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scenario-kd-pre-ner-full_data-univner_full44

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xlm-roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +111 -59
  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 @@
1
  ---
2
- base_model: FacebookAI/xlm-roberta-base
3
  library_name: transformers
4
  license: mit
 
 
 
5
  metrics:
6
  - precision
7
  - recall
8
  - f1
9
  - accuracy
10
- tags:
11
- - generated_from_trainer
12
  model-index:
13
  - name: scenario-kd-pre-ner-full_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 [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
23
  It achieves the following results on the evaluation set:
24
- - Loss: 0.4381
25
- - Precision: 0.8004
26
- - Recall: 0.7801
27
- - F1: 0.7902
28
- - Accuracy: 0.9786
29
 
30
  ## Model description
31
 
@@ -56,57 +56,109 @@ The following hyperparameters were used during training:
56
 
57
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
58
  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
59
- | 1.4593 | 0.5828 | 500 | 0.8367 | 0.6935 | 0.6559 | 0.6742 | 0.9682 |
60
- | 0.7232 | 1.1655 | 1000 | 0.7569 | 0.7339 | 0.6980 | 0.7155 | 0.9724 |
61
- | 0.594 | 1.7483 | 1500 | 0.6330 | 0.7335 | 0.7451 | 0.7392 | 0.9741 |
62
- | 0.4986 | 2.3310 | 2000 | 0.6003 | 0.7291 | 0.7552 | 0.7419 | 0.9746 |
63
- | 0.446 | 2.9138 | 2500 | 0.5729 | 0.7403 | 0.7601 | 0.7501 | 0.9747 |
64
- | 0.385 | 3.4965 | 3000 | 0.5584 | 0.7441 | 0.7617 | 0.7528 | 0.9757 |
65
- | 0.3605 | 4.0793 | 3500 | 0.5602 | 0.7615 | 0.7575 | 0.7595 | 0.9758 |
66
- | 0.3172 | 4.6620 | 4000 | 0.5417 | 0.7546 | 0.7725 | 0.7634 | 0.9764 |
67
- | 0.3061 | 5.2448 | 4500 | 0.5329 | 0.7884 | 0.7485 | 0.7680 | 0.9769 |
68
- | 0.2856 | 5.8275 | 5000 | 0.5194 | 0.7837 | 0.7618 | 0.7726 | 0.9769 |
69
- | 0.2642 | 6.4103 | 5500 | 0.5154 | 0.7622 | 0.7780 | 0.7700 | 0.9765 |
70
- | 0.2592 | 6.9930 | 6000 | 0.5193 | 0.7882 | 0.7572 | 0.7724 | 0.9764 |
71
- | 0.2401 | 7.5758 | 6500 | 0.5123 | 0.7727 | 0.7599 | 0.7663 | 0.9763 |
72
- | 0.2344 | 8.1585 | 7000 | 0.4987 | 0.7742 | 0.7736 | 0.7739 | 0.9771 |
73
- | 0.2234 | 8.7413 | 7500 | 0.4914 | 0.7894 | 0.7640 | 0.7764 | 0.9777 |
74
- | 0.2131 | 9.3240 | 8000 | 0.4856 | 0.7691 | 0.7827 | 0.7758 | 0.9770 |
75
- | 0.2089 | 9.9068 | 8500 | 0.4898 | 0.7895 | 0.7655 | 0.7773 | 0.9773 |
76
- | 0.1972 | 10.4895 | 9000 | 0.4860 | 0.7828 | 0.7726 | 0.7777 | 0.9775 |
77
- | 0.1942 | 11.0723 | 9500 | 0.4787 | 0.7807 | 0.7807 | 0.7807 | 0.9776 |
78
- | 0.1854 | 11.6550 | 10000 | 0.4858 | 0.7916 | 0.7635 | 0.7773 | 0.9771 |
79
- | 0.183 | 12.2378 | 10500 | 0.4739 | 0.7924 | 0.7800 | 0.7862 | 0.9779 |
80
- | 0.1781 | 12.8205 | 11000 | 0.4741 | 0.7990 | 0.7661 | 0.7822 | 0.9779 |
81
- | 0.1704 | 13.4033 | 11500 | 0.4622 | 0.7937 | 0.7719 | 0.7826 | 0.9784 |
82
- | 0.1698 | 13.9860 | 12000 | 0.4650 | 0.8000 | 0.7657 | 0.7825 | 0.9777 |
83
- | 0.1635 | 14.5688 | 12500 | 0.4604 | 0.7913 | 0.7778 | 0.7845 | 0.9782 |
84
- | 0.1605 | 15.1515 | 13000 | 0.4656 | 0.7990 | 0.7605 | 0.7793 | 0.9774 |
85
- | 0.1559 | 15.7343 | 13500 | 0.4638 | 0.8001 | 0.7658 | 0.7826 | 0.9778 |
86
- | 0.1531 | 16.3170 | 14000 | 0.4550 | 0.7991 | 0.7735 | 0.7861 | 0.9780 |
87
- | 0.1519 | 16.8998 | 14500 | 0.4606 | 0.7949 | 0.7735 | 0.7841 | 0.9780 |
88
- | 0.1482 | 17.4825 | 15000 | 0.4483 | 0.7947 | 0.7831 | 0.7889 | 0.9787 |
89
- | 0.1449 | 18.0653 | 15500 | 0.4521 | 0.7947 | 0.7722 | 0.7833 | 0.9780 |
90
- | 0.1407 | 18.6480 | 16000 | 0.4508 | 0.7932 | 0.7728 | 0.7829 | 0.9780 |
91
- | 0.1415 | 19.2308 | 16500 | 0.4484 | 0.8031 | 0.7728 | 0.7876 | 0.9785 |
92
- | 0.1385 | 19.8135 | 17000 | 0.4461 | 0.7991 | 0.7774 | 0.7881 | 0.9785 |
93
- | 0.1358 | 20.3963 | 17500 | 0.4488 | 0.7970 | 0.7756 | 0.7862 | 0.9783 |
94
- | 0.1358 | 20.9790 | 18000 | 0.4431 | 0.8006 | 0.7772 | 0.7887 | 0.9787 |
95
- | 0.1325 | 21.5618 | 18500 | 0.4395 | 0.8053 | 0.7768 | 0.7908 | 0.9785 |
96
- | 0.1322 | 22.1445 | 19000 | 0.4461 | 0.7960 | 0.7725 | 0.7841 | 0.9780 |
97
- | 0.1296 | 22.7273 | 19500 | 0.4401 | 0.7988 | 0.7746 | 0.7866 | 0.9781 |
98
- | 0.1288 | 23.3100 | 20000 | 0.4416 | 0.7961 | 0.7690 | 0.7823 | 0.9781 |
99
- | 0.1271 | 23.8928 | 20500 | 0.4450 | 0.8024 | 0.7673 | 0.7844 | 0.9781 |
100
- | 0.1246 | 24.4755 | 21000 | 0.4403 | 0.7967 | 0.7703 | 0.7833 | 0.9782 |
101
- | 0.1254 | 25.0583 | 21500 | 0.4403 | 0.7976 | 0.7742 | 0.7857 | 0.9782 |
102
- | 0.1231 | 25.6410 | 22000 | 0.4438 | 0.8057 | 0.7694 | 0.7872 | 0.9783 |
103
- | 0.1228 | 26.2238 | 22500 | 0.4365 | 0.8058 | 0.7741 | 0.7896 | 0.9785 |
104
- | 0.1224 | 26.8065 | 23000 | 0.4325 | 0.7995 | 0.7806 | 0.7899 | 0.9787 |
105
- | 0.1211 | 27.3893 | 23500 | 0.4402 | 0.8058 | 0.7676 | 0.7862 | 0.9782 |
106
- | 0.1202 | 27.9720 | 24000 | 0.4378 | 0.8017 | 0.7689 | 0.7849 | 0.9784 |
107
- | 0.1201 | 28.5548 | 24500 | 0.4331 | 0.8000 | 0.7784 | 0.7890 | 0.9786 |
108
- | 0.12 | 29.1375 | 25000 | 0.4317 | 0.7999 | 0.7794 | 0.7895 | 0.9787 |
109
- | 0.1194 | 29.7203 | 25500 | 0.4381 | 0.8004 | 0.7801 | 0.7902 | 0.9786 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
110
 
111
 
112
  ### Framework versions
 
1
  ---
 
2
  library_name: transformers
3
  license: mit
4
+ base_model: FacebookAI/xlm-roberta-base
5
+ 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_data-univner_full44
14
  results: []
 
21
 
22
  This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
23
  It achieves the following results on the evaluation set:
24
+ - Loss: 0.4193
25
+ - Precision: 0.8281
26
+ - Recall: 0.8175
27
+ - F1: 0.8228
28
+ - Accuracy: 0.9817
29
 
30
  ## Model description
31
 
 
56
 
57
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
58
  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
59
+ | 1.4887 | 0.2910 | 500 | 0.8780 | 0.6630 | 0.6943 | 0.6783 | 0.9693 |
60
+ | 0.7434 | 0.5821 | 1000 | 0.7247 | 0.7040 | 0.7534 | 0.7279 | 0.9735 |
61
+ | 0.6458 | 0.8731 | 1500 | 0.6695 | 0.7248 | 0.7692 | 0.7463 | 0.9752 |
62
+ | 0.562 | 1.1641 | 2000 | 0.6301 | 0.7474 | 0.7830 | 0.7648 | 0.9768 |
63
+ | 0.5142 | 1.4552 | 2500 | 0.6432 | 0.7960 | 0.7413 | 0.7677 | 0.9773 |
64
+ | 0.4901 | 1.7462 | 3000 | 0.5939 | 0.7664 | 0.7689 | 0.7676 | 0.9768 |
65
+ | 0.4625 | 2.0373 | 3500 | 0.5650 | 0.7763 | 0.7899 | 0.7830 | 0.9784 |
66
+ | 0.4058 | 2.3283 | 4000 | 0.5634 | 0.7941 | 0.7807 | 0.7873 | 0.9786 |
67
+ | 0.3987 | 2.6193 | 4500 | 0.5539 | 0.7763 | 0.8000 | 0.7880 | 0.9789 |
68
+ | 0.3879 | 2.9104 | 5000 | 0.5344 | 0.7848 | 0.8042 | 0.7944 | 0.9789 |
69
+ | 0.3586 | 3.2014 | 5500 | 0.5491 | 0.7907 | 0.7915 | 0.7911 | 0.9791 |
70
+ | 0.3418 | 3.4924 | 6000 | 0.5209 | 0.7757 | 0.8121 | 0.7935 | 0.9789 |
71
+ | 0.3334 | 3.7835 | 6500 | 0.5221 | 0.7954 | 0.8000 | 0.7977 | 0.9797 |
72
+ | 0.3254 | 4.0745 | 7000 | 0.5208 | 0.8027 | 0.7943 | 0.7985 | 0.9793 |
73
+ | 0.3037 | 4.3655 | 7500 | 0.5119 | 0.7912 | 0.8000 | 0.7956 | 0.9797 |
74
+ | 0.2948 | 4.6566 | 8000 | 0.5057 | 0.7966 | 0.8062 | 0.8014 | 0.9796 |
75
+ | 0.2929 | 4.9476 | 8500 | 0.5024 | 0.8051 | 0.7987 | 0.8019 | 0.9796 |
76
+ | 0.2721 | 5.2386 | 9000 | 0.5030 | 0.8024 | 0.7868 | 0.7945 | 0.9796 |
77
+ | 0.2654 | 5.5297 | 9500 | 0.4919 | 0.8124 | 0.7940 | 0.8031 | 0.9800 |
78
+ | 0.2664 | 5.8207 | 10000 | 0.4992 | 0.7986 | 0.8121 | 0.8053 | 0.9798 |
79
+ | 0.2582 | 6.1118 | 10500 | 0.4874 | 0.8126 | 0.8000 | 0.8063 | 0.9802 |
80
+ | 0.2404 | 6.4028 | 11000 | 0.4980 | 0.8081 | 0.8000 | 0.8040 | 0.9803 |
81
+ | 0.2408 | 6.6938 | 11500 | 0.4875 | 0.8026 | 0.8036 | 0.8031 | 0.9800 |
82
+ | 0.2416 | 6.9849 | 12000 | 0.4830 | 0.8074 | 0.7982 | 0.8027 | 0.9799 |
83
+ | 0.2246 | 7.2759 | 12500 | 0.4750 | 0.8084 | 0.8116 | 0.8100 | 0.9805 |
84
+ | 0.2225 | 7.5669 | 13000 | 0.4839 | 0.8017 | 0.8162 | 0.8089 | 0.9807 |
85
+ | 0.2235 | 7.8580 | 13500 | 0.4676 | 0.8052 | 0.8134 | 0.8093 | 0.9807 |
86
+ | 0.2111 | 8.1490 | 14000 | 0.4718 | 0.8151 | 0.8022 | 0.8086 | 0.9806 |
87
+ | 0.207 | 8.4400 | 14500 | 0.4777 | 0.8036 | 0.8165 | 0.8100 | 0.9802 |
88
+ | 0.2063 | 8.7311 | 15000 | 0.4704 | 0.8250 | 0.7995 | 0.8120 | 0.9806 |
89
+ | 0.2051 | 9.0221 | 15500 | 0.4718 | 0.8027 | 0.8119 | 0.8073 | 0.9803 |
90
+ | 0.1922 | 9.3132 | 16000 | 0.4767 | 0.8154 | 0.8077 | 0.8115 | 0.9806 |
91
+ | 0.192 | 9.6042 | 16500 | 0.4735 | 0.8160 | 0.8114 | 0.8137 | 0.9811 |
92
+ | 0.1946 | 9.8952 | 17000 | 0.4711 | 0.8100 | 0.8176 | 0.8138 | 0.9807 |
93
+ | 0.1843 | 10.1863 | 17500 | 0.4666 | 0.8096 | 0.8113 | 0.8105 | 0.9808 |
94
+ | 0.1801 | 10.4773 | 18000 | 0.4606 | 0.8064 | 0.8150 | 0.8107 | 0.9805 |
95
+ | 0.1824 | 10.7683 | 18500 | 0.4573 | 0.8158 | 0.8133 | 0.8145 | 0.9810 |
96
+ | 0.1775 | 11.0594 | 19000 | 0.4733 | 0.8209 | 0.7951 | 0.8078 | 0.9803 |
97
+ | 0.1723 | 11.3504 | 19500 | 0.4567 | 0.8164 | 0.8168 | 0.8166 | 0.9813 |
98
+ | 0.1716 | 11.6414 | 20000 | 0.4596 | 0.8153 | 0.8062 | 0.8107 | 0.9809 |
99
+ | 0.1696 | 11.9325 | 20500 | 0.4553 | 0.8141 | 0.8250 | 0.8195 | 0.9813 |
100
+ | 0.165 | 12.2235 | 21000 | 0.4474 | 0.8225 | 0.8114 | 0.8169 | 0.9810 |
101
+ | 0.1609 | 12.5146 | 21500 | 0.4638 | 0.8189 | 0.8094 | 0.8141 | 0.9810 |
102
+ | 0.1648 | 12.8056 | 22000 | 0.4459 | 0.8122 | 0.8120 | 0.8121 | 0.9809 |
103
+ | 0.1599 | 13.0966 | 22500 | 0.4509 | 0.8184 | 0.8104 | 0.8144 | 0.9811 |
104
+ | 0.1556 | 13.3877 | 23000 | 0.4603 | 0.8167 | 0.8062 | 0.8114 | 0.9808 |
105
+ | 0.1559 | 13.6787 | 23500 | 0.4515 | 0.8163 | 0.8150 | 0.8157 | 0.9807 |
106
+ | 0.1546 | 13.9697 | 24000 | 0.4436 | 0.8089 | 0.8225 | 0.8157 | 0.9809 |
107
+ | 0.1487 | 14.2608 | 24500 | 0.4422 | 0.8114 | 0.8228 | 0.8170 | 0.9811 |
108
+ | 0.1503 | 14.5518 | 25000 | 0.4467 | 0.8180 | 0.8169 | 0.8174 | 0.9813 |
109
+ | 0.1485 | 14.8428 | 25500 | 0.4508 | 0.8098 | 0.8215 | 0.8156 | 0.9807 |
110
+ | 0.1466 | 15.1339 | 26000 | 0.4441 | 0.8157 | 0.8147 | 0.8152 | 0.9812 |
111
+ | 0.1432 | 15.4249 | 26500 | 0.4473 | 0.8242 | 0.8111 | 0.8176 | 0.9813 |
112
+ | 0.1431 | 15.7159 | 27000 | 0.4513 | 0.8194 | 0.8159 | 0.8177 | 0.9812 |
113
+ | 0.1444 | 16.0070 | 27500 | 0.4381 | 0.8166 | 0.8209 | 0.8188 | 0.9812 |
114
+ | 0.1373 | 16.2980 | 28000 | 0.4420 | 0.8163 | 0.8234 | 0.8199 | 0.9815 |
115
+ | 0.1375 | 16.5891 | 28500 | 0.4395 | 0.8203 | 0.8179 | 0.8191 | 0.9815 |
116
+ | 0.1405 | 16.8801 | 29000 | 0.4409 | 0.8227 | 0.8126 | 0.8176 | 0.9810 |
117
+ | 0.1369 | 17.1711 | 29500 | 0.4371 | 0.8259 | 0.8124 | 0.8191 | 0.9811 |
118
+ | 0.1345 | 17.4622 | 30000 | 0.4428 | 0.8248 | 0.8096 | 0.8171 | 0.9809 |
119
+ | 0.1356 | 17.7532 | 30500 | 0.4341 | 0.8275 | 0.8175 | 0.8225 | 0.9815 |
120
+ | 0.1323 | 18.0442 | 31000 | 0.4312 | 0.8229 | 0.8199 | 0.8214 | 0.9813 |
121
+ | 0.1302 | 18.3353 | 31500 | 0.4308 | 0.8242 | 0.8222 | 0.8232 | 0.9819 |
122
+ | 0.1302 | 18.6263 | 32000 | 0.4308 | 0.8217 | 0.8159 | 0.8188 | 0.9814 |
123
+ | 0.1308 | 18.9173 | 32500 | 0.4371 | 0.8274 | 0.8042 | 0.8156 | 0.9813 |
124
+ | 0.1289 | 19.2084 | 33000 | 0.4339 | 0.8305 | 0.8108 | 0.8206 | 0.9815 |
125
+ | 0.1273 | 19.4994 | 33500 | 0.4358 | 0.8176 | 0.8158 | 0.8167 | 0.9813 |
126
+ | 0.1271 | 19.7905 | 34000 | 0.4403 | 0.8229 | 0.8123 | 0.8175 | 0.9810 |
127
+ | 0.125 | 20.0815 | 34500 | 0.4280 | 0.8235 | 0.8201 | 0.8218 | 0.9815 |
128
+ | 0.1259 | 20.3725 | 35000 | 0.4341 | 0.8243 | 0.8124 | 0.8183 | 0.9812 |
129
+ | 0.1233 | 20.6636 | 35500 | 0.4327 | 0.8282 | 0.8075 | 0.8177 | 0.9812 |
130
+ | 0.1243 | 20.9546 | 36000 | 0.4253 | 0.8252 | 0.8192 | 0.8222 | 0.9814 |
131
+ | 0.1233 | 21.2456 | 36500 | 0.4333 | 0.8203 | 0.8114 | 0.8158 | 0.9812 |
132
+ | 0.1202 | 21.5367 | 37000 | 0.4253 | 0.8196 | 0.8168 | 0.8182 | 0.9814 |
133
+ | 0.1223 | 21.8277 | 37500 | 0.4234 | 0.8311 | 0.8142 | 0.8225 | 0.9815 |
134
+ | 0.1215 | 22.1187 | 38000 | 0.4203 | 0.8249 | 0.8197 | 0.8223 | 0.9818 |
135
+ | 0.1177 | 22.4098 | 38500 | 0.4200 | 0.8280 | 0.8225 | 0.8253 | 0.9818 |
136
+ | 0.1198 | 22.7008 | 39000 | 0.4257 | 0.8267 | 0.8199 | 0.8233 | 0.9818 |
137
+ | 0.1187 | 22.9919 | 39500 | 0.4253 | 0.8274 | 0.8222 | 0.8248 | 0.9817 |
138
+ | 0.1179 | 23.2829 | 40000 | 0.4261 | 0.8267 | 0.8163 | 0.8215 | 0.9812 |
139
+ | 0.1168 | 23.5739 | 40500 | 0.4203 | 0.8295 | 0.8156 | 0.8225 | 0.9815 |
140
+ | 0.1174 | 23.8650 | 41000 | 0.4216 | 0.8278 | 0.8145 | 0.8211 | 0.9816 |
141
+ | 0.1159 | 24.1560 | 41500 | 0.4226 | 0.8271 | 0.8207 | 0.8239 | 0.9818 |
142
+ | 0.1147 | 24.4470 | 42000 | 0.4274 | 0.8328 | 0.8147 | 0.8237 | 0.9814 |
143
+ | 0.1168 | 24.7381 | 42500 | 0.4240 | 0.8221 | 0.8147 | 0.8184 | 0.9815 |
144
+ | 0.1148 | 25.0291 | 43000 | 0.4222 | 0.8224 | 0.8119 | 0.8171 | 0.9814 |
145
+ | 0.1141 | 25.3201 | 43500 | 0.4179 | 0.8248 | 0.8197 | 0.8222 | 0.9818 |
146
+ | 0.1142 | 25.6112 | 44000 | 0.4204 | 0.8235 | 0.8178 | 0.8206 | 0.9815 |
147
+ | 0.1131 | 25.9022 | 44500 | 0.4190 | 0.8342 | 0.8222 | 0.8282 | 0.9818 |
148
+ | 0.114 | 26.1932 | 45000 | 0.4247 | 0.8289 | 0.8201 | 0.8245 | 0.9816 |
149
+ | 0.1119 | 26.4843 | 45500 | 0.4198 | 0.8290 | 0.8179 | 0.8234 | 0.9815 |
150
+ | 0.1132 | 26.7753 | 46000 | 0.4221 | 0.8224 | 0.8166 | 0.8195 | 0.9814 |
151
+ | 0.1125 | 27.0664 | 46500 | 0.4216 | 0.8306 | 0.8129 | 0.8216 | 0.9814 |
152
+ | 0.1103 | 27.3574 | 47000 | 0.4232 | 0.8260 | 0.8126 | 0.8193 | 0.9813 |
153
+ | 0.1113 | 27.6484 | 47500 | 0.4200 | 0.8321 | 0.8150 | 0.8235 | 0.9815 |
154
+ | 0.112 | 27.9395 | 48000 | 0.4186 | 0.8285 | 0.8227 | 0.8256 | 0.9817 |
155
+ | 0.111 | 28.2305 | 48500 | 0.4203 | 0.8326 | 0.8182 | 0.8254 | 0.9817 |
156
+ | 0.1095 | 28.5215 | 49000 | 0.4194 | 0.8300 | 0.8173 | 0.8236 | 0.9816 |
157
+ | 0.1104 | 28.8126 | 49500 | 0.4212 | 0.8246 | 0.8192 | 0.8219 | 0.9815 |
158
+ | 0.1098 | 29.1036 | 50000 | 0.4189 | 0.8278 | 0.8165 | 0.8221 | 0.9814 |
159
+ | 0.1097 | 29.3946 | 50500 | 0.4176 | 0.8322 | 0.8173 | 0.8247 | 0.9817 |
160
+ | 0.1098 | 29.6857 | 51000 | 0.4173 | 0.8252 | 0.8165 | 0.8208 | 0.9813 |
161
+ | 0.1104 | 29.9767 | 51500 | 0.4193 | 0.8281 | 0.8175 | 0.8228 | 0.9817 |
162
 
163
 
164
  ### Framework versions
eval_result_ner.json CHANGED
@@ -1 +1 @@
1
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