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  1. README.md +111 -46
  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:
11
- - generated_from_trainer
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  model-index:
13
  - name: scenario-non-kd-scr-ner-full-mdeberta_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 [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: 0.2996
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- - Precision: 0.6155
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- - Recall: 0.6078
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- - F1: 0.6116
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- - Accuracy: 0.9626
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30
  ## Model description
31
 
@@ -56,44 +56,109 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
58
  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
59
- | 0.3117 | 0.2910 | 500 | 0.2393 | 0.3435 | 0.2104 | 0.2609 | 0.9346 |
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- | 0.1947 | 0.5821 | 1000 | 0.1952 | 0.4166 | 0.3151 | 0.3588 | 0.9443 |
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- | 0.1467 | 0.8731 | 1500 | 0.1617 | 0.4625 | 0.4578 | 0.4601 | 0.9517 |
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- | 0.1106 | 1.1641 | 2000 | 0.1519 | 0.5134 | 0.5318 | 0.5225 | 0.9559 |
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- | 0.0863 | 1.4552 | 2500 | 0.1606 | 0.5690 | 0.5041 | 0.5346 | 0.9581 |
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- | 0.08 | 1.7462 | 3000 | 0.1478 | 0.5338 | 0.5719 | 0.5522 | 0.9592 |
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- | 0.0707 | 2.0373 | 3500 | 0.1579 | 0.5849 | 0.5705 | 0.5776 | 0.9608 |
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- | 0.0444 | 2.3283 | 4000 | 0.1623 | 0.5820 | 0.5638 | 0.5728 | 0.9602 |
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- | 0.0472 | 2.6193 | 4500 | 0.1573 | 0.5723 | 0.5944 | 0.5832 | 0.9609 |
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- | 0.0461 | 2.9104 | 5000 | 0.1556 | 0.5929 | 0.6042 | 0.5985 | 0.9620 |
69
- | 0.0279 | 3.2014 | 5500 | 0.1835 | 0.6101 | 0.6100 | 0.6101 | 0.9629 |
70
- | 0.0258 | 3.4924 | 6000 | 0.1802 | 0.6218 | 0.5936 | 0.6074 | 0.9627 |
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- | 0.025 | 3.7835 | 6500 | 0.1855 | 0.6099 | 0.5972 | 0.6035 | 0.9625 |
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- | 0.0245 | 4.0745 | 7000 | 0.1934 | 0.5959 | 0.6240 | 0.6096 | 0.9628 |
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- | 0.0141 | 4.3655 | 7500 | 0.2029 | 0.6105 | 0.6158 | 0.6131 | 0.9633 |
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- | 0.0161 | 4.6566 | 8000 | 0.2164 | 0.6242 | 0.5888 | 0.6060 | 0.9626 |
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- | 0.0163 | 4.9476 | 8500 | 0.2095 | 0.5956 | 0.6070 | 0.6012 | 0.9620 |
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- | 0.0101 | 5.2386 | 9000 | 0.2343 | 0.6382 | 0.5965 | 0.6166 | 0.9636 |
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- | 0.0094 | 5.5297 | 9500 | 0.2260 | 0.6035 | 0.6080 | 0.6057 | 0.9619 |
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- | 0.0108 | 5.8207 | 10000 | 0.2264 | 0.6214 | 0.5806 | 0.6003 | 0.9611 |
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- | 0.01 | 6.1118 | 10500 | 0.2459 | 0.6283 | 0.5921 | 0.6097 | 0.9631 |
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- | 0.0065 | 6.4028 | 11000 | 0.2408 | 0.6146 | 0.6027 | 0.6086 | 0.9629 |
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- | 0.0069 | 6.6938 | 11500 | 0.2501 | 0.6094 | 0.6074 | 0.6084 | 0.9625 |
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- | 0.0077 | 6.9849 | 12000 | 0.2474 | 0.6140 | 0.6165 | 0.6153 | 0.9627 |
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- | 0.0048 | 7.2759 | 12500 | 0.2606 | 0.6124 | 0.6203 | 0.6163 | 0.9628 |
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- | 0.0046 | 7.5669 | 13000 | 0.2692 | 0.6082 | 0.6058 | 0.6070 | 0.9625 |
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- | 0.0054 | 7.8580 | 13500 | 0.2688 | 0.6095 | 0.6117 | 0.6106 | 0.9625 |
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- | 0.0046 | 8.1490 | 14000 | 0.2734 | 0.6250 | 0.5852 | 0.6044 | 0.9624 |
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- | 0.0038 | 8.4400 | 14500 | 0.2705 | 0.6234 | 0.6015 | 0.6123 | 0.9626 |
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- | 0.0042 | 8.7311 | 15000 | 0.2728 | 0.6058 | 0.6084 | 0.6071 | 0.9619 |
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- | 0.0041 | 9.0221 | 15500 | 0.2823 | 0.6228 | 0.5927 | 0.6074 | 0.9625 |
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- | 0.0031 | 9.3132 | 16000 | 0.2814 | 0.6045 | 0.6217 | 0.6130 | 0.9623 |
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- | 0.0029 | 9.6042 | 16500 | 0.2879 | 0.5942 | 0.6314 | 0.6122 | 0.9624 |
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- | 0.003 | 9.8952 | 17000 | 0.2998 | 0.6360 | 0.5814 | 0.6075 | 0.9622 |
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- | 0.0027 | 10.1863 | 17500 | 0.2819 | 0.6014 | 0.6129 | 0.6071 | 0.9621 |
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- | 0.0023 | 10.4773 | 18000 | 0.3042 | 0.6146 | 0.5983 | 0.6063 | 0.9626 |
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- | 0.0029 | 10.7683 | 18500 | 0.2878 | 0.6121 | 0.6089 | 0.6105 | 0.9624 |
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- | 0.0025 | 11.0594 | 19000 | 0.2996 | 0.6155 | 0.6078 | 0.6116 | 0.9626 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
 
2
  library_name: transformers
3
  license: mit
4
+ base_model: microsoft/mdeberta-v3-base
5
+ tags:
6
+ - generated_from_trainer
7
  metrics:
8
  - precision
9
  - recall
10
  - f1
11
  - accuracy
 
 
12
  model-index:
13
  - name: scenario-non-kd-scr-ner-full-mdeberta_data-univner_full66
14
  results: []
 
21
 
22
  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:
24
+ - Loss: 0.3771
25
+ - Precision: 0.6326
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+ - Recall: 0.6068
27
+ - F1: 0.6194
28
+ - Accuracy: 0.9635
29
 
30
  ## Model description
31
 
 
56
 
57
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
58
  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3111 | 0.2910 | 500 | 0.2388 | 0.3312 | 0.2252 | 0.2681 | 0.9340 |
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+ | 0.1965 | 0.5821 | 1000 | 0.2031 | 0.4073 | 0.2821 | 0.3333 | 0.9425 |
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+ | 0.1493 | 0.8731 | 1500 | 0.1645 | 0.4478 | 0.4533 | 0.4505 | 0.9510 |
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+ | 0.1123 | 1.1641 | 2000 | 0.1545 | 0.5054 | 0.5240 | 0.5146 | 0.9559 |
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+ | 0.0879 | 1.4552 | 2500 | 0.1620 | 0.5659 | 0.4996 | 0.5307 | 0.9578 |
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+ | 0.0809 | 1.7462 | 3000 | 0.1505 | 0.5293 | 0.5598 | 0.5441 | 0.9585 |
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+ | 0.0722 | 2.0373 | 3500 | 0.1621 | 0.5762 | 0.5591 | 0.5675 | 0.9603 |
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+ | 0.0457 | 2.3283 | 4000 | 0.1587 | 0.5676 | 0.5806 | 0.5740 | 0.9602 |
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+ | 0.0478 | 2.6193 | 4500 | 0.1604 | 0.5524 | 0.5852 | 0.5683 | 0.9599 |
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+ | 0.047 | 2.9104 | 5000 | 0.1550 | 0.5787 | 0.5865 | 0.5826 | 0.9612 |
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+ | 0.0296 | 3.2014 | 5500 | 0.1791 | 0.5980 | 0.5989 | 0.5985 | 0.9622 |
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+ | 0.0271 | 3.4924 | 6000 | 0.1783 | 0.6204 | 0.5865 | 0.6030 | 0.9623 |
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+ | 0.0272 | 3.7835 | 6500 | 0.1794 | 0.5971 | 0.6074 | 0.6022 | 0.9620 |
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+ | 0.0259 | 4.0745 | 7000 | 0.1968 | 0.6020 | 0.6136 | 0.6077 | 0.9627 |
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+ | 0.0155 | 4.3655 | 7500 | 0.2028 | 0.5972 | 0.6053 | 0.6012 | 0.9626 |
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+ | 0.0175 | 4.6566 | 8000 | 0.2095 | 0.6046 | 0.5754 | 0.5896 | 0.9615 |
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+ | 0.0173 | 4.9476 | 8500 | 0.2121 | 0.5892 | 0.5931 | 0.5912 | 0.9619 |
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+ | 0.0103 | 5.2386 | 9000 | 0.2274 | 0.6150 | 0.6024 | 0.6086 | 0.9627 |
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+ | 0.0102 | 5.5297 | 9500 | 0.2231 | 0.6210 | 0.5905 | 0.6054 | 0.9623 |
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+ | 0.0115 | 5.8207 | 10000 | 0.2175 | 0.6135 | 0.5966 | 0.6049 | 0.9624 |
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+ | 0.0096 | 6.1118 | 10500 | 0.2394 | 0.5723 | 0.6358 | 0.6024 | 0.9613 |
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+ | 0.0068 | 6.4028 | 11000 | 0.2474 | 0.6202 | 0.5957 | 0.6077 | 0.9629 |
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+ | 0.007 | 6.6938 | 11500 | 0.2500 | 0.6095 | 0.6104 | 0.6100 | 0.9629 |
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+ | 0.0085 | 6.9849 | 12000 | 0.2514 | 0.5995 | 0.5992 | 0.5994 | 0.9624 |
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+ | 0.0054 | 7.2759 | 12500 | 0.2613 | 0.6161 | 0.5956 | 0.6057 | 0.9627 |
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+ | 0.0052 | 7.5669 | 13000 | 0.2684 | 0.6083 | 0.6077 | 0.6080 | 0.9626 |
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+ | 0.0056 | 7.8580 | 13500 | 0.2655 | 0.5795 | 0.6211 | 0.5996 | 0.9612 |
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+ | 0.0048 | 8.1490 | 14000 | 0.2718 | 0.5925 | 0.6057 | 0.5990 | 0.9612 |
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+ | 0.004 | 8.4400 | 14500 | 0.2794 | 0.6129 | 0.6094 | 0.6112 | 0.9624 |
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+ | 0.0041 | 8.7311 | 15000 | 0.2811 | 0.6038 | 0.5937 | 0.5987 | 0.9618 |
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+ | 0.0045 | 9.0221 | 15500 | 0.2814 | 0.6154 | 0.5878 | 0.6013 | 0.9622 |
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+ | 0.0033 | 9.3132 | 16000 | 0.2879 | 0.5954 | 0.6203 | 0.6076 | 0.9621 |
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+ | 0.0034 | 9.6042 | 16500 | 0.2963 | 0.6251 | 0.5956 | 0.6100 | 0.9631 |
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+ | 0.0032 | 9.8952 | 17000 | 0.2935 | 0.5800 | 0.6321 | 0.6049 | 0.9615 |
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+ | 0.0031 | 10.1863 | 17500 | 0.2909 | 0.6003 | 0.6194 | 0.6097 | 0.9625 |
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+ | 0.0025 | 10.4773 | 18000 | 0.2991 | 0.5960 | 0.6096 | 0.6027 | 0.9619 |
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+ | 0.0026 | 10.7683 | 18500 | 0.2983 | 0.6080 | 0.6086 | 0.6083 | 0.9623 |
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+ | 0.0027 | 11.0594 | 19000 | 0.2975 | 0.6146 | 0.6054 | 0.6100 | 0.9624 |
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+ | 0.0016 | 11.3504 | 19500 | 0.3092 | 0.6172 | 0.5900 | 0.6033 | 0.9626 |
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+ | 0.0023 | 11.6414 | 20000 | 0.3168 | 0.6292 | 0.5918 | 0.6100 | 0.9630 |
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+ | 0.0025 | 11.9325 | 20500 | 0.3036 | 0.6216 | 0.5972 | 0.6091 | 0.9627 |
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+ | 0.0015 | 12.2235 | 21000 | 0.3222 | 0.6164 | 0.5918 | 0.6039 | 0.9621 |
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+ | 0.0017 | 12.5146 | 21500 | 0.3158 | 0.6127 | 0.6089 | 0.6108 | 0.9626 |
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+ | 0.0018 | 12.8056 | 22000 | 0.3223 | 0.6023 | 0.6008 | 0.6015 | 0.9623 |
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+ | 0.0019 | 13.0966 | 22500 | 0.3197 | 0.6047 | 0.5910 | 0.5977 | 0.9618 |
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+ | 0.0013 | 13.3877 | 23000 | 0.3190 | 0.6128 | 0.5985 | 0.6055 | 0.9620 |
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+ | 0.0013 | 13.6787 | 23500 | 0.3279 | 0.6144 | 0.5904 | 0.6022 | 0.9622 |
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+ | 0.0014 | 13.9697 | 24000 | 0.3278 | 0.6181 | 0.6089 | 0.6135 | 0.9624 |
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+ | 0.0011 | 14.2608 | 24500 | 0.3384 | 0.6119 | 0.5927 | 0.6022 | 0.9623 |
108
+ | 0.0014 | 14.5518 | 25000 | 0.3270 | 0.6225 | 0.5993 | 0.6107 | 0.9621 |
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+ | 0.0015 | 14.8428 | 25500 | 0.3320 | 0.5971 | 0.5969 | 0.5970 | 0.9616 |
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+ | 0.001 | 15.1339 | 26000 | 0.3442 | 0.6174 | 0.5936 | 0.6053 | 0.9623 |
111
+ | 0.0008 | 15.4249 | 26500 | 0.3344 | 0.6091 | 0.6154 | 0.6122 | 0.9624 |
112
+ | 0.0009 | 15.7159 | 27000 | 0.3347 | 0.6242 | 0.5982 | 0.6109 | 0.9625 |
113
+ | 0.0011 | 16.0070 | 27500 | 0.3407 | 0.6225 | 0.6064 | 0.6143 | 0.9625 |
114
+ | 0.0008 | 16.2980 | 28000 | 0.3376 | 0.6217 | 0.6081 | 0.6148 | 0.9626 |
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+ | 0.0008 | 16.5891 | 28500 | 0.3476 | 0.6030 | 0.6130 | 0.6080 | 0.9627 |
116
+ | 0.0009 | 16.8801 | 29000 | 0.3390 | 0.6224 | 0.5988 | 0.6103 | 0.9626 |
117
+ | 0.0009 | 17.1711 | 29500 | 0.3427 | 0.6094 | 0.6195 | 0.6144 | 0.9624 |
118
+ | 0.0006 | 17.4622 | 30000 | 0.3451 | 0.6200 | 0.6126 | 0.6163 | 0.9629 |
119
+ | 0.0004 | 17.7532 | 30500 | 0.3485 | 0.6190 | 0.6078 | 0.6134 | 0.9630 |
120
+ | 0.0008 | 18.0442 | 31000 | 0.3532 | 0.6237 | 0.5973 | 0.6102 | 0.9628 |
121
+ | 0.0007 | 18.3353 | 31500 | 0.3454 | 0.6143 | 0.6019 | 0.6080 | 0.9628 |
122
+ | 0.0006 | 18.6263 | 32000 | 0.3426 | 0.6253 | 0.6093 | 0.6172 | 0.9629 |
123
+ | 0.0006 | 18.9173 | 32500 | 0.3503 | 0.6205 | 0.6018 | 0.6110 | 0.9628 |
124
+ | 0.0004 | 19.2084 | 33000 | 0.3580 | 0.6344 | 0.6034 | 0.6185 | 0.9633 |
125
+ | 0.0004 | 19.4994 | 33500 | 0.3527 | 0.6072 | 0.6203 | 0.6137 | 0.9626 |
126
+ | 0.0006 | 19.7905 | 34000 | 0.3473 | 0.6173 | 0.6115 | 0.6144 | 0.9628 |
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+ | 0.0005 | 20.0815 | 34500 | 0.3550 | 0.6208 | 0.6106 | 0.6157 | 0.9630 |
128
+ | 0.0003 | 20.3725 | 35000 | 0.3623 | 0.6153 | 0.6074 | 0.6113 | 0.9626 |
129
+ | 0.0004 | 20.6636 | 35500 | 0.3639 | 0.6264 | 0.5989 | 0.6123 | 0.9628 |
130
+ | 0.0005 | 20.9546 | 36000 | 0.3505 | 0.6167 | 0.6179 | 0.6173 | 0.9631 |
131
+ | 0.0004 | 21.2456 | 36500 | 0.3570 | 0.6237 | 0.6093 | 0.6164 | 0.9631 |
132
+ | 0.0003 | 21.5367 | 37000 | 0.3608 | 0.6302 | 0.6089 | 0.6194 | 0.9634 |
133
+ | 0.0005 | 21.8277 | 37500 | 0.3597 | 0.6158 | 0.6027 | 0.6092 | 0.9626 |
134
+ | 0.0002 | 22.1187 | 38000 | 0.3595 | 0.6252 | 0.6070 | 0.6160 | 0.9632 |
135
+ | 0.0003 | 22.4098 | 38500 | 0.3615 | 0.6186 | 0.6135 | 0.6160 | 0.9631 |
136
+ | 0.0002 | 22.7008 | 39000 | 0.3630 | 0.6311 | 0.5983 | 0.6143 | 0.9633 |
137
+ | 0.0003 | 22.9919 | 39500 | 0.3694 | 0.6344 | 0.5825 | 0.6073 | 0.9629 |
138
+ | 0.0001 | 23.2829 | 40000 | 0.3673 | 0.6284 | 0.6071 | 0.6176 | 0.9634 |
139
+ | 0.0002 | 23.5739 | 40500 | 0.3693 | 0.6187 | 0.6063 | 0.6124 | 0.9630 |
140
+ | 0.0003 | 23.8650 | 41000 | 0.3704 | 0.6153 | 0.6087 | 0.6120 | 0.9630 |
141
+ | 0.0001 | 24.1560 | 41500 | 0.3663 | 0.6219 | 0.6070 | 0.6143 | 0.9633 |
142
+ | 0.0001 | 24.4470 | 42000 | 0.3667 | 0.6228 | 0.6161 | 0.6194 | 0.9637 |
143
+ | 0.0002 | 24.7381 | 42500 | 0.3736 | 0.6456 | 0.5926 | 0.6179 | 0.9633 |
144
+ | 0.0002 | 25.0291 | 43000 | 0.3742 | 0.6280 | 0.5953 | 0.6112 | 0.9633 |
145
+ | 0.0001 | 25.3201 | 43500 | 0.3714 | 0.6217 | 0.6016 | 0.6115 | 0.9629 |
146
+ | 0.0002 | 25.6112 | 44000 | 0.3720 | 0.6348 | 0.5933 | 0.6133 | 0.9632 |
147
+ | 0.0001 | 25.9022 | 44500 | 0.3726 | 0.6136 | 0.6152 | 0.6144 | 0.9631 |
148
+ | 0.0001 | 26.1932 | 45000 | 0.3694 | 0.6366 | 0.5963 | 0.6158 | 0.9636 |
149
+ | 0.0001 | 26.4843 | 45500 | 0.3678 | 0.6113 | 0.6227 | 0.6170 | 0.9632 |
150
+ | 0.0001 | 26.7753 | 46000 | 0.3702 | 0.6348 | 0.6016 | 0.6178 | 0.9636 |
151
+ | 0.0001 | 27.0664 | 46500 | 0.3747 | 0.6323 | 0.5998 | 0.6156 | 0.9634 |
152
+ | 0.0001 | 27.3574 | 47000 | 0.3738 | 0.6352 | 0.6006 | 0.6174 | 0.9635 |
153
+ | 0.0001 | 27.6484 | 47500 | 0.3701 | 0.6215 | 0.6135 | 0.6174 | 0.9633 |
154
+ | 0.0001 | 27.9395 | 48000 | 0.3718 | 0.6252 | 0.6122 | 0.6186 | 0.9633 |
155
+ | 0.0001 | 28.2305 | 48500 | 0.3743 | 0.6308 | 0.6066 | 0.6184 | 0.9634 |
156
+ | 0.0 | 28.5215 | 49000 | 0.3785 | 0.6333 | 0.5957 | 0.6139 | 0.9634 |
157
+ | 0.0001 | 28.8126 | 49500 | 0.3764 | 0.6258 | 0.6087 | 0.6171 | 0.9633 |
158
+ | 0.0001 | 29.1036 | 50000 | 0.3761 | 0.6266 | 0.6103 | 0.6183 | 0.9634 |
159
+ | 0.0001 | 29.3946 | 50500 | 0.3770 | 0.6333 | 0.6051 | 0.6189 | 0.9634 |
160
+ | 0.0001 | 29.6857 | 51000 | 0.3780 | 0.6346 | 0.6037 | 0.6188 | 0.9635 |
161
+ | 0.0 | 29.9767 | 51500 | 0.3771 | 0.6326 | 0.6068 | 0.6194 | 0.9635 |
162
 
163
 
164
  ### Framework versions
eval_result_ner.json CHANGED
@@ -1 +1 @@
1
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