971 | 0.7916 | 0.7552 | 0.7729 | 0.9775 |
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+ | 0.2525 | 5.2448 | 4500 | 0.4924 | 0.7733 | 0.7775 | 0.7754 | 0.9771 |
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+ | 0.2385 | 5.8275 | 5000 | 0.4891 | 0.7833 | 0.7725 | 0.7779 | 0.9775 |
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+ | 0.2269 | 6.4103 | 5500 | 0.4843 | 0.7828 | 0.7797 | 0.7813 | 0.9774 |
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+ | 0.2215 | 6.9930 | 6000 | 0.4729 | 0.7741 | 0.7862 | 0.7801 | 0.9778 |
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+ | 0.2076 | 7.5758 | 6500 | 0.4617 | 0.7838 | 0.7772 | 0.7805 | 0.9780 |
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+ | 0.201 | 8.1585 | 7000 | 0.4653 | 0.7975 | 0.7671 | 0.7820 | 0.9779 |
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+ | 0.1935 | 8.7413 | 7500 | 0.4574 | 0.7785 | 0.7922 | 0.7853 | 0.9778 |
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+ | 0.1869 | 9.3240 | 8000 | 0.4662 | 0.7905 | 0.7821 | 0.7863 | 0.9784 |
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+ | 0.1825 | 9.9068 | 8500 | 0.4539 | 0.7883 | 0.7807 | 0.7845 | 0.9782 |
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+ | 0.1748 | 10.4895 | 9000 | 0.4486 | 0.7975 | 0.7852 | 0.7913 | 0.9789 |
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+ | 0.1714 | 11.0723 | 9500 | 0.4499 | 0.7975 | 0.7829 | 0.7901 | 0.9787 |
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+ | 0.166 | 11.6550 | 10000 | 0.4429 | 0.7931 | 0.7852 | 0.7891 | 0.9787 |
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+ | 0.1612 | 12.2378 | 10500 | 0.4427 | 0.7913 | 0.7788 | 0.7850 | 0.9782 |
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+ | 0.1567 | 12.8205 | 11000 | 0.4413 | 0.8024 | 0.7762 | 0.7891 | 0.9786 |
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+ | 0.1544 | 13.4033 | 11500 | 0.4421 | 0.8068 | 0.7628 | 0.7842 | 0.9781 |
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+ | 0.1502 | 13.9860 | 12000 | 0.4388 | 0.8009 | 0.7843 | 0.7925 | 0.9788 |
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+ | 0.146 | 14.5688 | 12500 | 0.4295 | 0.8 | 0.7768 | 0.7882 | 0.9786 |
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+ | 0.1434 | 15.1515 | 13000 | 0.4402 | 0.8057 | 0.7755 | 0.7903 | 0.9784 |
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+ | 0.1404 | 15.7343 | 13500 | 0.4352 | 0.8106 | 0.7713 | 0.7905 | 0.9785 |
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+ | 0.1387 | 16.3170 | 14000 | 0.4360 | 0.7981 | 0.7729 | 0.7853 | 0.9783 |
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+ | 0.1356 | 16.8998 | 14500 | 0.4328 | 0.8071 | 0.7722 | 0.7893 | 0.9786 |
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+ | 0.1345 | 17.4825 | 15000 | 0.4278 | 0.7990 | 0.7736 | 0.7861 | 0.9786 |
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+ | 0.1313 | 18.0653 | 15500 | 0.4268 | 0.7985 | 0.7868 | 0.7926 | 0.9789 |
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+ | 0.1282 | 18.6480 | 16000 | 0.4219 | 0.7983 | 0.7818 | 0.7900 | 0.9789 |
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+ | 0.1284 | 19.2308 | 16500 | 0.4313 | 0.7968 | 0.7729 | 0.7847 | 0.9782 |
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+ | 0.1242 | 19.8135 | 17000 | 0.4255 | 0.8103 | 0.7803 | 0.7950 | 0.9790 |
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+ | 0.1239 | 20.3963 | 17500 | 0.4315 | 0.8060 | 0.7720 | 0.7887 | 0.9786 |
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+ | 0.124 | 20.9790 | 18000 | 0.4317 | 0.8117 | 0.7663 | 0.7883 | 0.9782 |
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+ | 0.1219 | 21.5618 | 18500 | 0.4198 | 0.7959 | 0.7758 | 0.7857 | 0.9783 |
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+ | 0.1199 | 22.1445 | 19000 | 0.4257 | 0.7976 | 0.7795 | 0.7885 | 0.9784 |
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+ | 0.1184 | 22.7273 | 19500 | 0.4271 | 0.8095 | 0.7664 | 0.7874 | 0.9784 |
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+ | 0.118 | 23.3100 | 20000 | 0.4169 | 0.8076 | 0.7769 | 0.7920 | 0.9789 |
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+ | 0.1176 | 23.8928 | 20500 | 0.4203 | 0.8069 | 0.7769 | 0.7916 | 0.9786 |
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+ | 0.1152 | 24.4755 | 21000 | 0.4180 | 0.8056 | 0.7816 | 0.7934 | 0.9790 |
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+ | 0.115 | 25.0583 | 21500 | 0.4206 | 0.8082 | 0.7765 | 0.7921 | 0.9791 |
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+ | 0.1126 | 25.6410 | 22000 | 0.4196 | 0.8047 | 0.7762 | 0.7902 | 0.9787 |
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+ | 0.1148 | 26.2238 | 22500 | 0.4176 | 0.8061 | 0.7820 | 0.7938 | 0.9789 |
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+ | 0.1123 | 26.8065 | 23000 | 0.4156 | 0.8086 | 0.7826 | 0.7954 | 0.9791 |
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+ | 0.1108 | 27.3893 | 23500 | 0.4133 | 0.8089 | 0.7829 | 0.7957 | 0.9792 |
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+ | 0.111 | 27.9720 | 24000 | 0.4114 | 0.8021 | 0.7768 | 0.7893 | 0.9790 |
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+ | 0.1099 | 28.5548 | 24500 | 0.4159 | 0.8066 | 0.7739 | 0.7899 | 0.9786 |
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+ | 0.1112 | 29.1375 | 25000 | 0.4151 | 0.8082 | 0.7804 | 0.7940 | 0.9789 |
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+ | 0.1091 | 29.7203 | 25500 | 0.4139 | 0.8074 | 0.7771 | 0.7919 | 0.9789 |
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  ### Framework versions
config.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "_name_or_path": "haryoaw/scenario-TCR-NER_data-univner_en",
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  "architectures": [
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  "XLMRobertaForTokenClassificationKD"
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  ],
 
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  {
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+ "_name_or_path": "haryoaw/scenario-TCR-NER_data-univner_half",
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  "architectures": [
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  "XLMRobertaForTokenClassificationKD"
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  ],
eval_result_ner.json CHANGED
@@ -1 +1 @@
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- {"ceb_gja": {"precision": 0.44594594594594594, "recall": 0.673469387755102, "f1": 0.5365853658536585, "accuracy": 0.9482625482625483}, "en_pud": {"precision": 0.7542120911793855, "recall": 0.707906976744186, "f1": 0.7303262955854126, "accuracy": 0.9746883264072534}, "de_pud": {"precision": 0.7038934426229508, "recall": 0.6612127045235804, "f1": 0.6818858560794044, "accuracy": 0.9671839107402372}, "pt_pud": {"precision": 0.780564263322884, "recall": 0.6797088262056415, "f1": 0.7266536964980544, "accuracy": 0.9725295851668304}, "ru_pud": {"precision": 0.6280552603613178, "recall": 0.5704633204633205, "f1": 0.5978755690440061, "accuracy": 0.9580470162748643}, "sv_pud": {"precision": 0.7890204520990313, "recall": 0.7123420796890184, "f1": 0.7487231869254342, "accuracy": 0.9742084294401342}, "tl_trg": {"precision": 0.6666666666666666, "recall": 0.782608695652174, "f1": 0.72, "accuracy": 0.9822888283378747}, "tl_ugnayan": {"precision": 0.5, "recall": 0.5454545454545454, "f1": 0.5217391304347826, "accuracy": 0.9653600729261622}, "zh_gsd": {"precision": 0.42066420664206644, "recall": 0.14863102998696218, "f1": 0.21965317919075147, "accuracy": 0.9029304029304029}, "zh_gsdsimp": {"precision": 0.42543859649122806, "recall": 0.127129750982962, "f1": 0.19576185671039356, "accuracy": 0.9024309024309024}, "hr_set": {"precision": 0.7464788732394366, "recall": 0.6421952957947256, "f1": 0.6904214559386973, "accuracy": 0.9617889530090684}, "da_ddt": {"precision": 0.7569832402234636, "recall": 0.6062639821029083, "f1": 0.6732919254658385, "accuracy": 0.9753566796368353}, "en_ewt": {"precision": 0.7956131605184447, "recall": 0.7334558823529411, "f1": 0.763271162123386, "accuracy": 0.9762919870900905}, "pt_bosque": {"precision": 0.7508055853920516, "recall": 0.5753086419753086, "f1": 0.6514445479962722, "accuracy": 0.963048833502391}, "sr_set": {"precision": 0.7551867219917012, "recall": 0.6446280991735537, "f1": 0.6955414012738853, "accuracy": 0.9533315821731897}, "sk_snk": {"precision": 0.6048484848484849, "recall": 0.5453551912568306, "f1": 0.5735632183908046, "accuracy": 0.9422895728643216}, "sv_talbanken": {"precision": 0.7627906976744186, "recall": 0.8367346938775511, "f1": 0.7980535279805352, "accuracy": 0.9960249300682141}}
 
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