Hugging Face's logo

hts98
/
electra-ner

PyTorch
electra
Generated from Trainer
Eval Results
Model card Files Community
hts98 commited on
Commit
fa009b4
·
1 Parent(s): 436ca5d

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +185 -0
README.md ADDED
@@ -0,0 +1,185 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: NlpHUST/electra-base-vn
4
+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - precision
8
+ - recall
9
+ - f1
10
+ - accuracy
11
+ model-index:
12
+ - name: Ner-vibert4news-base-cased
13
+ results: []
14
+ ---
15
+
16
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
+ should probably proofread and complete it, then remove this comment. -->
18
+
19
+ # Ner-vibert4news-base-cased
20
+
21
+ This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on an unknown dataset.
22
+ It achieves the following results on the evaluation set:
23
+ - Loss: 2.2552
24
+ - Precision: 0.6164
25
+ - Recall: 0.6728
26
+ - F1: 0.6434
27
+ - Accuracy: 0.7944
28
+
29
+ ## Model description
30
+
31
+ More information needed
32
+
33
+ ## Intended uses & limitations
34
+
35
+ More information needed
36
+
37
+ ## Training and evaluation data
38
+
39
+ More information needed
40
+
41
+ ## Training procedure
42
+
43
+ ### Training hyperparameters
44
+
45
+ The following hyperparameters were used during training:
46
+ - learning_rate: 3e-05
47
+ - train_batch_size: 16
48
+ - eval_batch_size: 16
49
+ - seed: 42
50
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
+ - lr_scheduler_type: linear
52
+ - num_epochs: 120.0
53
+
54
+ ### Training results
55
+
56
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
57
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
58
+ | No log | 1.0 | 487 | 0.7563 | 0.4074 | 0.5740 | 0.4765 | 0.7624 |
59
+ | 1.1928 | 2.0 | 974 | 0.6945 | 0.4871 | 0.6226 | 0.5466 | 0.7788 |
60
+ | 0.6432 | 3.0 | 1461 | 0.7379 | 0.5035 | 0.6348 | 0.5616 | 0.7798 |
61
+ | 0.4783 | 4.0 | 1948 | 0.7334 | 0.5022 | 0.6396 | 0.5626 | 0.7849 |
62
+ | 0.3609 | 5.0 | 2435 | 0.8053 | 0.5322 | 0.6535 | 0.5866 | 0.7819 |
63
+ | 0.2735 | 6.0 | 2922 | 0.8289 | 0.5283 | 0.6401 | 0.5789 | 0.7850 |
64
+ | 0.2243 | 7.0 | 3409 | 0.9323 | 0.5463 | 0.6396 | 0.5892 | 0.7812 |
65
+ | 0.1753 | 8.0 | 3896 | 0.9913 | 0.5343 | 0.6547 | 0.5884 | 0.7798 |
66
+ | 0.147 | 9.0 | 4383 | 1.0703 | 0.5336 | 0.6535 | 0.5875 | 0.7738 |
67
+ | 0.1204 | 10.0 | 4870 | 1.0797 | 0.5470 | 0.6357 | 0.5880 | 0.7853 |
68
+ | 0.1046 | 11.0 | 5357 | 1.1283 | 0.5346 | 0.6527 | 0.5878 | 0.7804 |
69
+ | 0.0839 | 12.0 | 5844 | 1.1800 | 0.5472 | 0.6516 | 0.5949 | 0.7841 |
70
+ | 0.0702 | 13.0 | 6331 | 1.2153 | 0.5662 | 0.6600 | 0.6095 | 0.7898 |
71
+ | 0.0631 | 14.0 | 6818 | 1.2912 | 0.5451 | 0.6443 | 0.5906 | 0.7824 |
72
+ | 0.0508 | 15.0 | 7305 | 1.3288 | 0.5665 | 0.6505 | 0.6056 | 0.7855 |
73
+ | 0.0463 | 16.0 | 7792 | 1.4110 | 0.5716 | 0.6485 | 0.6076 | 0.7857 |
74
+ | 0.0387 | 17.0 | 8279 | 1.4022 | 0.5641 | 0.6563 | 0.6067 | 0.7860 |
75
+ | 0.0352 | 18.0 | 8766 | 1.4306 | 0.5540 | 0.6555 | 0.6005 | 0.7892 |
76
+ | 0.031 | 19.0 | 9253 | 1.4502 | 0.5659 | 0.6482 | 0.6043 | 0.7931 |
77
+ | 0.0286 | 20.0 | 9740 | 1.5111 | 0.5469 | 0.6572 | 0.5970 | 0.7867 |
78
+ | 0.0262 | 21.0 | 10227 | 1.6086 | 0.5745 | 0.6468 | 0.6085 | 0.7826 |
79
+ | 0.0212 | 22.0 | 10714 | 1.6134 | 0.5790 | 0.6622 | 0.6178 | 0.7845 |
80
+ | 0.0188 | 23.0 | 11201 | 1.6032 | 0.5662 | 0.6533 | 0.6066 | 0.7831 |
81
+ | 0.0179 | 24.0 | 11688 | 1.6524 | 0.5649 | 0.6561 | 0.6071 | 0.7821 |
82
+ | 0.0156 | 25.0 | 12175 | 1.6468 | 0.5643 | 0.6614 | 0.6090 | 0.7832 |
83
+ | 0.0165 | 26.0 | 12662 | 1.6683 | 0.5753 | 0.6524 | 0.6115 | 0.7857 |
84
+ | 0.0141 | 27.0 | 13149 | 1.6882 | 0.5740 | 0.6499 | 0.6096 | 0.7868 |
85
+ | 0.013 | 28.0 | 13636 | 1.7041 | 0.5761 | 0.6443 | 0.6083 | 0.7892 |
86
+ | 0.0125 | 29.0 | 14123 | 1.7665 | 0.5803 | 0.6549 | 0.6153 | 0.7868 |
87
+ | 0.0113 | 30.0 | 14610 | 1.7239 | 0.5814 | 0.6538 | 0.6155 | 0.7935 |
88
+ | 0.0115 | 31.0 | 15097 | 1.8083 | 0.5721 | 0.6535 | 0.6101 | 0.7848 |
89
+ | 0.0121 | 32.0 | 15584 | 1.7592 | 0.5660 | 0.6628 | 0.6106 | 0.7925 |
90
+ | 0.0105 | 33.0 | 16071 | 1.7803 | 0.5799 | 0.6572 | 0.6161 | 0.7882 |
91
+ | 0.0089 | 34.0 | 16558 | 1.8192 | 0.5786 | 0.6513 | 0.6128 | 0.7871 |
92
+ | 0.0107 | 35.0 | 17045 | 1.8329 | 0.5668 | 0.6597 | 0.6097 | 0.7860 |
93
+ | 0.011 | 36.0 | 17532 | 1.8010 | 0.5714 | 0.6547 | 0.6102 | 0.7834 |
94
+ | 0.0087 | 37.0 | 18019 | 1.8314 | 0.5906 | 0.6544 | 0.6208 | 0.7898 |
95
+ | 0.0075 | 38.0 | 18506 | 1.8428 | 0.5912 | 0.6577 | 0.6227 | 0.7913 |
96
+ | 0.0075 | 39.0 | 18993 | 1.8757 | 0.5816 | 0.6678 | 0.6217 | 0.7893 |
97
+ | 0.0079 | 40.0 | 19480 | 1.8514 | 0.5897 | 0.6586 | 0.6223 | 0.7897 |
98
+ | 0.0086 | 41.0 | 19967 | 1.8783 | 0.5878 | 0.6655 | 0.6242 | 0.7897 |
99
+ | 0.0075 | 42.0 | 20454 | 1.8177 | 0.5868 | 0.6644 | 0.6232 | 0.7951 |
100
+ | 0.0071 | 43.0 | 20941 | 1.8850 | 0.6038 | 0.6650 | 0.6329 | 0.7940 |
101
+ | 0.0068 | 44.0 | 21428 | 1.9210 | 0.5996 | 0.6661 | 0.6311 | 0.7918 |
102
+ | 0.006 | 45.0 | 21915 | 1.9289 | 0.5892 | 0.6630 | 0.6239 | 0.7913 |
103
+ | 0.0077 | 46.0 | 22402 | 1.9011 | 0.5876 | 0.6602 | 0.6218 | 0.7938 |
104
+ | 0.0047 | 47.0 | 22889 | 1.9092 | 0.5856 | 0.6630 | 0.6219 | 0.7934 |
105
+ | 0.0073 | 48.0 | 23376 | 1.9654 | 0.5886 | 0.6639 | 0.6240 | 0.7885 |
106
+ | 0.0058 | 49.0 | 23863 | 1.9483 | 0.5809 | 0.6639 | 0.6196 | 0.7884 |
107
+ | 0.0081 | 50.0 | 24350 | 1.9434 | 0.5995 | 0.6566 | 0.6268 | 0.7899 |
108
+ | 0.0063 | 51.0 | 24837 | 1.9490 | 0.5938 | 0.6639 | 0.6269 | 0.7903 |
109
+ | 0.0052 | 52.0 | 25324 | 1.9654 | 0.6072 | 0.6552 | 0.6303 | 0.7879 |
110
+ | 0.007 | 53.0 | 25811 | 1.9699 | 0.5967 | 0.6591 | 0.6263 | 0.7880 |
111
+ | 0.0047 | 54.0 | 26298 | 1.9713 | 0.5967 | 0.6614 | 0.6274 | 0.7909 |
112
+ | 0.0041 | 55.0 | 26785 | 1.9534 | 0.5909 | 0.6630 | 0.6249 | 0.7895 |
113
+ | 0.0042 | 56.0 | 27272 | 1.9982 | 0.6028 | 0.6630 | 0.6315 | 0.7941 |
114
+ | 0.0045 | 57.0 | 27759 | 1.9968 | 0.6058 | 0.6544 | 0.6292 | 0.7921 |
115
+ | 0.0045 | 58.0 | 28246 | 1.9851 | 0.6039 | 0.6580 | 0.6298 | 0.7905 |
116
+ | 0.0039 | 59.0 | 28733 | 2.0431 | 0.6067 | 0.6653 | 0.6346 | 0.7891 |
117
+ | 0.0048 | 60.0 | 29220 | 2.0036 | 0.5953 | 0.6494 | 0.6212 | 0.7878 |
118
+ | 0.004 | 61.0 | 29707 | 1.9971 | 0.6022 | 0.6669 | 0.6329 | 0.7914 |
119
+ | 0.0032 | 62.0 | 30194 | 2.0073 | 0.6025 | 0.6605 | 0.6302 | 0.7912 |
120
+ | 0.0033 | 63.0 | 30681 | 2.0134 | 0.5962 | 0.6608 | 0.6269 | 0.7918 |
121
+ | 0.0035 | 64.0 | 31168 | 2.0015 | 0.5981 | 0.6619 | 0.6284 | 0.7937 |
122
+ | 0.0032 | 65.0 | 31655 | 1.9974 | 0.5905 | 0.6650 | 0.6255 | 0.7940 |
123
+ | 0.0036 | 66.0 | 32142 | 2.0523 | 0.5935 | 0.6672 | 0.6282 | 0.7892 |
124
+ | 0.0027 | 67.0 | 32629 | 2.0683 | 0.6010 | 0.6695 | 0.6334 | 0.7901 |
125
+ | 0.0039 | 68.0 | 33116 | 2.1081 | 0.5919 | 0.6608 | 0.6245 | 0.7876 |
126
+ | 0.0027 | 69.0 | 33603 | 2.0555 | 0.5973 | 0.6655 | 0.6296 | 0.7923 |
127
+ | 0.003 | 70.0 | 34090 | 2.1007 | 0.5912 | 0.6614 | 0.6243 | 0.7880 |
128
+ | 0.0023 | 71.0 | 34577 | 2.0916 | 0.6085 | 0.6709 | 0.6382 | 0.7937 |
129
+ | 0.0016 | 72.0 | 35064 | 2.1564 | 0.5940 | 0.6600 | 0.6252 | 0.7908 |
130
+ | 0.0028 | 73.0 | 35551 | 2.1620 | 0.5947 | 0.6633 | 0.6272 | 0.7863 |
131
+ | 0.0028 | 74.0 | 36038 | 2.1390 | 0.5991 | 0.6683 | 0.6318 | 0.7892 |
132
+ | 0.0025 | 75.0 | 36525 | 2.1204 | 0.6026 | 0.6681 | 0.6337 | 0.7925 |
133
+ | 0.0026 | 76.0 | 37012 | 2.1700 | 0.6011 | 0.6614 | 0.6298 | 0.7884 |
134
+ | 0.0026 | 77.0 | 37499 | 2.1478 | 0.5994 | 0.6639 | 0.6300 | 0.7924 |
135
+ | 0.0022 | 78.0 | 37986 | 2.1547 | 0.5954 | 0.6650 | 0.6282 | 0.7879 |
136
+ | 0.0026 | 79.0 | 38473 | 2.1489 | 0.5851 | 0.6686 | 0.6241 | 0.7879 |
137
+ | 0.0017 | 80.0 | 38960 | 2.1789 | 0.5903 | 0.6706 | 0.6279 | 0.7870 |
138
+ | 0.0016 | 81.0 | 39447 | 2.1882 | 0.6026 | 0.6639 | 0.6318 | 0.7877 |
139
+ | 0.0014 | 82.0 | 39934 | 2.1825 | 0.6015 | 0.6711 | 0.6344 | 0.7880 |
140
+ | 0.0019 | 83.0 | 40421 | 2.1753 | 0.6013 | 0.6661 | 0.6321 | 0.7903 |
141
+ | 0.0014 | 84.0 | 40908 | 2.1887 | 0.6001 | 0.6661 | 0.6314 | 0.7911 |
142
+ | 0.0011 | 85.0 | 41395 | 2.1974 | 0.6055 | 0.6667 | 0.6346 | 0.7913 |
143
+ | 0.0019 | 86.0 | 41882 | 2.1918 | 0.6025 | 0.6678 | 0.6335 | 0.7913 |
144
+ | 0.0014 | 87.0 | 42369 | 2.1962 | 0.6133 | 0.6588 | 0.6353 | 0.7901 |
145
+ | 0.0019 | 88.0 | 42856 | 2.1974 | 0.5953 | 0.6628 | 0.6272 | 0.7902 |
146
+ | 0.0009 | 89.0 | 43343 | 2.1818 | 0.6002 | 0.6667 | 0.6317 | 0.7918 |
147
+ | 0.0016 | 90.0 | 43830 | 2.2059 | 0.6140 | 0.6706 | 0.6410 | 0.7945 |
148
+ | 0.0013 | 91.0 | 44317 | 2.2013 | 0.6086 | 0.6720 | 0.6387 | 0.7922 |
149
+ | 0.001 | 92.0 | 44804 | 2.1723 | 0.6084 | 0.6689 | 0.6372 | 0.7945 |
150
+ | 0.0012 | 93.0 | 45291 | 2.1967 | 0.6104 | 0.6706 | 0.6391 | 0.7966 |
151
+ | 0.0023 | 94.0 | 45778 | 2.2024 | 0.6157 | 0.6695 | 0.6414 | 0.7939 |
152
+ | 0.0012 | 95.0 | 46265 | 2.2250 | 0.6097 | 0.6748 | 0.6406 | 0.7929 |
153
+ | 0.0015 | 96.0 | 46752 | 2.1938 | 0.6204 | 0.6734 | 0.6458 | 0.7914 |
154
+ | 0.0012 | 97.0 | 47239 | 2.1854 | 0.6012 | 0.6801 | 0.6382 | 0.7897 |
155
+ | 0.0008 | 98.0 | 47726 | 2.2005 | 0.6199 | 0.6734 | 0.6455 | 0.7930 |
156
+ | 0.0008 | 99.0 | 48213 | 2.1999 | 0.6088 | 0.6731 | 0.6394 | 0.7896 |
157
+ | 0.0011 | 100.0 | 48700 | 2.2228 | 0.6086 | 0.6695 | 0.6376 | 0.7931 |
158
+ | 0.0006 | 101.0 | 49187 | 2.2300 | 0.6110 | 0.6784 | 0.6429 | 0.7925 |
159
+ | 0.001 | 102.0 | 49674 | 2.2194 | 0.6059 | 0.6748 | 0.6385 | 0.7917 |
160
+ | 0.0007 | 103.0 | 50161 | 2.2048 | 0.6131 | 0.6742 | 0.6422 | 0.7947 |
161
+ | 0.0003 | 104.0 | 50648 | 2.2270 | 0.6232 | 0.6731 | 0.6472 | 0.7938 |
162
+ | 0.0008 | 105.0 | 51135 | 2.2284 | 0.6184 | 0.6742 | 0.6451 | 0.7952 |
163
+ | 0.0005 | 106.0 | 51622 | 2.2278 | 0.6080 | 0.6742 | 0.6394 | 0.7921 |
164
+ | 0.0004 | 107.0 | 52109 | 2.2571 | 0.6157 | 0.6759 | 0.6444 | 0.7926 |
165
+ | 0.0006 | 108.0 | 52596 | 2.2562 | 0.6069 | 0.6723 | 0.6379 | 0.7925 |
166
+ | 0.0005 | 109.0 | 53083 | 2.2255 | 0.6172 | 0.6717 | 0.6433 | 0.7950 |
167
+ | 0.0006 | 110.0 | 53570 | 2.2429 | 0.6104 | 0.6759 | 0.6415 | 0.7931 |
168
+ | 0.0004 | 111.0 | 54057 | 2.2416 | 0.6123 | 0.6742 | 0.6418 | 0.7927 |
169
+ | 0.0004 | 112.0 | 54544 | 2.2629 | 0.6123 | 0.6689 | 0.6394 | 0.7939 |
170
+ | 0.0004 | 113.0 | 55031 | 2.2645 | 0.6136 | 0.6748 | 0.6427 | 0.7932 |
171
+ | 0.0003 | 114.0 | 55518 | 2.2761 | 0.6208 | 0.6736 | 0.6461 | 0.7944 |
172
+ | 0.0004 | 115.0 | 56005 | 2.2684 | 0.6159 | 0.6745 | 0.6438 | 0.7937 |
173
+ | 0.0004 | 116.0 | 56492 | 2.2741 | 0.6160 | 0.6736 | 0.6436 | 0.7926 |
174
+ | 0.0003 | 117.0 | 56979 | 2.2576 | 0.6160 | 0.6736 | 0.6436 | 0.7939 |
175
+ | 0.001 | 118.0 | 57466 | 2.2543 | 0.6157 | 0.6731 | 0.6431 | 0.7943 |
176
+ | 0.0003 | 119.0 | 57953 | 2.2541 | 0.6163 | 0.6739 | 0.6438 | 0.7947 |
177
+ | 0.0005 | 120.0 | 58440 | 2.2552 | 0.6164 | 0.6728 | 0.6434 | 0.7944 |
178
+
179
+
180
+ ### Framework versions
181
+
182
+ - Transformers 4.32.0.dev0
183
+ - Pytorch 2.1.0+cu121
184
+ - Datasets 3.1.0
185
+ - Tokenizers 0.13.3