ner_base
This model is a fine-tuned version of vinai/phobert-base on the hts98/UIT dataset. It achieves the following results on the evaluation set:
- Loss: 1.6160
- Precision: 0.6525
- Recall: 0.7066
- F1: 0.6785
- Accuracy: 0.8276
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 120.0
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 244 | 0.8214 | 0.4246 | 0.5544 | 0.4809 | 0.7636 |
No log | 2.0 | 488 | 0.6734 | 0.5305 | 0.6159 | 0.5700 | 0.8023 |
0.9764 | 3.0 | 732 | 0.6425 | 0.5591 | 0.6519 | 0.6020 | 0.8072 |
0.9764 | 4.0 | 976 | 0.6203 | 0.5664 | 0.6731 | 0.6151 | 0.8215 |
0.4504 | 5.0 | 1220 | 0.6483 | 0.5879 | 0.6795 | 0.6304 | 0.8171 |
0.4504 | 6.0 | 1464 | 0.6828 | 0.5478 | 0.6826 | 0.6078 | 0.8138 |
0.2877 | 7.0 | 1708 | 0.7097 | 0.5868 | 0.6803 | 0.6301 | 0.8115 |
0.2877 | 8.0 | 1952 | 0.7538 | 0.5864 | 0.6884 | 0.6334 | 0.8133 |
0.1968 | 9.0 | 2196 | 0.7853 | 0.5949 | 0.6837 | 0.6362 | 0.8129 |
0.1968 | 10.0 | 2440 | 0.8311 | 0.5984 | 0.6815 | 0.6373 | 0.8094 |
0.1443 | 11.0 | 2684 | 0.7910 | 0.6101 | 0.6898 | 0.6475 | 0.8190 |
0.1443 | 12.0 | 2928 | 0.8414 | 0.5927 | 0.6803 | 0.6335 | 0.8147 |
0.1118 | 13.0 | 3172 | 0.8946 | 0.6023 | 0.6857 | 0.6413 | 0.8069 |
0.1118 | 14.0 | 3416 | 0.9195 | 0.6055 | 0.6843 | 0.6425 | 0.8130 |
0.0838 | 15.0 | 3660 | 0.9149 | 0.6020 | 0.6882 | 0.6422 | 0.8192 |
0.0838 | 16.0 | 3904 | 0.9357 | 0.6072 | 0.6893 | 0.6457 | 0.8201 |
0.0661 | 17.0 | 4148 | 0.9784 | 0.6033 | 0.6887 | 0.6432 | 0.8173 |
0.0661 | 18.0 | 4392 | 0.9842 | 0.6121 | 0.6868 | 0.6473 | 0.8184 |
0.0514 | 19.0 | 4636 | 1.0097 | 0.6105 | 0.6896 | 0.6476 | 0.8164 |
0.0514 | 20.0 | 4880 | 1.0300 | 0.6086 | 0.6946 | 0.6488 | 0.8167 |
0.0416 | 21.0 | 5124 | 1.0250 | 0.6190 | 0.6904 | 0.6528 | 0.8205 |
0.0416 | 22.0 | 5368 | 1.0879 | 0.6171 | 0.6937 | 0.6532 | 0.8167 |
0.0324 | 23.0 | 5612 | 1.1349 | 0.6093 | 0.6817 | 0.6435 | 0.8127 |
0.0324 | 24.0 | 5856 | 1.0994 | 0.6191 | 0.6935 | 0.6542 | 0.8181 |
0.0277 | 25.0 | 6100 | 1.1401 | 0.6180 | 0.6951 | 0.6543 | 0.8153 |
0.0277 | 26.0 | 6344 | 1.1868 | 0.5984 | 0.6904 | 0.6411 | 0.8084 |
0.0223 | 27.0 | 6588 | 1.2052 | 0.6282 | 0.6859 | 0.6558 | 0.8139 |
0.0223 | 28.0 | 6832 | 1.1964 | 0.6168 | 0.6935 | 0.6529 | 0.8153 |
0.019 | 29.0 | 7076 | 1.1898 | 0.6214 | 0.6887 | 0.6533 | 0.8202 |
0.019 | 30.0 | 7320 | 1.2819 | 0.6136 | 0.6946 | 0.6516 | 0.8135 |
0.0159 | 31.0 | 7564 | 1.2687 | 0.6093 | 0.6912 | 0.6477 | 0.8128 |
0.0159 | 32.0 | 7808 | 1.2997 | 0.6128 | 0.6971 | 0.6522 | 0.8159 |
0.0141 | 33.0 | 8052 | 1.2800 | 0.6172 | 0.6843 | 0.6490 | 0.8157 |
0.0141 | 34.0 | 8296 | 1.3110 | 0.6220 | 0.6901 | 0.6543 | 0.8141 |
0.0107 | 35.0 | 8540 | 1.3343 | 0.6081 | 0.6910 | 0.6469 | 0.8160 |
0.0107 | 36.0 | 8784 | 1.3406 | 0.6111 | 0.6926 | 0.6493 | 0.8130 |
0.0106 | 37.0 | 9028 | 1.3921 | 0.6080 | 0.6876 | 0.6454 | 0.8127 |
0.0106 | 38.0 | 9272 | 1.4061 | 0.6086 | 0.6868 | 0.6453 | 0.8100 |
0.0088 | 39.0 | 9516 | 1.3828 | 0.6293 | 0.6921 | 0.6592 | 0.8166 |
0.0088 | 40.0 | 9760 | 1.4263 | 0.6242 | 0.6940 | 0.6572 | 0.8130 |
0.0086 | 41.0 | 10004 | 1.3521 | 0.6202 | 0.6993 | 0.6574 | 0.8185 |
0.0086 | 42.0 | 10248 | 1.3722 | 0.6451 | 0.6999 | 0.6714 | 0.8196 |
0.0086 | 43.0 | 10492 | 1.3784 | 0.6373 | 0.6946 | 0.6647 | 0.8226 |
0.0075 | 44.0 | 10736 | 1.4340 | 0.6334 | 0.6940 | 0.6623 | 0.8140 |
0.0075 | 45.0 | 10980 | 1.3902 | 0.6321 | 0.7002 | 0.6644 | 0.8161 |
0.0066 | 46.0 | 11224 | 1.4019 | 0.6230 | 0.6985 | 0.6586 | 0.8162 |
0.0066 | 47.0 | 11468 | 1.4320 | 0.6183 | 0.6960 | 0.6548 | 0.8161 |
0.0067 | 48.0 | 11712 | 1.4461 | 0.6326 | 0.6999 | 0.6645 | 0.8200 |
0.0067 | 49.0 | 11956 | 1.4327 | 0.6249 | 0.6982 | 0.6595 | 0.8202 |
0.0054 | 50.0 | 12200 | 1.4616 | 0.6348 | 0.6943 | 0.6632 | 0.8176 |
0.0054 | 51.0 | 12444 | 1.4537 | 0.6135 | 0.7010 | 0.6543 | 0.8177 |
0.0052 | 52.0 | 12688 | 1.5622 | 0.6265 | 0.6884 | 0.6560 | 0.8096 |
0.0052 | 53.0 | 12932 | 1.4217 | 0.6348 | 0.7027 | 0.6670 | 0.8236 |
0.0051 | 54.0 | 13176 | 1.4625 | 0.6266 | 0.6965 | 0.6597 | 0.8217 |
0.0051 | 55.0 | 13420 | 1.4359 | 0.6393 | 0.6918 | 0.6645 | 0.8257 |
0.0049 | 56.0 | 13664 | 1.4617 | 0.6447 | 0.6977 | 0.6702 | 0.8231 |
0.0049 | 57.0 | 13908 | 1.5171 | 0.6337 | 0.6951 | 0.6630 | 0.8181 |
0.0037 | 58.0 | 14152 | 1.4999 | 0.6339 | 0.7032 | 0.6668 | 0.8206 |
0.0037 | 59.0 | 14396 | 1.4841 | 0.6269 | 0.7007 | 0.6617 | 0.8208 |
0.004 | 60.0 | 14640 | 1.4361 | 0.6381 | 0.7044 | 0.6696 | 0.8244 |
0.004 | 61.0 | 14884 | 1.4800 | 0.6425 | 0.7035 | 0.6716 | 0.8235 |
0.004 | 62.0 | 15128 | 1.4700 | 0.6330 | 0.6991 | 0.6644 | 0.8241 |
0.004 | 63.0 | 15372 | 1.5107 | 0.6309 | 0.7016 | 0.6644 | 0.8212 |
0.0037 | 64.0 | 15616 | 1.5132 | 0.6389 | 0.7024 | 0.6691 | 0.8227 |
0.0037 | 65.0 | 15860 | 1.5229 | 0.6287 | 0.7016 | 0.6631 | 0.8239 |
0.0033 | 66.0 | 16104 | 1.5574 | 0.6395 | 0.7027 | 0.6696 | 0.8242 |
0.0033 | 67.0 | 16348 | 1.5216 | 0.6270 | 0.7052 | 0.6638 | 0.8196 |
0.0033 | 68.0 | 16592 | 1.4877 | 0.6347 | 0.6951 | 0.6636 | 0.8242 |
0.0033 | 69.0 | 16836 | 1.5373 | 0.6281 | 0.7021 | 0.6631 | 0.8195 |
0.0026 | 70.0 | 17080 | 1.5522 | 0.6335 | 0.7002 | 0.6652 | 0.8201 |
0.0026 | 71.0 | 17324 | 1.5180 | 0.6380 | 0.7035 | 0.6691 | 0.8227 |
0.0024 | 72.0 | 17568 | 1.5517 | 0.6504 | 0.6974 | 0.6730 | 0.8218 |
0.0024 | 73.0 | 17812 | 1.5392 | 0.6332 | 0.7021 | 0.6659 | 0.8206 |
0.0026 | 74.0 | 18056 | 1.5396 | 0.6415 | 0.7010 | 0.6700 | 0.8246 |
0.0026 | 75.0 | 18300 | 1.5638 | 0.6500 | 0.6999 | 0.6740 | 0.8233 |
0.0019 | 76.0 | 18544 | 1.5790 | 0.6438 | 0.6912 | 0.6667 | 0.8202 |
0.0019 | 77.0 | 18788 | 1.5546 | 0.6500 | 0.7052 | 0.6765 | 0.8216 |
0.0029 | 78.0 | 19032 | 1.5374 | 0.6369 | 0.7032 | 0.6684 | 0.8236 |
0.0029 | 79.0 | 19276 | 1.5923 | 0.6351 | 0.6982 | 0.6652 | 0.8180 |
0.0015 | 80.0 | 19520 | 1.5728 | 0.6354 | 0.7027 | 0.6674 | 0.8246 |
0.0015 | 81.0 | 19764 | 1.5646 | 0.6417 | 0.6979 | 0.6686 | 0.8229 |
0.0019 | 82.0 | 20008 | 1.5845 | 0.6247 | 0.7030 | 0.6615 | 0.8211 |
0.0019 | 83.0 | 20252 | 1.5894 | 0.6424 | 0.6935 | 0.6669 | 0.8193 |
0.0019 | 84.0 | 20496 | 1.6702 | 0.6428 | 0.6907 | 0.6659 | 0.8169 |
0.0012 | 85.0 | 20740 | 1.6313 | 0.6342 | 0.7027 | 0.6667 | 0.8189 |
0.0012 | 86.0 | 20984 | 1.5829 | 0.6357 | 0.7038 | 0.6680 | 0.8232 |
0.0015 | 87.0 | 21228 | 1.6056 | 0.6396 | 0.7085 | 0.6723 | 0.8210 |
0.0015 | 88.0 | 21472 | 1.5823 | 0.6471 | 0.7105 | 0.6773 | 0.8225 |
0.0015 | 89.0 | 21716 | 1.5736 | 0.6367 | 0.7016 | 0.6676 | 0.8249 |
0.0015 | 90.0 | 21960 | 1.5921 | 0.6457 | 0.7016 | 0.6725 | 0.8236 |
0.0012 | 91.0 | 22204 | 1.6114 | 0.6371 | 0.7030 | 0.6684 | 0.8231 |
0.0012 | 92.0 | 22448 | 1.5752 | 0.6408 | 0.7038 | 0.6708 | 0.8245 |
0.0014 | 93.0 | 22692 | 1.6123 | 0.6360 | 0.7018 | 0.6673 | 0.8217 |
0.0014 | 94.0 | 22936 | 1.6183 | 0.6374 | 0.7021 | 0.6682 | 0.8221 |
0.0009 | 95.0 | 23180 | 1.6078 | 0.6474 | 0.6988 | 0.6721 | 0.8275 |
0.0009 | 96.0 | 23424 | 1.6201 | 0.6401 | 0.6991 | 0.6683 | 0.8246 |
0.0008 | 97.0 | 23668 | 1.6216 | 0.6388 | 0.7016 | 0.6687 | 0.8238 |
0.0008 | 98.0 | 23912 | 1.6113 | 0.6410 | 0.7024 | 0.6703 | 0.8244 |
0.0011 | 99.0 | 24156 | 1.5995 | 0.6497 | 0.7027 | 0.6752 | 0.8245 |
0.0011 | 100.0 | 24400 | 1.5953 | 0.6423 | 0.7027 | 0.6711 | 0.8259 |
0.0009 | 101.0 | 24644 | 1.6178 | 0.6447 | 0.7027 | 0.6725 | 0.8248 |
0.0009 | 102.0 | 24888 | 1.6171 | 0.6408 | 0.7066 | 0.6721 | 0.8257 |
0.0006 | 103.0 | 25132 | 1.6054 | 0.6508 | 0.7077 | 0.6781 | 0.8271 |
0.0006 | 104.0 | 25376 | 1.6218 | 0.6412 | 0.7018 | 0.6701 | 0.8251 |
0.0008 | 105.0 | 25620 | 1.6308 | 0.6475 | 0.7024 | 0.6738 | 0.8245 |
0.0008 | 106.0 | 25864 | 1.6342 | 0.6471 | 0.7066 | 0.6756 | 0.8267 |
0.0004 | 107.0 | 26108 | 1.6346 | 0.6447 | 0.7058 | 0.6739 | 0.8254 |
0.0004 | 108.0 | 26352 | 1.6328 | 0.6437 | 0.7066 | 0.6737 | 0.8257 |
0.0008 | 109.0 | 26596 | 1.6220 | 0.6476 | 0.7038 | 0.6745 | 0.8257 |
0.0008 | 110.0 | 26840 | 1.6160 | 0.6525 | 0.7066 | 0.6785 | 0.8276 |
0.0006 | 111.0 | 27084 | 1.6100 | 0.6455 | 0.7055 | 0.6741 | 0.8270 |
0.0006 | 112.0 | 27328 | 1.6270 | 0.6394 | 0.7055 | 0.6708 | 0.8247 |
0.0005 | 113.0 | 27572 | 1.6234 | 0.6505 | 0.7024 | 0.6754 | 0.8273 |
0.0005 | 114.0 | 27816 | 1.6328 | 0.6417 | 0.7035 | 0.6712 | 0.8252 |
0.0004 | 115.0 | 28060 | 1.6352 | 0.6428 | 0.7018 | 0.6710 | 0.8251 |
0.0004 | 116.0 | 28304 | 1.6269 | 0.6458 | 0.7055 | 0.6743 | 0.8265 |
0.0005 | 117.0 | 28548 | 1.6377 | 0.6442 | 0.7041 | 0.6728 | 0.8253 |
0.0005 | 118.0 | 28792 | 1.6353 | 0.6450 | 0.7049 | 0.6736 | 0.8257 |
0.0004 | 119.0 | 29036 | 1.6395 | 0.6476 | 0.7044 | 0.6748 | 0.8257 |
0.0004 | 120.0 | 29280 | 1.6385 | 0.6467 | 0.7038 | 0.6741 | 0.8256 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 3.1.0
- Tokenizers 0.13.3
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Model tree for hts98/ner_base
Base model
vinai/phobert-baseDataset used to train hts98/ner_base
Evaluation results
- Precision on hts98/UITself-reported0.652
- Recall on hts98/UITself-reported0.707
- F1 on hts98/UITself-reported0.678
- Accuracy on hts98/UITself-reported0.828