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scenario-kd-pre-ner-full-mdeberta_data-univner_full44

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deberta-v2
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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: microsoft/mdeberta-v3-base
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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-pre-ner-full-mdeberta_data-univner_full44
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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 [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2650
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- - Precision: 0.8107
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- - Recall: 0.8117
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- - F1: 0.8112
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- - Accuracy: 0.9806
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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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- | 1.3559 | 0.2911 | 500 | 0.7891 | 0.4246 | 0.3525 | 0.3852 | 0.9433 |
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- | 0.7017 | 0.5822 | 1000 | 0.5422 | 0.6316 | 0.6298 | 0.6307 | 0.9646 |
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- | 0.528 | 0.8732 | 1500 | 0.4693 | 0.6856 | 0.6830 | 0.6843 | 0.9692 |
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- | 0.4354 | 1.1643 | 2000 | 0.4211 | 0.7101 | 0.7376 | 0.7236 | 0.9724 |
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- | 0.385 | 1.4554 | 2500 | 0.3893 | 0.7482 | 0.7374 | 0.7428 | 0.9747 |
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- | 0.3575 | 1.7465 | 3000 | 0.3713 | 0.7678 | 0.7331 | 0.7500 | 0.9752 |
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- | 0.3298 | 2.0375 | 3500 | 0.3550 | 0.7497 | 0.7800 | 0.7645 | 0.9761 |
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- | 0.2879 | 2.3286 | 4000 | 0.3492 | 0.7964 | 0.7367 | 0.7654 | 0.9763 |
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- | 0.2748 | 2.6197 | 4500 | 0.3272 | 0.7660 | 0.7924 | 0.7790 | 0.9782 |
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- | 0.2644 | 2.9108 | 5000 | 0.3192 | 0.7817 | 0.7811 | 0.7814 | 0.9779 |
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- | 0.2416 | 3.2019 | 5500 | 0.3239 | 0.8004 | 0.7681 | 0.7839 | 0.9782 |
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- | 0.2303 | 3.4929 | 6000 | 0.3085 | 0.7846 | 0.7966 | 0.7905 | 0.9787 |
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- | 0.2252 | 3.7840 | 6500 | 0.3051 | 0.7973 | 0.7883 | 0.7928 | 0.9787 |
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- | 0.2159 | 4.0751 | 7000 | 0.3045 | 0.7987 | 0.7908 | 0.7948 | 0.9790 |
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- | 0.2067 | 4.3662 | 7500 | 0.2979 | 0.7969 | 0.7943 | 0.7956 | 0.9793 |
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- | 0.2028 | 4.6573 | 8000 | 0.2924 | 0.7855 | 0.8132 | 0.7991 | 0.9792 |
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- | 0.1985 | 4.9483 | 8500 | 0.2904 | 0.8008 | 0.7986 | 0.7997 | 0.9791 |
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- | 0.1867 | 5.2394 | 9000 | 0.2884 | 0.8 | 0.8033 | 0.8017 | 0.9797 |
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- | 0.1838 | 5.5305 | 9500 | 0.2841 | 0.7997 | 0.8220 | 0.8107 | 0.9800 |
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- | 0.1838 | 5.8216 | 10000 | 0.2810 | 0.7895 | 0.8165 | 0.8028 | 0.9798 |
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- | 0.1786 | 6.1126 | 10500 | 0.2767 | 0.8065 | 0.8150 | 0.8108 | 0.9802 |
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- | 0.1719 | 6.4037 | 11000 | 0.2790 | 0.8133 | 0.8057 | 0.8095 | 0.9803 |
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- | 0.1706 | 6.6948 | 11500 | 0.2795 | 0.8140 | 0.7983 | 0.8061 | 0.9802 |
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- | 0.1695 | 6.9859 | 12000 | 0.2723 | 0.8124 | 0.8121 | 0.8123 | 0.9807 |
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- | 0.1638 | 7.2770 | 12500 | 0.2726 | 0.8070 | 0.8078 | 0.8074 | 0.9803 |
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- | 0.162 | 7.5680 | 13000 | 0.2724 | 0.8118 | 0.8173 | 0.8146 | 0.9807 |
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- | 0.1619 | 7.8591 | 13500 | 0.2678 | 0.8018 | 0.8235 | 0.8125 | 0.9805 |
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- | 0.1594 | 8.1502 | 14000 | 0.2719 | 0.8103 | 0.8068 | 0.8086 | 0.9800 |
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- | 0.1571 | 8.4413 | 14500 | 0.2688 | 0.8097 | 0.8127 | 0.8112 | 0.9805 |
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- | 0.1585 | 8.7324 | 15000 | 0.2673 | 0.8126 | 0.8150 | 0.8138 | 0.9806 |
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- | 0.1546 | 9.0234 | 15500 | 0.2658 | 0.8105 | 0.8120 | 0.8112 | 0.9805 |
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- | 0.1534 | 9.3145 | 16000 | 0.2652 | 0.8101 | 0.8198 | 0.8149 | 0.9807 |
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- | 0.1535 | 9.6056 | 16500 | 0.2646 | 0.8097 | 0.8140 | 0.8119 | 0.9807 |
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- | 0.1531 | 9.8967 | 17000 | 0.2650 | 0.8107 | 0.8117 | 0.8112 | 0.9806 |
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  ### Framework versions
 
1
  ---
 
2
  library_name: transformers
3
  license: mit
4
+ base_model: microsoft/mdeberta-v3-base
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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-pre-ner-full-mdeberta_data-univner_full44
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  results: []
 
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  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
23
  It achieves the following results on the evaluation set:
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+ - Loss: 46.6459
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+ - Precision: 0.8272
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+ - Recall: 0.8335
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+ - F1: 0.8303
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+ - Accuracy: 0.9822
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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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+ | 149.9231 | 0.2911 | 500 | 110.8748 | 0.5070 | 0.1208 | 0.1951 | 0.9347 |
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+ | 101.1776 | 0.5822 | 1000 | 93.5222 | 0.7123 | 0.6586 | 0.6844 | 0.9701 |
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+ | 89.9222 | 0.8732 | 1500 | 86.5450 | 0.7487 | 0.7276 | 0.7380 | 0.9748 |
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+ | 83.6167 | 1.1643 | 2000 | 81.4135 | 0.7818 | 0.7501 | 0.7656 | 0.9769 |
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+ | 78.8225 | 1.4554 | 2500 | 77.5955 | 0.7905 | 0.7547 | 0.7722 | 0.9777 |
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+ | 75.3094 | 1.7465 | 3000 | 74.1885 | 0.7825 | 0.7798 | 0.7812 | 0.9783 |
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+ | 71.9149 | 2.0375 | 3500 | 71.4168 | 0.7893 | 0.8020 | 0.7956 | 0.9790 |
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+ | 68.8017 | 2.3286 | 4000 | 68.6904 | 0.8194 | 0.7778 | 0.7981 | 0.9794 |
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+ | 66.2935 | 2.6197 | 4500 | 66.3018 | 0.7981 | 0.8070 | 0.8025 | 0.9802 |
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+ | 64.1282 | 2.9108 | 5000 | 64.3227 | 0.7988 | 0.8130 | 0.8059 | 0.9803 |
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+ | 61.983 | 3.2019 | 5500 | 62.6362 | 0.8141 | 0.8114 | 0.8128 | 0.9808 |
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+ | 60.0914 | 3.4929 | 6000 | 60.8145 | 0.8106 | 0.8149 | 0.8127 | 0.9808 |
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+ | 58.497 | 3.7840 | 6500 | 59.2819 | 0.8126 | 0.8158 | 0.8142 | 0.9812 |
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+ | 57.0173 | 4.0751 | 7000 | 58.0187 | 0.8126 | 0.7990 | 0.8058 | 0.9804 |
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+ | 55.5793 | 4.3662 | 7500 | 56.7794 | 0.8033 | 0.8240 | 0.8135 | 0.9808 |
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+ | 54.4031 | 4.6573 | 8000 | 55.5089 | 0.8072 | 0.8287 | 0.8178 | 0.9812 |
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+ | 53.2147 | 4.9483 | 8500 | 54.5450 | 0.8128 | 0.8094 | 0.8111 | 0.9810 |
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+ | 52.0438 | 5.2394 | 9000 | 53.6043 | 0.8145 | 0.8222 | 0.8184 | 0.9814 |
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+ | 51.102 | 5.5305 | 9500 | 52.6326 | 0.8100 | 0.8261 | 0.818 | 0.9811 |
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+ | 50.3841 | 5.8216 | 10000 | 51.8428 | 0.8138 | 0.8300 | 0.8219 | 0.9815 |
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+ | 49.4812 | 6.1126 | 10500 | 51.1615 | 0.8192 | 0.8296 | 0.8244 | 0.9819 |
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+ | 48.7273 | 6.4037 | 11000 | 50.4750 | 0.8156 | 0.8201 | 0.8178 | 0.9813 |
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+ | 48.1157 | 6.6948 | 11500 | 49.8869 | 0.8190 | 0.8259 | 0.8224 | 0.9818 |
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+ | 47.4821 | 6.9859 | 12000 | 49.2946 | 0.8203 | 0.8279 | 0.8241 | 0.9819 |
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+ | 46.889 | 7.2770 | 12500 | 48.8428 | 0.8178 | 0.8224 | 0.8201 | 0.9816 |
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+ | 46.3939 | 7.5680 | 13000 | 48.3821 | 0.8264 | 0.8224 | 0.8244 | 0.9819 |
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+ | 46.0878 | 7.8591 | 13500 | 47.9867 | 0.8210 | 0.8272 | 0.8241 | 0.9817 |
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+ | 45.669 | 8.1502 | 14000 | 47.6715 | 0.8207 | 0.8257 | 0.8232 | 0.9818 |
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+ | 45.3064 | 8.4413 | 14500 | 47.3744 | 0.8167 | 0.8336 | 0.8251 | 0.9818 |
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+ | 45.0768 | 8.7324 | 15000 | 47.1812 | 0.8221 | 0.8235 | 0.8228 | 0.9821 |
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+ | 44.8212 | 9.0234 | 15500 | 46.9769 | 0.8172 | 0.8274 | 0.8223 | 0.9816 |
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+ | 44.6107 | 9.3145 | 16000 | 46.8141 | 0.8204 | 0.8298 | 0.8250 | 0.9819 |
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+ | 44.4495 | 9.6056 | 16500 | 46.7872 | 0.8189 | 0.8285 | 0.8236 | 0.9819 |
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+ | 44.51 | 9.8967 | 17000 | 46.6459 | 0.8272 | 0.8335 | 0.8303 | 0.9822 |
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  ### Framework versions
eval_result_ner.json CHANGED
@@ -1 +1 @@
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