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scenario-kd-po-ner-full-mdeberta_data-univner_full55

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  1. README.md +42 -42
  2. eval_result_ner.json +1 -1
  3. model.safetensors +1 -1
  4. 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_full
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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-mdeberta_data-univner_full55
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  results: []
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2447
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- - Precision: 0.8164
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- - Recall: 0.8230
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- - F1: 0.8197
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- - Accuracy: 0.9815
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  ## Model description
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@@ -58,40 +58,40 @@ 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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- | 1.1833 | 0.2911 | 500 | 0.6892 | 0.5129 | 0.4802 | 0.4960 | 0.9543 |
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- | 0.5847 | 0.5822 | 1000 | 0.4874 | 0.6855 | 0.6350 | 0.6593 | 0.9673 |
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- | 0.4508 | 0.8732 | 1500 | 0.4097 | 0.7055 | 0.7560 | 0.7299 | 0.9734 |
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- | 0.373 | 1.1643 | 2000 | 0.3775 | 0.7500 | 0.7422 | 0.7460 | 0.9753 |
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- | 0.3179 | 1.4554 | 2500 | 0.3419 | 0.7546 | 0.7756 | 0.7650 | 0.9768 |
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- | 0.2999 | 1.7465 | 3000 | 0.3334 | 0.7713 | 0.8019 | 0.7863 | 0.9785 |
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- | 0.2786 | 2.0375 | 3500 | 0.3187 | 0.7838 | 0.7883 | 0.7861 | 0.9789 |
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- | 0.2448 | 2.3286 | 4000 | 0.3204 | 0.7932 | 0.7777 | 0.7854 | 0.9787 |
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- | 0.2332 | 2.6197 | 4500 | 0.3065 | 0.8004 | 0.7833 | 0.7917 | 0.9790 |
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- | 0.2267 | 2.9108 | 5000 | 0.2972 | 0.8025 | 0.8029 | 0.8027 | 0.9799 |
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- | 0.2114 | 3.2019 | 5500 | 0.2948 | 0.7903 | 0.8046 | 0.7974 | 0.9794 |
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- | 0.2006 | 3.4929 | 6000 | 0.2877 | 0.8131 | 0.8045 | 0.8088 | 0.9804 |
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- | 0.1941 | 3.7840 | 6500 | 0.2819 | 0.8007 | 0.8018 | 0.8012 | 0.9801 |
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- | 0.1885 | 4.0751 | 7000 | 0.2787 | 0.8008 | 0.8058 | 0.8033 | 0.9797 |
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- | 0.1798 | 4.3662 | 7500 | 0.2821 | 0.8071 | 0.8061 | 0.8066 | 0.9800 |
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- | 0.1768 | 4.6573 | 8000 | 0.2750 | 0.8046 | 0.8061 | 0.8053 | 0.9802 |
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- | 0.1742 | 4.9483 | 8500 | 0.2706 | 0.7992 | 0.8231 | 0.8110 | 0.9804 |
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- | 0.1651 | 5.2394 | 9000 | 0.2671 | 0.8192 | 0.8130 | 0.8161 | 0.9808 |
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- | 0.1619 | 5.5305 | 9500 | 0.2680 | 0.8168 | 0.8097 | 0.8132 | 0.9807 |
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- | 0.1616 | 5.8216 | 10000 | 0.2611 | 0.8121 | 0.8188 | 0.8154 | 0.9808 |
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- | 0.1604 | 6.1126 | 10500 | 0.2614 | 0.8165 | 0.8074 | 0.8119 | 0.9808 |
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- | 0.1542 | 6.4037 | 11000 | 0.2569 | 0.8110 | 0.8247 | 0.8178 | 0.9810 |
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- | 0.1534 | 6.6948 | 11500 | 0.2598 | 0.8126 | 0.8152 | 0.8139 | 0.9810 |
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- | 0.1507 | 6.9859 | 12000 | 0.2607 | 0.8216 | 0.8124 | 0.8170 | 0.9813 |
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- | 0.1467 | 7.2770 | 12500 | 0.2531 | 0.8143 | 0.8227 | 0.8185 | 0.9811 |
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- | 0.1455 | 7.5680 | 13000 | 0.2519 | 0.8229 | 0.8147 | 0.8188 | 0.9813 |
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- | 0.1457 | 7.8591 | 13500 | 0.2524 | 0.8236 | 0.8152 | 0.8194 | 0.9811 |
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- | 0.1454 | 8.1502 | 14000 | 0.2483 | 0.8179 | 0.8194 | 0.8187 | 0.9812 |
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- | 0.1416 | 8.4413 | 14500 | 0.2478 | 0.8188 | 0.8248 | 0.8218 | 0.9814 |
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- | 0.1419 | 8.7324 | 15000 | 0.2484 | 0.8224 | 0.8298 | 0.8261 | 0.9814 |
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- | 0.1404 | 9.0234 | 15500 | 0.2482 | 0.8222 | 0.8201 | 0.8211 | 0.9812 |
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- | 0.1406 | 9.3145 | 16000 | 0.2474 | 0.8227 | 0.8224 | 0.8226 | 0.9816 |
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- | 0.1377 | 9.6056 | 16500 | 0.2448 | 0.8212 | 0.8211 | 0.8212 | 0.9815 |
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- | 0.1401 | 9.8967 | 17000 | 0.2447 | 0.8164 | 0.8230 | 0.8197 | 0.9815 |
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  ### Framework versions
 
1
  ---
 
2
  library_name: transformers
3
  license: mit
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+ base_model: haryoaw/scenario-TCR-NER_data-univner_full
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+ tags:
6
+ - 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-mdeberta_data-univner_full55
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  results: []
 
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  This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 46.9305
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+ - Precision: 0.8196
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+ - Recall: 0.8292
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+ - F1: 0.8244
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+ - Accuracy: 0.9823
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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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+ | 135.8523 | 0.2911 | 500 | 108.5680 | 0.5906 | 0.3842 | 0.4656 | 0.9517 |
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+ | 100.5734 | 0.5822 | 1000 | 94.6790 | 0.7351 | 0.6641 | 0.6978 | 0.9712 |
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+ | 91.2154 | 0.8732 | 1500 | 88.0640 | 0.7607 | 0.7570 | 0.7588 | 0.9762 |
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+ | 85.164 | 1.1643 | 2000 | 83.3722 | 0.8082 | 0.7302 | 0.7672 | 0.9767 |
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+ | 80.2474 | 1.4554 | 2500 | 78.9752 | 0.7780 | 0.7925 | 0.7852 | 0.9791 |
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+ | 76.4386 | 1.7465 | 3000 | 75.6330 | 0.8035 | 0.7945 | 0.7990 | 0.9801 |
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+ | 73.0828 | 2.0375 | 3500 | 72.4945 | 0.7997 | 0.7970 | 0.7983 | 0.9804 |
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+ | 69.7284 | 2.3286 | 4000 | 69.7705 | 0.7983 | 0.8048 | 0.8016 | 0.9804 |
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+ | 67.0314 | 2.6197 | 4500 | 67.3742 | 0.8113 | 0.7970 | 0.8041 | 0.9805 |
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+ | 64.9596 | 2.9108 | 5000 | 65.2223 | 0.8108 | 0.8025 | 0.8066 | 0.9805 |
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+ | 62.6221 | 3.2019 | 5500 | 63.1795 | 0.8049 | 0.8169 | 0.8109 | 0.9810 |
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+ | 60.6361 | 3.4929 | 6000 | 61.4200 | 0.8124 | 0.8186 | 0.8155 | 0.9814 |
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+ | 58.8661 | 3.7840 | 6500 | 59.9772 | 0.8102 | 0.8192 | 0.8147 | 0.9815 |
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+ | 57.5058 | 4.0751 | 7000 | 58.4410 | 0.8114 | 0.8168 | 0.8141 | 0.9811 |
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+ | 55.9259 | 4.3662 | 7500 | 57.1486 | 0.8151 | 0.8179 | 0.8165 | 0.9814 |
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+ | 54.6494 | 4.6573 | 8000 | 55.9362 | 0.8206 | 0.8155 | 0.8180 | 0.9814 |
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+ | 53.5407 | 4.9483 | 8500 | 54.8810 | 0.8152 | 0.8205 | 0.8179 | 0.9816 |
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+ | 52.3581 | 5.2394 | 9000 | 53.9021 | 0.8169 | 0.8266 | 0.8217 | 0.9816 |
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+ | 51.3581 | 5.5305 | 9500 | 53.0325 | 0.8200 | 0.8204 | 0.8202 | 0.9816 |
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+ | 50.5535 | 5.8216 | 10000 | 52.1425 | 0.8182 | 0.8282 | 0.8232 | 0.9818 |
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+ | 49.8392 | 6.1126 | 10500 | 51.4247 | 0.8178 | 0.8254 | 0.8216 | 0.9817 |
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+ | 48.9716 | 6.4037 | 11000 | 50.6978 | 0.8191 | 0.8338 | 0.8264 | 0.9823 |
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+ | 48.3296 | 6.6948 | 11500 | 50.1578 | 0.8164 | 0.8290 | 0.8227 | 0.9818 |
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+ | 47.712 | 6.9859 | 12000 | 49.5760 | 0.8234 | 0.8266 | 0.8250 | 0.9824 |
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+ | 47.0545 | 7.2770 | 12500 | 49.0523 | 0.8227 | 0.8354 | 0.8290 | 0.9821 |
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+ | 46.6326 | 7.5680 | 13000 | 48.6282 | 0.8174 | 0.8287 | 0.8230 | 0.9820 |
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+ | 46.2306 | 7.8591 | 13500 | 48.2713 | 0.8208 | 0.8254 | 0.8231 | 0.9819 |
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+ | 45.9118 | 8.1502 | 14000 | 47.9235 | 0.8185 | 0.8259 | 0.8222 | 0.9817 |
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+ | 45.5272 | 8.4413 | 14500 | 47.6086 | 0.8241 | 0.8259 | 0.8250 | 0.9822 |
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+ | 45.2228 | 8.7324 | 15000 | 47.3476 | 0.8250 | 0.8321 | 0.8285 | 0.9822 |
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+ | 44.9978 | 9.0234 | 15500 | 47.1635 | 0.8204 | 0.8263 | 0.8233 | 0.9821 |
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+ | 44.8309 | 9.3145 | 16000 | 47.0839 | 0.8264 | 0.8285 | 0.8274 | 0.9821 |
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+ | 44.6998 | 9.6056 | 16500 | 46.9565 | 0.8228 | 0.8292 | 0.8260 | 0.9824 |
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+ | 44.6759 | 9.8967 | 17000 | 46.9305 | 0.8196 | 0.8292 | 0.8244 | 0.9823 |
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  ### Framework versions
eval_result_ner.json CHANGED
@@ -1 +1 @@
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