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# distilbert-base-cased-finetuned-ner-finetuned-ner

This model is a fine-tuned version of [topmas/distilbert-base-cased-finetuned-ner](https://huggingface.co/topmas/distilbert-base-cased-finetuned-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3797
- Precision: 0.6051
- Recall: 0.6048
- F1: 0.6049
- Accuracy: 0.9421

## 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: 2e-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: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 17   | 0.4153          | 0.3326    | 0.1698 | 0.2249 | 0.8947   |
| No log        | 2.0   | 34   | 0.3612          | 0.3385    | 0.2862 | 0.3102 | 0.9080   |
| No log        | 3.0   | 51   | 0.3411          | 0.4266    | 0.3058 | 0.3562 | 0.9141   |
| No log        | 4.0   | 68   | 0.3186          | 0.4545    | 0.3598 | 0.4017 | 0.9171   |
| No log        | 5.0   | 85   | 0.2984          | 0.4233    | 0.4101 | 0.4166 | 0.9181   |
| No log        | 6.0   | 102  | 0.2886          | 0.5030    | 0.3952 | 0.4427 | 0.9209   |
| No log        | 7.0   | 119  | 0.2806          | 0.4884    | 0.4354 | 0.4604 | 0.9227   |
| No log        | 8.0   | 136  | 0.2629          | 0.4994    | 0.4804 | 0.4898 | 0.9250   |
| No log        | 9.0   | 153  | 0.2686          | 0.5052    | 0.4852 | 0.4950 | 0.9265   |
| No log        | 10.0  | 170  | 0.2656          | 0.5278    | 0.4868 | 0.5065 | 0.9276   |
| No log        | 11.0  | 187  | 0.2539          | 0.4924    | 0.5328 | 0.5118 | 0.9275   |
| No log        | 12.0  | 204  | 0.2792          | 0.6093    | 0.4868 | 0.5412 | 0.9338   |
| No log        | 13.0  | 221  | 0.2518          | 0.5263    | 0.5450 | 0.5355 | 0.9314   |
| No log        | 14.0  | 238  | 0.2677          | 0.5171    | 0.5360 | 0.5264 | 0.9312   |
| No log        | 15.0  | 255  | 0.2957          | 0.4611    | 0.5365 | 0.4960 | 0.9244   |
| No log        | 16.0  | 272  | 0.2635          | 0.5638    | 0.5492 | 0.5564 | 0.9344   |
| No log        | 17.0  | 289  | 0.2545          | 0.5052    | 0.5915 | 0.5450 | 0.9312   |
| No log        | 18.0  | 306  | 0.2600          | 0.5660    | 0.5556 | 0.5607 | 0.9343   |
| No log        | 19.0  | 323  | 0.2767          | 0.5880    | 0.5339 | 0.5596 | 0.9364   |
| No log        | 20.0  | 340  | 0.2832          | 0.5782    | 0.5534 | 0.5656 | 0.9359   |
| No log        | 21.0  | 357  | 0.2660          | 0.5621    | 0.5841 | 0.5729 | 0.9367   |
| No log        | 22.0  | 374  | 0.2855          | 0.4915    | 0.6085 | 0.5437 | 0.9255   |
| No log        | 23.0  | 391  | 0.2623          | 0.6019    | 0.5735 | 0.5874 | 0.9384   |
| No log        | 24.0  | 408  | 0.2615          | 0.5711    | 0.5884 | 0.5796 | 0.9380   |
| No log        | 25.0  | 425  | 0.2876          | 0.5545    | 0.5603 | 0.5574 | 0.9358   |
| No log        | 26.0  | 442  | 0.2890          | 0.5796    | 0.5757 | 0.5776 | 0.9380   |
| No log        | 27.0  | 459  | 0.2660          | 0.5731    | 0.5889 | 0.5809 | 0.9389   |
| No log        | 28.0  | 476  | 0.2849          | 0.5818    | 0.5778 | 0.5798 | 0.9372   |
| No log        | 29.0  | 493  | 0.2930          | 0.5817    | 0.5878 | 0.5847 | 0.9386   |
| 0.1365        | 30.0  | 510  | 0.2825          | 0.5310    | 0.6169 | 0.5707 | 0.9340   |
| 0.1365        | 31.0  | 527  | 0.2978          | 0.6088    | 0.5566 | 0.5815 | 0.9396   |
| 0.1365        | 32.0  | 544  | 0.2835          | 0.5860    | 0.6005 | 0.5932 | 0.9394   |
| 0.1365        | 33.0  | 561  | 0.2979          | 0.6040    | 0.5640 | 0.5833 | 0.9400   |
| 0.1365        | 34.0  | 578  | 0.2975          | 0.5842    | 0.5926 | 0.5884 | 0.9395   |
| 0.1365        | 35.0  | 595  | 0.2900          | 0.5910    | 0.5926 | 0.5918 | 0.9397   |
| 0.1365        | 36.0  | 612  | 0.2826          | 0.5751    | 0.6180 | 0.5958 | 0.9400   |
| 0.1365        | 37.0  | 629  | 0.2938          | 0.6038    | 0.5847 | 0.5941 | 0.9413   |
| 0.1365        | 38.0  | 646  | 0.3010          | 0.5871    | 0.5868 | 0.5869 | 0.9398   |
| 0.1365        | 39.0  | 663  | 0.3090          | 0.5720    | 0.5741 | 0.5730 | 0.9376   |
| 0.1365        | 40.0  | 680  | 0.3134          | 0.5872    | 0.5788 | 0.5830 | 0.9390   |
| 0.1365        | 41.0  | 697  | 0.2956          | 0.5781    | 0.6127 | 0.5949 | 0.9392   |
| 0.1365        | 42.0  | 714  | 0.3220          | 0.5854    | 0.5619 | 0.5734 | 0.9382   |
| 0.1365        | 43.0  | 731  | 0.2978          | 0.5560    | 0.6090 | 0.5813 | 0.9378   |
| 0.1365        | 44.0  | 748  | 0.3057          | 0.5973    | 0.5815 | 0.5893 | 0.9403   |
| 0.1365        | 45.0  | 765  | 0.3032          | 0.6193    | 0.5878 | 0.6031 | 0.9420   |
| 0.1365        | 46.0  | 782  | 0.3240          | 0.6094    | 0.5746 | 0.5915 | 0.9400   |
| 0.1365        | 47.0  | 799  | 0.2982          | 0.5999    | 0.6148 | 0.6073 | 0.9412   |
| 0.1365        | 48.0  | 816  | 0.3125          | 0.6052    | 0.5979 | 0.6015 | 0.9412   |
| 0.1365        | 49.0  | 833  | 0.3206          | 0.5729    | 0.5984 | 0.5854 | 0.9396   |
| 0.1365        | 50.0  | 850  | 0.3107          | 0.5823    | 0.6138 | 0.5976 | 0.9408   |
| 0.1365        | 51.0  | 867  | 0.3186          | 0.6107    | 0.5942 | 0.6023 | 0.9409   |
| 0.1365        | 52.0  | 884  | 0.3132          | 0.5971    | 0.5889 | 0.5930 | 0.9410   |
| 0.1365        | 53.0  | 901  | 0.3138          | 0.5709    | 0.6138 | 0.5915 | 0.9398   |
| 0.1365        | 54.0  | 918  | 0.3202          | 0.5868    | 0.5905 | 0.5886 | 0.9406   |
| 0.1365        | 55.0  | 935  | 0.3036          | 0.5892    | 0.6201 | 0.6043 | 0.9420   |
| 0.1365        | 56.0  | 952  | 0.3158          | 0.5783    | 0.6016 | 0.5897 | 0.9407   |
| 0.1365        | 57.0  | 969  | 0.3083          | 0.6024    | 0.6053 | 0.6039 | 0.9421   |
| 0.1365        | 58.0  | 986  | 0.3262          | 0.5831    | 0.5905 | 0.5868 | 0.9392   |
| 0.018         | 59.0  | 1003 | 0.3194          | 0.5783    | 0.6037 | 0.5907 | 0.9398   |
| 0.018         | 60.0  | 1020 | 0.3348          | 0.6130    | 0.5741 | 0.5929 | 0.9413   |
| 0.018         | 61.0  | 1037 | 0.3273          | 0.6053    | 0.6085 | 0.6069 | 0.9421   |
| 0.018         | 62.0  | 1054 | 0.3162          | 0.5936    | 0.6159 | 0.6045 | 0.9412   |
| 0.018         | 63.0  | 1071 | 0.3269          | 0.6105    | 0.5804 | 0.5951 | 0.9417   |
| 0.018         | 64.0  | 1088 | 0.3253          | 0.5941    | 0.6111 | 0.6025 | 0.9420   |
| 0.018         | 65.0  | 1105 | 0.3299          | 0.5884    | 0.6037 | 0.5960 | 0.9414   |
| 0.018         | 66.0  | 1122 | 0.3361          | 0.5970    | 0.6074 | 0.6022 | 0.9408   |
| 0.018         | 67.0  | 1139 | 0.3286          | 0.6032    | 0.6    | 0.6016 | 0.9407   |
| 0.018         | 68.0  | 1156 | 0.3384          | 0.5857    | 0.5984 | 0.5920 | 0.9404   |
| 0.018         | 69.0  | 1173 | 0.3371          | 0.5968    | 0.5984 | 0.5976 | 0.9409   |
| 0.018         | 70.0  | 1190 | 0.3352          | 0.5962    | 0.6148 | 0.6054 | 0.9409   |
| 0.018         | 71.0  | 1207 | 0.3318          | 0.5865    | 0.6153 | 0.6006 | 0.9409   |
| 0.018         | 72.0  | 1224 | 0.3494          | 0.5980    | 0.5878 | 0.5928 | 0.9408   |
| 0.018         | 73.0  | 1241 | 0.3303          | 0.6030    | 0.6148 | 0.6089 | 0.9415   |
| 0.018         | 74.0  | 1258 | 0.3455          | 0.5878    | 0.5931 | 0.5905 | 0.9403   |
| 0.018         | 75.0  | 1275 | 0.3311          | 0.5948    | 0.6291 | 0.6115 | 0.9411   |
| 0.018         | 76.0  | 1292 | 0.3396          | 0.6007    | 0.5963 | 0.5985 | 0.9419   |
| 0.018         | 77.0  | 1309 | 0.3372          | 0.5830    | 0.6153 | 0.5987 | 0.9404   |
| 0.018         | 78.0  | 1326 | 0.3484          | 0.5993    | 0.5942 | 0.5967 | 0.9408   |
| 0.018         | 79.0  | 1343 | 0.3408          | 0.6108    | 0.6011 | 0.6059 | 0.9422   |
| 0.018         | 80.0  | 1360 | 0.3462          | 0.6067    | 0.5910 | 0.5988 | 0.9415   |
| 0.018         | 81.0  | 1377 | 0.3505          | 0.6158    | 0.5995 | 0.6075 | 0.9418   |
| 0.018         | 82.0  | 1394 | 0.3347          | 0.6105    | 0.6153 | 0.6129 | 0.9424   |
| 0.018         | 83.0  | 1411 | 0.3449          | 0.5721    | 0.6212 | 0.5956 | 0.9390   |
| 0.018         | 84.0  | 1428 | 0.3428          | 0.5754    | 0.6296 | 0.6013 | 0.9398   |
| 0.018         | 85.0  | 1445 | 0.3456          | 0.5802    | 0.6222 | 0.6005 | 0.9399   |
| 0.018         | 86.0  | 1462 | 0.3524          | 0.6109    | 0.5989 | 0.6049 | 0.9418   |
| 0.018         | 87.0  | 1479 | 0.3563          | 0.6160    | 0.5899 | 0.6027 | 0.9421   |
| 0.018         | 88.0  | 1496 | 0.3571          | 0.6101    | 0.5937 | 0.6018 | 0.9423   |
| 0.0069        | 89.0  | 1513 | 0.3489          | 0.6076    | 0.5974 | 0.6025 | 0.9424   |
| 0.0069        | 90.0  | 1530 | 0.3418          | 0.5983    | 0.6217 | 0.6098 | 0.9421   |
| 0.0069        | 91.0  | 1547 | 0.3495          | 0.5851    | 0.5984 | 0.5917 | 0.9403   |
| 0.0069        | 92.0  | 1564 | 0.3483          | 0.6114    | 0.6085 | 0.6099 | 0.9428   |
| 0.0069        | 93.0  | 1581 | 0.3508          | 0.6026    | 0.6090 | 0.6058 | 0.9413   |
| 0.0069        | 94.0  | 1598 | 0.3499          | 0.6117    | 0.6026 | 0.6071 | 0.9415   |
| 0.0069        | 95.0  | 1615 | 0.3394          | 0.5999    | 0.6164 | 0.6080 | 0.9420   |
| 0.0069        | 96.0  | 1632 | 0.3610          | 0.6007    | 0.5995 | 0.6001 | 0.9410   |
| 0.0069        | 97.0  | 1649 | 0.3471          | 0.5979    | 0.6206 | 0.6090 | 0.9416   |
| 0.0069        | 98.0  | 1666 | 0.3559          | 0.5902    | 0.5974 | 0.5937 | 0.9408   |
| 0.0069        | 99.0  | 1683 | 0.3528          | 0.6140    | 0.6101 | 0.6120 | 0.9426   |
| 0.0069        | 100.0 | 1700 | 0.3552          | 0.5901    | 0.6153 | 0.6024 | 0.9415   |
| 0.0069        | 101.0 | 1717 | 0.3684          | 0.6201    | 0.5847 | 0.6019 | 0.9420   |
| 0.0069        | 102.0 | 1734 | 0.3432          | 0.6075    | 0.6011 | 0.6043 | 0.9424   |
| 0.0069        | 103.0 | 1751 | 0.3567          | 0.6270    | 0.5905 | 0.6082 | 0.9427   |
| 0.0069        | 104.0 | 1768 | 0.3539          | 0.6009    | 0.5958 | 0.5983 | 0.9417   |
| 0.0069        | 105.0 | 1785 | 0.3456          | 0.5976    | 0.6169 | 0.6071 | 0.9424   |
| 0.0069        | 106.0 | 1802 | 0.3551          | 0.6211    | 0.6037 | 0.6123 | 0.9429   |
| 0.0069        | 107.0 | 1819 | 0.3571          | 0.6035    | 0.6063 | 0.6049 | 0.9423   |
| 0.0069        | 108.0 | 1836 | 0.3512          | 0.5910    | 0.6222 | 0.6062 | 0.9413   |
| 0.0069        | 109.0 | 1853 | 0.3527          | 0.6031    | 0.6079 | 0.6055 | 0.9420   |
| 0.0069        | 110.0 | 1870 | 0.3593          | 0.6213    | 0.5894 | 0.6049 | 0.9421   |
| 0.0069        | 111.0 | 1887 | 0.3552          | 0.6179    | 0.5905 | 0.6039 | 0.9423   |
| 0.0069        | 112.0 | 1904 | 0.3552          | 0.5891    | 0.6138 | 0.6012 | 0.9410   |
| 0.0069        | 113.0 | 1921 | 0.3649          | 0.6290    | 0.5884 | 0.6080 | 0.9414   |
| 0.0069        | 114.0 | 1938 | 0.3677          | 0.6052    | 0.5968 | 0.6010 | 0.9412   |
| 0.0069        | 115.0 | 1955 | 0.3615          | 0.6067    | 0.5884 | 0.5974 | 0.9405   |
| 0.0069        | 116.0 | 1972 | 0.3659          | 0.5927    | 0.5937 | 0.5932 | 0.9404   |
| 0.0069        | 117.0 | 1989 | 0.3652          | 0.6099    | 0.5931 | 0.6014 | 0.9410   |
| 0.0042        | 118.0 | 2006 | 0.3668          | 0.6080    | 0.6048 | 0.6064 | 0.9419   |
| 0.0042        | 119.0 | 2023 | 0.3632          | 0.6173    | 0.6026 | 0.6099 | 0.9428   |
| 0.0042        | 120.0 | 2040 | 0.3548          | 0.6004    | 0.6090 | 0.6047 | 0.9414   |
| 0.0042        | 121.0 | 2057 | 0.3617          | 0.6077    | 0.6    | 0.6038 | 0.9417   |
| 0.0042        | 122.0 | 2074 | 0.3676          | 0.6119    | 0.5857 | 0.5985 | 0.9414   |
| 0.0042        | 123.0 | 2091 | 0.3621          | 0.5965    | 0.6085 | 0.6024 | 0.9406   |
| 0.0042        | 124.0 | 2108 | 0.3687          | 0.6063    | 0.6111 | 0.6087 | 0.9412   |
| 0.0042        | 125.0 | 2125 | 0.3635          | 0.6144    | 0.6011 | 0.6076 | 0.9417   |
| 0.0042        | 126.0 | 2142 | 0.3709          | 0.6020    | 0.5931 | 0.5975 | 0.9406   |
| 0.0042        | 127.0 | 2159 | 0.3571          | 0.6023    | 0.6217 | 0.6118 | 0.9415   |
| 0.0042        | 128.0 | 2176 | 0.3722          | 0.6229    | 0.5926 | 0.6074 | 0.9416   |
| 0.0042        | 129.0 | 2193 | 0.3647          | 0.6156    | 0.6085 | 0.6120 | 0.9420   |
| 0.0042        | 130.0 | 2210 | 0.3595          | 0.5844    | 0.6228 | 0.6030 | 0.9404   |
| 0.0042        | 131.0 | 2227 | 0.3750          | 0.6243    | 0.5820 | 0.6024 | 0.9421   |
| 0.0042        | 132.0 | 2244 | 0.3704          | 0.5986    | 0.5862 | 0.5924 | 0.9411   |
| 0.0042        | 133.0 | 2261 | 0.3660          | 0.6039    | 0.5952 | 0.5995 | 0.9413   |
| 0.0042        | 134.0 | 2278 | 0.3762          | 0.6178    | 0.5926 | 0.6049 | 0.9415   |
| 0.0042        | 135.0 | 2295 | 0.3784          | 0.5993    | 0.5825 | 0.5908 | 0.9405   |
| 0.0042        | 136.0 | 2312 | 0.3718          | 0.5925    | 0.6101 | 0.6011 | 0.9411   |
| 0.0042        | 137.0 | 2329 | 0.3832          | 0.6111    | 0.5862 | 0.5984 | 0.9415   |
| 0.0042        | 138.0 | 2346 | 0.3765          | 0.5919    | 0.6101 | 0.6008 | 0.9412   |
| 0.0042        | 139.0 | 2363 | 0.3834          | 0.6042    | 0.5984 | 0.6013 | 0.9413   |
| 0.0042        | 140.0 | 2380 | 0.3763          | 0.6042    | 0.6026 | 0.6034 | 0.9415   |
| 0.0042        | 141.0 | 2397 | 0.3814          | 0.6075    | 0.5905 | 0.5989 | 0.9416   |
| 0.0042        | 142.0 | 2414 | 0.3781          | 0.6027    | 0.5963 | 0.5995 | 0.9412   |
| 0.0042        | 143.0 | 2431 | 0.3829          | 0.6085    | 0.5921 | 0.6002 | 0.9414   |
| 0.0042        | 144.0 | 2448 | 0.3742          | 0.5907    | 0.6032 | 0.5969 | 0.9406   |
| 0.0042        | 145.0 | 2465 | 0.3740          | 0.6082    | 0.6037 | 0.6059 | 0.9418   |
| 0.0042        | 146.0 | 2482 | 0.3703          | 0.6081    | 0.6175 | 0.6128 | 0.9425   |
| 0.0042        | 147.0 | 2499 | 0.3781          | 0.6189    | 0.5921 | 0.6052 | 0.9422   |
| 0.0032        | 148.0 | 2516 | 0.3754          | 0.5985    | 0.6042 | 0.6014 | 0.9408   |
| 0.0032        | 149.0 | 2533 | 0.3712          | 0.6055    | 0.5984 | 0.6019 | 0.9416   |
| 0.0032        | 150.0 | 2550 | 0.3688          | 0.6095    | 0.6053 | 0.6074 | 0.9423   |
| 0.0032        | 151.0 | 2567 | 0.3736          | 0.6036    | 0.6058 | 0.6047 | 0.9419   |
| 0.0032        | 152.0 | 2584 | 0.3684          | 0.6165    | 0.6048 | 0.6106 | 0.9421   |
| 0.0032        | 153.0 | 2601 | 0.3677          | 0.6050    | 0.6053 | 0.6051 | 0.9421   |
| 0.0032        | 154.0 | 2618 | 0.3732          | 0.5877    | 0.5995 | 0.5935 | 0.9404   |
| 0.0032        | 155.0 | 2635 | 0.3717          | 0.6156    | 0.6    | 0.6077 | 0.9424   |
| 0.0032        | 156.0 | 2652 | 0.3711          | 0.5961    | 0.6037 | 0.5999 | 0.9410   |
| 0.0032        | 157.0 | 2669 | 0.3731          | 0.6018    | 0.6037 | 0.6027 | 0.9420   |
| 0.0032        | 158.0 | 2686 | 0.3712          | 0.6175    | 0.6063 | 0.6119 | 0.9426   |
| 0.0032        | 159.0 | 2703 | 0.3744          | 0.6041    | 0.6079 | 0.6060 | 0.9418   |
| 0.0032        | 160.0 | 2720 | 0.3788          | 0.5946    | 0.6085 | 0.6015 | 0.9412   |
| 0.0032        | 161.0 | 2737 | 0.3836          | 0.6108    | 0.5878 | 0.5991 | 0.9419   |
| 0.0032        | 162.0 | 2754 | 0.3748          | 0.6063    | 0.6127 | 0.6095 | 0.9418   |
| 0.0032        | 163.0 | 2771 | 0.3763          | 0.6243    | 0.6005 | 0.6122 | 0.9426   |
| 0.0032        | 164.0 | 2788 | 0.3772          | 0.6025    | 0.6111 | 0.6068 | 0.9413   |
| 0.0032        | 165.0 | 2805 | 0.3780          | 0.6044    | 0.6063 | 0.6054 | 0.9413   |
| 0.0032        | 166.0 | 2822 | 0.3788          | 0.6074    | 0.6016 | 0.6045 | 0.9417   |
| 0.0032        | 167.0 | 2839 | 0.3720          | 0.5984    | 0.6127 | 0.6055 | 0.9419   |
| 0.0032        | 168.0 | 2856 | 0.3767          | 0.5960    | 0.6111 | 0.6034 | 0.9418   |
| 0.0032        | 169.0 | 2873 | 0.3730          | 0.5948    | 0.6106 | 0.6026 | 0.9417   |
| 0.0032        | 170.0 | 2890 | 0.3709          | 0.6       | 0.6079 | 0.6039 | 0.9419   |
| 0.0032        | 171.0 | 2907 | 0.3695          | 0.5958    | 0.6106 | 0.6031 | 0.9417   |
| 0.0032        | 172.0 | 2924 | 0.3752          | 0.5878    | 0.6127 | 0.6    | 0.9413   |
| 0.0032        | 173.0 | 2941 | 0.3748          | 0.6007    | 0.6058 | 0.6033 | 0.9419   |
| 0.0032        | 174.0 | 2958 | 0.3742          | 0.6054    | 0.5958 | 0.6005 | 0.9419   |
| 0.0032        | 175.0 | 2975 | 0.3717          | 0.5996    | 0.6037 | 0.6016 | 0.9417   |
| 0.0032        | 176.0 | 2992 | 0.3741          | 0.5970    | 0.6011 | 0.5990 | 0.9417   |
| 0.0027        | 177.0 | 3009 | 0.3741          | 0.5986    | 0.6021 | 0.6004 | 0.9418   |
| 0.0027        | 178.0 | 3026 | 0.3768          | 0.5959    | 0.5984 | 0.5971 | 0.9416   |
| 0.0027        | 179.0 | 3043 | 0.3783          | 0.5925    | 0.5963 | 0.5944 | 0.9413   |
| 0.0027        | 180.0 | 3060 | 0.3793          | 0.6022    | 0.5952 | 0.5987 | 0.9418   |
| 0.0027        | 181.0 | 3077 | 0.3827          | 0.6061    | 0.5952 | 0.6006 | 0.9417   |
| 0.0027        | 182.0 | 3094 | 0.3834          | 0.6090    | 0.5926 | 0.6007 | 0.9422   |
| 0.0027        | 183.0 | 3111 | 0.3790          | 0.6037    | 0.6005 | 0.6021 | 0.9419   |
| 0.0027        | 184.0 | 3128 | 0.3838          | 0.6075    | 0.5905 | 0.5989 | 0.9419   |
| 0.0027        | 185.0 | 3145 | 0.3780          | 0.6009    | 0.6063 | 0.6036 | 0.9418   |
| 0.0027        | 186.0 | 3162 | 0.3785          | 0.5998    | 0.6058 | 0.6028 | 0.9418   |
| 0.0027        | 187.0 | 3179 | 0.3790          | 0.6031    | 0.5989 | 0.6010 | 0.9419   |
| 0.0027        | 188.0 | 3196 | 0.3808          | 0.6060    | 0.5915 | 0.5987 | 0.9418   |
| 0.0027        | 189.0 | 3213 | 0.3831          | 0.6095    | 0.5905 | 0.5998 | 0.9419   |
| 0.0027        | 190.0 | 3230 | 0.3820          | 0.6068    | 0.5952 | 0.6010 | 0.9419   |
| 0.0027        | 191.0 | 3247 | 0.3812          | 0.6024    | 0.5989 | 0.6007 | 0.9419   |
| 0.0027        | 192.0 | 3264 | 0.3808          | 0.6054    | 0.6    | 0.6027 | 0.9418   |
| 0.0027        | 193.0 | 3281 | 0.3809          | 0.6071    | 0.6    | 0.6035 | 0.9421   |
| 0.0027        | 194.0 | 3298 | 0.3803          | 0.6054    | 0.6016 | 0.6035 | 0.9421   |
| 0.0027        | 195.0 | 3315 | 0.3799          | 0.6048    | 0.6032 | 0.6040 | 0.9421   |
| 0.0027        | 196.0 | 3332 | 0.3805          | 0.6056    | 0.6021 | 0.6039 | 0.9421   |
| 0.0027        | 197.0 | 3349 | 0.3798          | 0.6053    | 0.6037 | 0.6045 | 0.9421   |
| 0.0027        | 198.0 | 3366 | 0.3795          | 0.6050    | 0.6053 | 0.6051 | 0.9421   |
| 0.0027        | 199.0 | 3383 | 0.3798          | 0.6048    | 0.6048 | 0.6048 | 0.9421   |
| 0.0027        | 200.0 | 3400 | 0.3797          | 0.6051    | 0.6048 | 0.6049 | 0.9421   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-cased-finetuned-ner-finetuned-ner", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-cased-finetuned-ner-finetuned-ner
=================================================


This model is a fine-tuned version of topmas/distilbert-base-cased-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3797
* Precision: 0.6051
* Recall: 0.6048
* F1: 0.6049
* Accuracy: 0.9421


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: 2e-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: 200


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 200 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 200 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 200### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2