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---
license: mit
base_model: vinai/phobert-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner
This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7865
- Precision: 0.6846
- Recall: 0.7180
- F1: 0.7009
- Accuracy: 0.8312
## 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: 16
- eval_batch_size: 16
- 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 | 487 | 0.6517 | 0.5180 | 0.6267 | 0.5672 | 0.7979 |
| 1.0091 | 2.0 | 974 | 0.6198 | 0.5438 | 0.6583 | 0.5956 | 0.8116 |
| 0.5042 | 3.0 | 1461 | 0.6614 | 0.5677 | 0.6745 | 0.6165 | 0.8094 |
| 0.3543 | 4.0 | 1948 | 0.6794 | 0.5792 | 0.6826 | 0.6267 | 0.8231 |
| 0.2476 | 5.0 | 2435 | 0.7132 | 0.6090 | 0.7041 | 0.6531 | 0.8249 |
| 0.1849 | 6.0 | 2922 | 0.7761 | 0.6023 | 0.6926 | 0.6443 | 0.8219 |
| 0.1465 | 7.0 | 3409 | 0.8294 | 0.5965 | 0.7007 | 0.6444 | 0.8173 |
| 0.1176 | 8.0 | 3896 | 0.8653 | 0.6150 | 0.6935 | 0.6519 | 0.8230 |
| 0.1023 | 9.0 | 4383 | 0.8614 | 0.6123 | 0.6926 | 0.6500 | 0.8226 |
| 0.0823 | 10.0 | 4870 | 0.9825 | 0.6073 | 0.6848 | 0.6437 | 0.8216 |
| 0.069 | 11.0 | 5357 | 0.9783 | 0.6246 | 0.6957 | 0.6582 | 0.8248 |
| 0.0578 | 12.0 | 5844 | 1.0037 | 0.6115 | 0.7030 | 0.6540 | 0.8224 |
| 0.0522 | 13.0 | 6331 | 1.0799 | 0.6177 | 0.6829 | 0.6486 | 0.8161 |
| 0.0461 | 14.0 | 6818 | 1.0693 | 0.6088 | 0.7016 | 0.6519 | 0.8203 |
| 0.0402 | 15.0 | 7305 | 1.0560 | 0.6158 | 0.6991 | 0.6548 | 0.8230 |
| 0.0369 | 16.0 | 7792 | 1.1046 | 0.6307 | 0.6910 | 0.6595 | 0.8197 |
| 0.0391 | 17.0 | 8279 | 1.1480 | 0.6228 | 0.6873 | 0.6535 | 0.8233 |
| 0.0537 | 18.0 | 8766 | 1.2141 | 0.6234 | 0.6873 | 0.6538 | 0.8204 |
| 0.0497 | 19.0 | 9253 | 1.2230 | 0.6241 | 0.6957 | 0.6580 | 0.8189 |
| 0.0512 | 20.0 | 9740 | 1.2078 | 0.6357 | 0.7016 | 0.6670 | 0.8268 |
| 0.0508 | 21.0 | 10227 | 1.1941 | 0.6153 | 0.6921 | 0.6514 | 0.8178 |
| 0.044 | 22.0 | 10714 | 1.3114 | 0.6377 | 0.6924 | 0.6639 | 0.8161 |
| 0.041 | 23.0 | 11201 | 1.2640 | 0.6191 | 0.6884 | 0.6519 | 0.8165 |
| 0.0216 | 24.0 | 11688 | 1.3127 | 0.6349 | 0.6929 | 0.6627 | 0.8240 |
| 0.0187 | 25.0 | 12175 | 1.3329 | 0.6452 | 0.7004 | 0.6717 | 0.8229 |
| 0.0158 | 26.0 | 12662 | 1.2958 | 0.6243 | 0.7004 | 0.6602 | 0.8177 |
| 0.0151 | 27.0 | 13149 | 1.3276 | 0.6204 | 0.6985 | 0.6571 | 0.8181 |
| 0.016 | 28.0 | 13636 | 1.2671 | 0.6481 | 0.6999 | 0.6730 | 0.8251 |
| 0.0157 | 29.0 | 14123 | 1.3374 | 0.6191 | 0.6946 | 0.6547 | 0.8204 |
| 0.0146 | 30.0 | 14610 | 1.3941 | 0.6558 | 0.6932 | 0.6740 | 0.8192 |
| 0.0134 | 31.0 | 15097 | 1.4215 | 0.6344 | 0.6854 | 0.6589 | 0.8164 |
| 0.0146 | 32.0 | 15584 | 1.4602 | 0.6510 | 0.6937 | 0.6717 | 0.8198 |
| 0.0105 | 33.0 | 16071 | 1.4085 | 0.6459 | 0.7038 | 0.6736 | 0.8240 |
| 0.0135 | 34.0 | 16558 | 1.3593 | 0.6337 | 0.7002 | 0.6653 | 0.8166 |
| 0.0155 | 35.0 | 17045 | 1.3412 | 0.6519 | 0.6943 | 0.6724 | 0.8222 |
| 0.0141 | 36.0 | 17532 | 1.3676 | 0.6385 | 0.7021 | 0.6688 | 0.8219 |
| 0.0145 | 37.0 | 18019 | 1.3878 | 0.6573 | 0.6993 | 0.6777 | 0.8251 |
| 0.0106 | 38.0 | 18506 | 1.4314 | 0.6298 | 0.7016 | 0.6638 | 0.8239 |
| 0.0106 | 39.0 | 18993 | 1.3729 | 0.6666 | 0.7071 | 0.6863 | 0.8282 |
| 0.0112 | 40.0 | 19480 | 1.3455 | 0.6506 | 0.7032 | 0.6759 | 0.8283 |
| 0.0109 | 41.0 | 19967 | 1.3884 | 0.6429 | 0.7060 | 0.6730 | 0.8278 |
| 0.0084 | 42.0 | 20454 | 1.4240 | 0.6428 | 0.7080 | 0.6738 | 0.8255 |
| 0.0082 | 43.0 | 20941 | 1.4280 | 0.6091 | 0.6829 | 0.6439 | 0.8176 |
| 0.0122 | 44.0 | 21428 | 1.4723 | 0.6533 | 0.7032 | 0.6773 | 0.8239 |
| 0.0082 | 45.0 | 21915 | 1.5151 | 0.6189 | 0.6960 | 0.6552 | 0.8180 |
| 0.0068 | 46.0 | 22402 | 1.4441 | 0.6331 | 0.7046 | 0.6669 | 0.8211 |
| 0.0074 | 47.0 | 22889 | 1.4753 | 0.6497 | 0.6974 | 0.6727 | 0.8203 |
| 0.0076 | 48.0 | 23376 | 1.5148 | 0.6515 | 0.6957 | 0.6729 | 0.8215 |
| 0.0098 | 49.0 | 23863 | 1.4481 | 0.6319 | 0.6974 | 0.6630 | 0.8233 |
| 0.0104 | 50.0 | 24350 | 1.4814 | 0.6585 | 0.7074 | 0.6821 | 0.8235 |
| 0.0119 | 51.0 | 24837 | 1.4050 | 0.6555 | 0.7133 | 0.6832 | 0.8264 |
| 0.0078 | 52.0 | 25324 | 1.4854 | 0.6615 | 0.7049 | 0.6825 | 0.8234 |
| 0.007 | 53.0 | 25811 | 1.4941 | 0.6476 | 0.7013 | 0.6734 | 0.8204 |
| 0.0079 | 54.0 | 26298 | 1.4138 | 0.6529 | 0.7088 | 0.6797 | 0.8228 |
| 0.0092 | 55.0 | 26785 | 1.4301 | 0.6762 | 0.7018 | 0.6888 | 0.8218 |
| 0.0097 | 56.0 | 27272 | 1.5276 | 0.6544 | 0.6974 | 0.6752 | 0.8182 |
| 0.0076 | 57.0 | 27759 | 1.4302 | 0.6517 | 0.7032 | 0.6765 | 0.8258 |
| 0.0056 | 58.0 | 28246 | 1.4996 | 0.6675 | 0.7046 | 0.6856 | 0.8265 |
| 0.0047 | 59.0 | 28733 | 1.4309 | 0.6625 | 0.7032 | 0.6823 | 0.8241 |
| 0.0126 | 60.0 | 29220 | 1.4903 | 0.6457 | 0.7002 | 0.6718 | 0.8172 |
| 0.0054 | 61.0 | 29707 | 1.4318 | 0.6398 | 0.7035 | 0.6701 | 0.8218 |
| 0.0076 | 62.0 | 30194 | 1.5745 | 0.6660 | 0.6988 | 0.6820 | 0.8196 |
| 0.0043 | 63.0 | 30681 | 1.5102 | 0.6607 | 0.7058 | 0.6825 | 0.8268 |
| 0.0046 | 64.0 | 31168 | 1.5500 | 0.6655 | 0.6949 | 0.6799 | 0.8252 |
| 0.0042 | 65.0 | 31655 | 1.5357 | 0.6555 | 0.7138 | 0.6834 | 0.8274 |
| 0.0039 | 66.0 | 32142 | 1.5469 | 0.6650 | 0.7105 | 0.6870 | 0.8220 |
| 0.004 | 67.0 | 32629 | 1.4814 | 0.6542 | 0.7147 | 0.6831 | 0.8289 |
| 0.0031 | 68.0 | 33116 | 1.5210 | 0.6545 | 0.7097 | 0.6810 | 0.8250 |
| 0.0047 | 69.0 | 33603 | 1.5326 | 0.6549 | 0.7083 | 0.6805 | 0.8272 |
| 0.0029 | 70.0 | 34090 | 1.6057 | 0.6643 | 0.7027 | 0.6829 | 0.8226 |
| 0.0027 | 71.0 | 34577 | 1.5920 | 0.6594 | 0.7141 | 0.6857 | 0.8255 |
| 0.0016 | 72.0 | 35064 | 1.6220 | 0.6668 | 0.7024 | 0.6842 | 0.8255 |
| 0.0025 | 73.0 | 35551 | 1.6261 | 0.6803 | 0.7027 | 0.6913 | 0.8239 |
| 0.0037 | 74.0 | 36038 | 1.6440 | 0.6769 | 0.7049 | 0.6906 | 0.8207 |
| 0.003 | 75.0 | 36525 | 1.6027 | 0.6701 | 0.7071 | 0.6881 | 0.8263 |
| 0.0031 | 76.0 | 37012 | 1.6013 | 0.6670 | 0.7141 | 0.6898 | 0.8262 |
| 0.0031 | 77.0 | 37499 | 1.6714 | 0.6434 | 0.7147 | 0.6772 | 0.8185 |
| 0.002 | 78.0 | 37986 | 1.6293 | 0.6666 | 0.7071 | 0.6863 | 0.8267 |
| 0.0024 | 79.0 | 38473 | 1.6796 | 0.6578 | 0.7094 | 0.6826 | 0.8222 |
| 0.003 | 80.0 | 38960 | 1.6463 | 0.6701 | 0.7094 | 0.6892 | 0.8283 |
| 0.0015 | 81.0 | 39447 | 1.6634 | 0.6765 | 0.7074 | 0.6916 | 0.8266 |
| 0.003 | 82.0 | 39934 | 1.6947 | 0.6636 | 0.7055 | 0.6839 | 0.8255 |
| 0.0036 | 83.0 | 40421 | 1.6515 | 0.6554 | 0.7046 | 0.6791 | 0.8227 |
| 0.0018 | 84.0 | 40908 | 1.6855 | 0.6641 | 0.7102 | 0.6864 | 0.8266 |
| 0.0012 | 85.0 | 41395 | 1.6966 | 0.6545 | 0.7108 | 0.6815 | 0.8241 |
| 0.0019 | 86.0 | 41882 | 1.6564 | 0.6623 | 0.7058 | 0.6833 | 0.8255 |
| 0.0015 | 87.0 | 42369 | 1.6363 | 0.6501 | 0.7080 | 0.6778 | 0.8239 |
| 0.0022 | 88.0 | 42856 | 1.6879 | 0.6813 | 0.7055 | 0.6932 | 0.8260 |
| 0.0011 | 89.0 | 43343 | 1.6870 | 0.6660 | 0.7113 | 0.6879 | 0.8294 |
| 0.0017 | 90.0 | 43830 | 1.7018 | 0.6707 | 0.7041 | 0.6870 | 0.8276 |
| 0.0016 | 91.0 | 44317 | 1.6699 | 0.6701 | 0.7133 | 0.6910 | 0.8281 |
| 0.0015 | 92.0 | 44804 | 1.6737 | 0.6773 | 0.7125 | 0.6944 | 0.8320 |
| 0.0017 | 93.0 | 45291 | 1.7271 | 0.6769 | 0.7189 | 0.6973 | 0.8280 |
| 0.0005 | 94.0 | 45778 | 1.7245 | 0.6654 | 0.7127 | 0.6882 | 0.8261 |
| 0.0013 | 95.0 | 46265 | 1.8143 | 0.6772 | 0.7052 | 0.6909 | 0.8235 |
| 0.0012 | 96.0 | 46752 | 1.7299 | 0.6736 | 0.7091 | 0.6909 | 0.8262 |
| 0.002 | 97.0 | 47239 | 1.7251 | 0.6758 | 0.7125 | 0.6937 | 0.8273 |
| 0.0009 | 98.0 | 47726 | 1.7183 | 0.6565 | 0.7183 | 0.6860 | 0.8262 |
| 0.0009 | 99.0 | 48213 | 1.7801 | 0.6759 | 0.7116 | 0.6933 | 0.8279 |
| 0.0008 | 100.0 | 48700 | 1.7749 | 0.6817 | 0.7108 | 0.6959 | 0.8263 |
| 0.0006 | 101.0 | 49187 | 1.7413 | 0.6732 | 0.7113 | 0.6917 | 0.8272 |
| 0.0005 | 102.0 | 49674 | 1.7939 | 0.6648 | 0.7144 | 0.6887 | 0.8270 |
| 0.0008 | 103.0 | 50161 | 1.7955 | 0.6602 | 0.7111 | 0.6847 | 0.8237 |
| 0.0007 | 104.0 | 50648 | 1.7844 | 0.6686 | 0.7130 | 0.6901 | 0.8266 |
| 0.0005 | 105.0 | 51135 | 1.7983 | 0.6808 | 0.7127 | 0.6964 | 0.8279 |
| 0.0004 | 106.0 | 51622 | 1.7945 | 0.6798 | 0.7130 | 0.6960 | 0.8256 |
| 0.0005 | 107.0 | 52109 | 1.8209 | 0.6879 | 0.7164 | 0.7019 | 0.8297 |
| 0.0004 | 108.0 | 52596 | 1.8150 | 0.6839 | 0.7085 | 0.6960 | 0.8281 |
| 0.0006 | 109.0 | 53083 | 1.7784 | 0.6778 | 0.7166 | 0.6967 | 0.8287 |
| 0.0009 | 110.0 | 53570 | 1.7941 | 0.6761 | 0.7180 | 0.6965 | 0.8293 |
| 0.0006 | 111.0 | 54057 | 1.8079 | 0.6762 | 0.7200 | 0.6974 | 0.8280 |
| 0.0006 | 112.0 | 54544 | 1.7968 | 0.6752 | 0.7166 | 0.6953 | 0.8277 |
| 0.0003 | 113.0 | 55031 | 1.7972 | 0.6753 | 0.7166 | 0.6954 | 0.8285 |
| 0.0003 | 114.0 | 55518 | 1.7985 | 0.6764 | 0.7172 | 0.6962 | 0.8298 |
| 0.0006 | 115.0 | 56005 | 1.8048 | 0.6759 | 0.7172 | 0.6959 | 0.8287 |
| 0.0006 | 116.0 | 56492 | 1.7985 | 0.6758 | 0.7152 | 0.6950 | 0.8298 |
| 0.0004 | 117.0 | 56979 | 1.7883 | 0.6835 | 0.7164 | 0.6996 | 0.8314 |
| 0.0009 | 118.0 | 57466 | 1.7852 | 0.6830 | 0.7180 | 0.7001 | 0.8311 |
| 0.0002 | 119.0 | 57953 | 1.7869 | 0.6853 | 0.7180 | 0.7013 | 0.8309 |
| 0.0003 | 120.0 | 58440 | 1.7865 | 0.6846 | 0.7180 | 0.7009 | 0.8312 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 3.1.0
- Tokenizers 0.13.3
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