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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: 0.4704
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- - Precision: 0.7639
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- - Recall: 0.7236
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- - F1: 0.7432
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- - Accuracy: 0.9788
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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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- | 0.9997 | 1.2755 | 500 | 0.7818 | 0.4929 | 0.4286 | 0.4585 | 0.9622 |
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- | 0.4472 | 2.5510 | 1000 | 0.5948 | 0.6438 | 0.6398 | 0.6417 | 0.9734 |
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- | 0.3048 | 3.8265 | 1500 | 0.5434 | 0.7053 | 0.6936 | 0.6994 | 0.9768 |
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- | 0.2301 | 5.1020 | 2000 | 0.5331 | 0.7080 | 0.7205 | 0.7142 | 0.9768 |
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- | 0.19 | 6.3776 | 2500 | 0.5176 | 0.7118 | 0.7236 | 0.7177 | 0.9766 |
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- | 0.1622 | 7.6531 | 3000 | 0.5157 | 0.7330 | 0.7050 | 0.7187 | 0.9778 |
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- | 0.1461 | 8.9286 | 3500 | 0.5090 | 0.7553 | 0.6967 | 0.7248 | 0.9775 |
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- | 0.1331 | 10.2041 | 4000 | 0.4857 | 0.7558 | 0.7081 | 0.7312 | 0.9781 |
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- | 0.123 | 11.4796 | 4500 | 0.5082 | 0.7566 | 0.7081 | 0.7316 | 0.9784 |
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- | 0.1134 | 12.7551 | 5000 | 0.5113 | 0.7440 | 0.7008 | 0.7217 | 0.9775 |
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- | 0.109 | 14.0306 | 5500 | 0.5122 | 0.7559 | 0.6925 | 0.7229 | 0.9773 |
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- | 0.1045 | 15.3061 | 6000 | 0.4942 | 0.7362 | 0.7164 | 0.7261 | 0.9779 |
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- | 0.1005 | 16.5816 | 6500 | 0.4817 | 0.7770 | 0.7143 | 0.7443 | 0.9794 |
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- | 0.0967 | 17.8571 | 7000 | 0.4947 | 0.7642 | 0.7081 | 0.7351 | 0.9782 |
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- | 0.094 | 19.1327 | 7500 | 0.4737 | 0.7527 | 0.7215 | 0.7368 | 0.9785 |
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- | 0.0917 | 20.4082 | 8000 | 0.4815 | 0.7669 | 0.7153 | 0.7402 | 0.9786 |
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- | 0.0889 | 21.6837 | 8500 | 0.4797 | 0.7783 | 0.7195 | 0.7477 | 0.9791 |
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- | 0.0885 | 22.9592 | 9000 | 0.4824 | 0.7584 | 0.7215 | 0.7395 | 0.9783 |
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- | 0.0866 | 24.2347 | 9500 | 0.4557 | 0.7630 | 0.7164 | 0.7389 | 0.9794 |
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- | 0.0855 | 25.5102 | 10000 | 0.4618 | 0.7749 | 0.7236 | 0.7484 | 0.9797 |
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- | 0.0851 | 26.7857 | 10500 | 0.4466 | 0.7641 | 0.7443 | 0.7541 | 0.9800 |
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- | 0.0837 | 28.0612 | 11000 | 0.4526 | 0.7725 | 0.7381 | 0.7549 | 0.9795 |
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- | 0.0834 | 29.3367 | 11500 | 0.4704 | 0.7639 | 0.7236 | 0.7432 | 0.9788 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
 
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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:
8
  - 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: 0.3242
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+ - Precision: 0.8056
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+ - Recall: 0.7751
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+ - F1: 0.7901
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+ - Accuracy: 0.9783
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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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+ | 1.2921 | 0.5828 | 500 | 0.7894 | 0.4860 | 0.4574 | 0.4712 | 0.9537 |
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+ | 0.6435 | 1.1655 | 1000 | 0.5676 | 0.6461 | 0.6621 | 0.6540 | 0.9669 |
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+ | 0.4512 | 1.7483 | 1500 | 0.4976 | 0.7198 | 0.6950 | 0.7072 | 0.9713 |
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+ | 0.3533 | 2.3310 | 2000 | 0.4642 | 0.7328 | 0.7188 | 0.7257 | 0.9730 |
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+ | 0.3058 | 2.9138 | 2500 | 0.4469 | 0.7334 | 0.7259 | 0.7296 | 0.9732 |
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+ | 0.2496 | 3.4965 | 3000 | 0.4380 | 0.7275 | 0.7591 | 0.7429 | 0.9741 |
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+ | 0.2323 | 4.0793 | 3500 | 0.4192 | 0.7561 | 0.7419 | 0.7489 | 0.9750 |
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+ | 0.2013 | 4.6620 | 4000 | 0.4210 | 0.7635 | 0.7332 | 0.7481 | 0.9751 |
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+ | 0.1896 | 5.2448 | 4500 | 0.4109 | 0.7415 | 0.7645 | 0.7529 | 0.9753 |
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+ | 0.1738 | 5.8275 | 5000 | 0.4173 | 0.7627 | 0.7425 | 0.7524 | 0.9752 |
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+ | 0.1657 | 6.4103 | 5500 | 0.3956 | 0.7657 | 0.7648 | 0.7653 | 0.9761 |
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+ | 0.1565 | 6.9930 | 6000 | 0.3871 | 0.7660 | 0.7668 | 0.7664 | 0.9766 |
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+ | 0.1469 | 7.5758 | 6500 | 0.3904 | 0.7668 | 0.7642 | 0.7655 | 0.9761 |
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+ | 0.1398 | 8.1585 | 7000 | 0.3882 | 0.7785 | 0.7477 | 0.7628 | 0.9760 |
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+ | 0.1353 | 8.7413 | 7500 | 0.3902 | 0.7805 | 0.7582 | 0.7692 | 0.9764 |
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+ | 0.13 | 9.3240 | 8000 | 0.3803 | 0.7887 | 0.7557 | 0.7719 | 0.9768 |
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+ | 0.1278 | 9.9068 | 8500 | 0.3693 | 0.7842 | 0.7624 | 0.7731 | 0.9772 |
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+ | 0.1225 | 10.4895 | 9000 | 0.3724 | 0.7898 | 0.7589 | 0.7740 | 0.9769 |
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+ | 0.1206 | 11.0723 | 9500 | 0.3725 | 0.7671 | 0.7818 | 0.7744 | 0.9768 |
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+ | 0.1168 | 11.6550 | 10000 | 0.3849 | 0.7976 | 0.7419 | 0.7687 | 0.9764 |
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+ | 0.1145 | 12.2378 | 10500 | 0.3673 | 0.7901 | 0.7638 | 0.7768 | 0.9770 |
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+ | 0.1112 | 12.8205 | 11000 | 0.3567 | 0.7861 | 0.7849 | 0.7855 | 0.9779 |
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+ | 0.1095 | 13.4033 | 11500 | 0.3578 | 0.7970 | 0.7573 | 0.7767 | 0.9774 |
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+ | 0.1079 | 13.9860 | 12000 | 0.3579 | 0.7888 | 0.7696 | 0.7791 | 0.9772 |
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+ | 0.1049 | 14.5688 | 12500 | 0.3515 | 0.7756 | 0.7875 | 0.7815 | 0.9775 |
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+ | 0.1025 | 15.1515 | 13000 | 0.3537 | 0.7922 | 0.7755 | 0.7838 | 0.9777 |
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+ | 0.1025 | 15.7343 | 13500 | 0.3633 | 0.7988 | 0.7593 | 0.7786 | 0.9769 |
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+ | 0.1013 | 16.3170 | 14000 | 0.3556 | 0.7995 | 0.7556 | 0.7769 | 0.9771 |
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+ | 0.099 | 16.8998 | 14500 | 0.3611 | 0.7883 | 0.7638 | 0.7758 | 0.9770 |
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+ | 0.0979 | 17.4825 | 15000 | 0.3492 | 0.8138 | 0.7513 | 0.7813 | 0.9775 |
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+ | 0.0968 | 18.0653 | 15500 | 0.3440 | 0.7963 | 0.7706 | 0.7833 | 0.9778 |
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+ | 0.0943 | 18.6480 | 16000 | 0.3488 | 0.7949 | 0.7752 | 0.7850 | 0.9777 |
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+ | 0.0951 | 19.2308 | 16500 | 0.3452 | 0.7943 | 0.7709 | 0.7824 | 0.9779 |
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+ | 0.0923 | 19.8135 | 17000 | 0.3336 | 0.7879 | 0.7793 | 0.7835 | 0.9782 |
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+ | 0.0935 | 20.3963 | 17500 | 0.3401 | 0.8052 | 0.7614 | 0.7826 | 0.9777 |
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+ | 0.0918 | 20.9790 | 18000 | 0.3368 | 0.7963 | 0.7794 | 0.7878 | 0.9781 |
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+ | 0.0912 | 21.5618 | 18500 | 0.3391 | 0.8037 | 0.7713 | 0.7872 | 0.9778 |
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+ | 0.09 | 22.1445 | 19000 | 0.3328 | 0.8001 | 0.7722 | 0.7859 | 0.9780 |
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+ | 0.0892 | 22.7273 | 19500 | 0.3396 | 0.8075 | 0.7645 | 0.7854 | 0.9778 |
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+ | 0.0885 | 23.3100 | 20000 | 0.3352 | 0.8024 | 0.7754 | 0.7887 | 0.9782 |
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+ | 0.088 | 23.8928 | 20500 | 0.3298 | 0.8089 | 0.7775 | 0.7929 | 0.9786 |
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+ | 0.0874 | 24.4755 | 21000 | 0.3278 | 0.7972 | 0.7756 | 0.7863 | 0.9782 |
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+ | 0.087 | 25.0583 | 21500 | 0.3305 | 0.8063 | 0.7697 | 0.7876 | 0.9782 |
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+ | 0.0857 | 25.6410 | 22000 | 0.3316 | 0.8093 | 0.7666 | 0.7873 | 0.9781 |
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+ | 0.0862 | 26.2238 | 22500 | 0.3305 | 0.8011 | 0.7699 | 0.7852 | 0.9778 |
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+ | 0.0858 | 26.8065 | 23000 | 0.3305 | 0.8062 | 0.7700 | 0.7877 | 0.9781 |
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+ | 0.0857 | 27.3893 | 23500 | 0.3291 | 0.7981 | 0.7720 | 0.7848 | 0.9780 |
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+ | 0.0847 | 27.9720 | 24000 | 0.3264 | 0.8108 | 0.7700 | 0.7899 | 0.9783 |
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+ | 0.0846 | 28.5548 | 24500 | 0.3270 | 0.8038 | 0.7673 | 0.7851 | 0.9781 |
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+ | 0.0848 | 29.1375 | 25000 | 0.3272 | 0.8078 | 0.7738 | 0.7904 | 0.9784 |
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+ | 0.084 | 29.7203 | 25500 | 0.3242 | 0.8056 | 0.7751 | 0.7901 | 0.9783 |
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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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  "DebertaForTokenClassificationKD"
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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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  "DebertaForTokenClassificationKD"
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  ],
eval_result_ner.json CHANGED
@@ -1 +1 @@
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- {"ceb_gja": {"precision": 0.32710280373831774, "recall": 0.7142857142857143, "f1": 0.4487179487179487, "accuracy": 0.9281853281853282}, "en_pud": {"precision": 0.7364798426745329, "recall": 0.6967441860465117, "f1": 0.7160611854684512, "accuracy": 0.9730827351718927}, "de_pud": {"precision": 0.6587982832618026, "recall": 0.5909528392685275, "f1": 0.6230339928970066, "accuracy": 0.962027096713703}, "pt_pud": {"precision": 0.70625, "recall": 0.6169244767970883, "f1": 0.6585721223895095, "accuracy": 0.9655231341051822}, "ru_pud": {"precision": 0.5360501567398119, "recall": 0.4951737451737452, "f1": 0.5148018063221274, "accuracy": 0.9514337380521829}, "sv_pud": {"precision": 0.7566320645905421, "recall": 0.6375121477162293, "f1": 0.6919831223628691, "accuracy": 0.9691235059760956}, "tl_trg": {"precision": 0.2857142857142857, "recall": 0.6956521739130435, "f1": 0.4050632911392405, "accuracy": 0.9277929155313351}, "tl_ugnayan": {"precision": 0.273972602739726, "recall": 0.6060606060606061, "f1": 0.3773584905660377, "accuracy": 0.9361896080218779}, "zh_gsd": {"precision": 0.4166666666666667, "recall": 0.20860495436766624, "f1": 0.27801911381407474, "accuracy": 0.9069264069264069}, "zh_gsdsimp": {"precision": 0.43007915567282323, "recall": 0.21363040629095675, "f1": 0.28546409807355516, "accuracy": 0.9075091575091575}, "hr_set": {"precision": 0.6354166666666666, "recall": 0.5652173913043478, "f1": 0.5982648057336853, "accuracy": 0.9518136850783182}, "da_ddt": {"precision": 0.6771428571428572, "recall": 0.5302013422818792, "f1": 0.5947302383939775, "accuracy": 0.9708670058864611}, "en_ewt": {"precision": 0.7904483430799221, "recall": 0.7454044117647058, "f1": 0.7672658467360455, "accuracy": 0.977009204287365}, "pt_bosque": {"precision": 0.6593521421107628, "recall": 0.5193415637860083, "f1": 0.5810313075506446, "accuracy": 0.9572887987248225}, "sr_set": {"precision": 0.649867374005305, "recall": 0.5785123966942148, "f1": 0.6121174266083698, "accuracy": 0.9438753173977761}, "sk_snk": {"precision": 0.5450346420323325, "recall": 0.5158469945355191, "f1": 0.5300393037619315, "accuracy": 0.9336526381909548}, "sv_talbanken": {"precision": 0.7647058823529411, "recall": 0.7295918367346939, "f1": 0.7467362924281984, "accuracy": 0.9950925062570545}}
 
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