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scenario-kd-scr-ner-half-xlmr_data-univner_full66

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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-scr-ner-half-xlmr_data-univner_full66
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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: 1.2164
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- - Precision: 0.4268
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- - Recall: 0.3872
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- - F1: 0.4061
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- - Accuracy: 0.9451
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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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- | 2.8528 | 0.2911 | 500 | 2.3182 | 0.5357 | 0.0022 | 0.0043 | 0.9241 |
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- | 2.1207 | 0.5822 | 1000 | 2.0259 | 0.2078 | 0.0100 | 0.0190 | 0.9243 |
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- | 1.8727 | 0.8732 | 1500 | 1.8417 | 0.2116 | 0.0853 | 0.1216 | 0.9265 |
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- | 1.7263 | 1.1643 | 2000 | 1.7362 | 0.1592 | 0.0693 | 0.0965 | 0.9268 |
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- | 1.6268 | 1.4554 | 2500 | 1.7251 | 0.2612 | 0.1594 | 0.1980 | 0.9303 |
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- | 1.5859 | 1.7465 | 3000 | 1.6161 | 0.3013 | 0.1824 | 0.2272 | 0.9331 |
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- | 1.4992 | 2.0375 | 3500 | 1.5799 | 0.3257 | 0.2106 | 0.2558 | 0.9348 |
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- | 1.4072 | 2.3286 | 4000 | 1.5337 | 0.3402 | 0.2483 | 0.2871 | 0.9358 |
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- | 1.374 | 2.6197 | 4500 | 1.5234 | 0.3113 | 0.2886 | 0.2995 | 0.9363 |
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- | 1.3602 | 2.9108 | 5000 | 1.4697 | 0.3426 | 0.2717 | 0.3030 | 0.9376 |
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- | 1.2661 | 3.2019 | 5500 | 1.4345 | 0.3421 | 0.2953 | 0.3170 | 0.9386 |
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- | 1.2524 | 3.4929 | 6000 | 1.4146 | 0.3843 | 0.3011 | 0.3376 | 0.9395 |
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- | 1.2057 | 3.7840 | 6500 | 1.4308 | 0.3767 | 0.2730 | 0.3166 | 0.9400 |
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- | 1.2018 | 4.0751 | 7000 | 1.3902 | 0.3836 | 0.3069 | 0.3410 | 0.9406 |
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- | 1.1243 | 4.3662 | 7500 | 1.3595 | 0.3811 | 0.3255 | 0.3511 | 0.9414 |
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- | 1.1248 | 4.6573 | 8000 | 1.3407 | 0.3940 | 0.3122 | 0.3484 | 0.9414 |
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- | 1.1234 | 4.9483 | 8500 | 1.3333 | 0.3802 | 0.3196 | 0.3473 | 0.9415 |
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- | 1.0707 | 5.2394 | 9000 | 1.3303 | 0.3937 | 0.3301 | 0.3591 | 0.9422 |
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- | 1.0384 | 5.5305 | 9500 | 1.2940 | 0.3962 | 0.3370 | 0.3642 | 0.9425 |
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- | 1.0239 | 5.8216 | 10000 | 1.2959 | 0.3967 | 0.3486 | 0.3711 | 0.9420 |
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- | 1.007 | 6.1126 | 10500 | 1.2798 | 0.4070 | 0.3653 | 0.3850 | 0.9430 |
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- | 0.9654 | 6.4037 | 11000 | 1.2714 | 0.3904 | 0.3634 | 0.3764 | 0.9424 |
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- | 0.9657 | 6.6948 | 11500 | 1.2591 | 0.3861 | 0.3774 | 0.3817 | 0.9428 |
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- | 0.9678 | 6.9859 | 12000 | 1.2546 | 0.4209 | 0.3509 | 0.3827 | 0.9435 |
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- | 0.9217 | 7.2770 | 12500 | 1.2610 | 0.4124 | 0.3686 | 0.3893 | 0.9433 |
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- | 0.9056 | 7.5680 | 13000 | 1.2403 | 0.4238 | 0.3744 | 0.3976 | 0.9442 |
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- | 0.9146 | 7.8591 | 13500 | 1.2396 | 0.4242 | 0.3779 | 0.3997 | 0.9445 |
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- | 0.8974 | 8.1502 | 14000 | 1.2246 | 0.4213 | 0.3910 | 0.4056 | 0.9448 |
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- | 0.8572 | 8.4413 | 14500 | 1.2233 | 0.4232 | 0.3831 | 0.4022 | 0.9447 |
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- | 0.8703 | 8.7324 | 15000 | 1.2265 | 0.4228 | 0.3740 | 0.3969 | 0.9450 |
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- | 0.8774 | 9.0234 | 15500 | 1.2190 | 0.4415 | 0.3806 | 0.4088 | 0.9454 |
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- | 0.8581 | 9.3145 | 16000 | 1.2245 | 0.4251 | 0.3838 | 0.4034 | 0.9449 |
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- | 0.8411 | 9.6056 | 16500 | 1.2153 | 0.4298 | 0.3982 | 0.4134 | 0.9453 |
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- | 0.8466 | 9.8967 | 17000 | 1.2164 | 0.4268 | 0.3872 | 0.4061 | 0.9451 |
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  ### Framework versions
 
1
  ---
 
2
  library_name: transformers
3
  license: mit
4
+ base_model: haryoaw/scenario-TCR-NER_data-univner_full
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+ tags:
6
+ - generated_from_trainer
7
  metrics:
8
  - precision
9
  - recall
10
  - f1
11
  - accuracy
 
 
12
  model-index:
13
  - name: scenario-kd-scr-ner-half-xlmr_data-univner_full66
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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: 120.2460
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+ - Precision: 0.4418
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+ - Recall: 0.4111
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+ - F1: 0.4259
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+ - Accuracy: 0.9508
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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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+ | 257.7836 | 0.2911 | 500 | 190.0373 | 0.0 | 0.0 | 0.0 | 0.9241 |
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+ | 179.3866 | 0.5822 | 1000 | 173.8501 | 0.4324 | 0.0023 | 0.0046 | 0.9242 |
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+ | 167.4276 | 0.8732 | 1500 | 164.6176 | 0.4172 | 0.0196 | 0.0375 | 0.9249 |
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+ | 160.874 | 1.1643 | 2000 | 160.7553 | 0.4603 | 0.0042 | 0.0083 | 0.9243 |
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+ | 155.3677 | 1.4554 | 2500 | 154.5823 | 0.2516 | 0.0685 | 0.1077 | 0.9268 |
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+ | 151.6743 | 1.7465 | 3000 | 151.6693 | 0.3540 | 0.0594 | 0.1018 | 0.9267 |
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+ | 147.7351 | 2.0375 | 3500 | 147.3540 | 0.2816 | 0.0925 | 0.1392 | 0.9277 |
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+ | 144.2784 | 2.3286 | 4000 | 144.3419 | 0.2836 | 0.1122 | 0.1608 | 0.9285 |
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+ | 141.5455 | 2.6197 | 4500 | 141.6975 | 0.2921 | 0.1101 | 0.1599 | 0.9293 |
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+ | 139.1013 | 2.9108 | 5000 | 139.2388 | 0.2906 | 0.1260 | 0.1757 | 0.9311 |
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+ | 135.433 | 3.2019 | 5500 | 137.0034 | 0.2676 | 0.1945 | 0.2252 | 0.9334 |
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+ | 133.6936 | 3.4929 | 6000 | 135.0955 | 0.2873 | 0.1860 | 0.2258 | 0.9350 |
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+ | 131.3839 | 3.7840 | 6500 | 133.8098 | 0.2778 | 0.1666 | 0.2083 | 0.9350 |
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+ | 129.797 | 4.0751 | 7000 | 132.1772 | 0.2916 | 0.1961 | 0.2345 | 0.9369 |
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+ | 127.666 | 4.3662 | 7500 | 130.7785 | 0.3195 | 0.2108 | 0.2540 | 0.9380 |
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+ | 126.5971 | 4.6573 | 8000 | 129.6297 | 0.3243 | 0.2394 | 0.2754 | 0.9392 |
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+ | 125.6906 | 4.9483 | 8500 | 128.3762 | 0.3276 | 0.2613 | 0.2907 | 0.9403 |
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+ | 124.4524 | 5.2394 | 9000 | 127.9805 | 0.3305 | 0.2536 | 0.2870 | 0.9410 |
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+ | 122.7245 | 5.5305 | 9500 | 126.6189 | 0.3384 | 0.2789 | 0.3058 | 0.9418 |
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+ | 121.9463 | 5.8216 | 10000 | 125.9754 | 0.3504 | 0.2995 | 0.3230 | 0.9422 |
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+ | 120.7658 | 6.1126 | 10500 | 125.1251 | 0.3666 | 0.2923 | 0.3253 | 0.9438 |
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+ | 119.7118 | 6.4037 | 11000 | 124.2384 | 0.3649 | 0.3300 | 0.3466 | 0.9451 |
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+ | 119.2242 | 6.6948 | 11500 | 123.6015 | 0.3891 | 0.3443 | 0.3653 | 0.9456 |
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+ | 118.7415 | 6.9859 | 12000 | 123.2859 | 0.4014 | 0.3474 | 0.3725 | 0.9462 |
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+ | 117.3371 | 7.2770 | 12500 | 122.5413 | 0.4022 | 0.3730 | 0.3870 | 0.9480 |
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+ | 116.8112 | 7.5680 | 13000 | 122.0957 | 0.4210 | 0.3542 | 0.3847 | 0.9477 |
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+ | 116.6829 | 7.8591 | 13500 | 121.7368 | 0.4190 | 0.3939 | 0.4060 | 0.9491 |
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+ | 116.0694 | 8.1502 | 14000 | 121.2429 | 0.4264 | 0.4098 | 0.4179 | 0.9501 |
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+ | 115.1811 | 8.4413 | 14500 | 120.9883 | 0.4293 | 0.4087 | 0.4188 | 0.9497 |
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+ | 115.0686 | 8.7324 | 15000 | 120.9065 | 0.4307 | 0.3806 | 0.4041 | 0.9493 |
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+ | 114.9443 | 9.0234 | 15500 | 120.5293 | 0.4313 | 0.4005 | 0.4153 | 0.9499 |
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+ | 114.2954 | 9.3145 | 16000 | 120.3870 | 0.4287 | 0.4160 | 0.4222 | 0.9501 |
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+ | 114.2907 | 9.6056 | 16500 | 120.1462 | 0.4380 | 0.4135 | 0.4254 | 0.9511 |
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+ | 114.2952 | 9.8967 | 17000 | 120.2460 | 0.4418 | 0.4111 | 0.4259 | 0.9508 |
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  ### Framework versions
eval_result_ner.json CHANGED
@@ -1 +1 @@
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