scenario-kd-po-ner-half_data-univner_full44
This model is a fine-tuned version of haryoaw/scenario-TCR-NER_data-univner_full on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4036
- Precision: 0.8328
- Recall: 0.8256
- F1: 0.8292
- Accuracy: 0.9821
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: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.9712 | 0.2910 | 500 | 0.7018 | 0.7311 | 0.7712 | 0.7506 | 0.9758 |
0.5607 | 0.5821 | 1000 | 0.6182 | 0.7648 | 0.7927 | 0.7785 | 0.9780 |
0.5004 | 0.8731 | 1500 | 0.5862 | 0.7844 | 0.7895 | 0.7869 | 0.9786 |
0.4431 | 1.1641 | 2000 | 0.5566 | 0.7646 | 0.8132 | 0.7881 | 0.9787 |
0.4064 | 1.4552 | 2500 | 0.5562 | 0.7940 | 0.7979 | 0.7959 | 0.9795 |
0.3839 | 1.7462 | 3000 | 0.5438 | 0.7918 | 0.7971 | 0.7944 | 0.9791 |
0.3668 | 2.0373 | 3500 | 0.5229 | 0.7997 | 0.7928 | 0.7963 | 0.9796 |
0.328 | 2.3283 | 4000 | 0.5153 | 0.8032 | 0.7980 | 0.8006 | 0.9797 |
0.3247 | 2.6193 | 4500 | 0.5129 | 0.8029 | 0.7957 | 0.7993 | 0.9797 |
0.3186 | 2.9104 | 5000 | 0.5061 | 0.8012 | 0.8049 | 0.8031 | 0.9798 |
0.2965 | 3.2014 | 5500 | 0.4900 | 0.8091 | 0.8159 | 0.8125 | 0.9809 |
0.2827 | 3.4924 | 6000 | 0.4904 | 0.7916 | 0.8225 | 0.8068 | 0.9799 |
0.2788 | 3.7835 | 6500 | 0.4974 | 0.8184 | 0.7966 | 0.8073 | 0.9805 |
0.2711 | 4.0745 | 7000 | 0.4937 | 0.8129 | 0.8075 | 0.8102 | 0.9804 |
0.2557 | 4.3655 | 7500 | 0.4804 | 0.8058 | 0.8129 | 0.8093 | 0.9805 |
0.2512 | 4.6566 | 8000 | 0.4762 | 0.8092 | 0.8127 | 0.8110 | 0.9807 |
0.2493 | 4.9476 | 8500 | 0.4673 | 0.8102 | 0.8136 | 0.8119 | 0.9806 |
0.2327 | 5.2386 | 9000 | 0.4668 | 0.8113 | 0.8163 | 0.8138 | 0.9810 |
0.2268 | 5.5297 | 9500 | 0.4650 | 0.8211 | 0.8020 | 0.8115 | 0.9807 |
0.2314 | 5.8207 | 10000 | 0.4747 | 0.8086 | 0.8152 | 0.8119 | 0.9804 |
0.2238 | 6.1118 | 10500 | 0.4577 | 0.8144 | 0.8067 | 0.8105 | 0.9806 |
0.2077 | 6.4028 | 11000 | 0.4646 | 0.8157 | 0.8147 | 0.8152 | 0.9809 |
0.2114 | 6.6938 | 11500 | 0.4639 | 0.8173 | 0.8153 | 0.8163 | 0.9812 |
0.2098 | 6.9849 | 12000 | 0.4545 | 0.8230 | 0.8090 | 0.8159 | 0.9809 |
0.1965 | 7.2759 | 12500 | 0.4511 | 0.8168 | 0.8181 | 0.8174 | 0.9812 |
0.1952 | 7.5669 | 13000 | 0.4553 | 0.8153 | 0.8191 | 0.8172 | 0.9813 |
0.197 | 7.8580 | 13500 | 0.4441 | 0.8100 | 0.8221 | 0.8160 | 0.9811 |
0.1868 | 8.1490 | 14000 | 0.4439 | 0.8189 | 0.8214 | 0.8201 | 0.9813 |
0.1827 | 8.4400 | 14500 | 0.4500 | 0.8194 | 0.8160 | 0.8177 | 0.9810 |
0.1822 | 8.7311 | 15000 | 0.4460 | 0.8188 | 0.8139 | 0.8164 | 0.9810 |
0.1811 | 9.0221 | 15500 | 0.4449 | 0.8145 | 0.8152 | 0.8148 | 0.9809 |
0.1708 | 9.3132 | 16000 | 0.4543 | 0.8232 | 0.8110 | 0.8171 | 0.9810 |
0.171 | 9.6042 | 16500 | 0.4464 | 0.8182 | 0.8171 | 0.8176 | 0.9814 |
0.1726 | 9.8952 | 17000 | 0.4381 | 0.8158 | 0.8234 | 0.8196 | 0.9814 |
0.1646 | 10.1863 | 17500 | 0.4392 | 0.8183 | 0.8273 | 0.8228 | 0.9815 |
0.162 | 10.4773 | 18000 | 0.4351 | 0.8142 | 0.8208 | 0.8175 | 0.9811 |
0.1619 | 10.7683 | 18500 | 0.4434 | 0.8154 | 0.8191 | 0.8172 | 0.9811 |
0.1588 | 11.0594 | 19000 | 0.4441 | 0.8226 | 0.8116 | 0.8171 | 0.9811 |
0.155 | 11.3504 | 19500 | 0.4316 | 0.8176 | 0.8224 | 0.8200 | 0.9817 |
0.1533 | 11.6414 | 20000 | 0.4360 | 0.8249 | 0.8117 | 0.8183 | 0.9814 |
0.1528 | 11.9325 | 20500 | 0.4388 | 0.8164 | 0.8182 | 0.8173 | 0.9811 |
0.1484 | 12.2235 | 21000 | 0.4282 | 0.8258 | 0.8168 | 0.8213 | 0.9812 |
0.1468 | 12.5146 | 21500 | 0.4422 | 0.8213 | 0.8136 | 0.8174 | 0.9808 |
0.1495 | 12.8056 | 22000 | 0.4321 | 0.8156 | 0.8142 | 0.8149 | 0.9809 |
0.1452 | 13.0966 | 22500 | 0.4405 | 0.8240 | 0.8088 | 0.8164 | 0.9813 |
0.1415 | 13.3877 | 23000 | 0.4365 | 0.8268 | 0.8175 | 0.8221 | 0.9814 |
0.1412 | 13.6787 | 23500 | 0.4346 | 0.8246 | 0.8149 | 0.8197 | 0.9810 |
0.1407 | 13.9697 | 24000 | 0.4257 | 0.8191 | 0.8260 | 0.8226 | 0.9816 |
0.1359 | 14.2608 | 24500 | 0.4296 | 0.8262 | 0.8181 | 0.8221 | 0.9815 |
0.1359 | 14.5518 | 25000 | 0.4301 | 0.8180 | 0.8166 | 0.8173 | 0.9811 |
0.1356 | 14.8428 | 25500 | 0.4317 | 0.8148 | 0.8269 | 0.8208 | 0.9813 |
0.1331 | 15.1339 | 26000 | 0.4313 | 0.8266 | 0.8090 | 0.8177 | 0.9812 |
0.1308 | 15.4249 | 26500 | 0.4269 | 0.8251 | 0.8175 | 0.8213 | 0.9816 |
0.1306 | 15.7159 | 27000 | 0.4301 | 0.8217 | 0.8218 | 0.8218 | 0.9816 |
0.1319 | 16.0070 | 27500 | 0.4204 | 0.8235 | 0.8248 | 0.8242 | 0.9817 |
0.1254 | 16.2980 | 28000 | 0.4234 | 0.8257 | 0.8283 | 0.8270 | 0.9819 |
0.1265 | 16.5891 | 28500 | 0.4223 | 0.8236 | 0.8225 | 0.8231 | 0.9817 |
0.1288 | 16.8801 | 29000 | 0.4225 | 0.8332 | 0.8257 | 0.8294 | 0.9819 |
0.1259 | 17.1711 | 29500 | 0.4184 | 0.8225 | 0.8244 | 0.8235 | 0.9815 |
0.1233 | 17.4622 | 30000 | 0.4216 | 0.8310 | 0.8173 | 0.8241 | 0.9817 |
0.1243 | 17.7532 | 30500 | 0.4151 | 0.8238 | 0.8296 | 0.8267 | 0.9818 |
0.1222 | 18.0442 | 31000 | 0.4216 | 0.8290 | 0.8199 | 0.8245 | 0.9815 |
0.1207 | 18.3353 | 31500 | 0.4192 | 0.8286 | 0.8147 | 0.8216 | 0.9816 |
0.1202 | 18.6263 | 32000 | 0.4138 | 0.8308 | 0.8185 | 0.8246 | 0.9817 |
0.1209 | 18.9173 | 32500 | 0.4195 | 0.8318 | 0.8215 | 0.8267 | 0.9821 |
0.1196 | 19.2084 | 33000 | 0.4137 | 0.8356 | 0.8137 | 0.8245 | 0.9818 |
0.1184 | 19.4994 | 33500 | 0.4152 | 0.8262 | 0.8225 | 0.8244 | 0.9818 |
0.1177 | 19.7905 | 34000 | 0.4154 | 0.8333 | 0.8308 | 0.8320 | 0.9821 |
0.1155 | 20.0815 | 34500 | 0.4095 | 0.8281 | 0.8250 | 0.8265 | 0.9820 |
0.1168 | 20.3725 | 35000 | 0.4130 | 0.8326 | 0.8175 | 0.8250 | 0.9817 |
0.1147 | 20.6636 | 35500 | 0.4134 | 0.8264 | 0.8185 | 0.8224 | 0.9817 |
0.1153 | 20.9546 | 36000 | 0.4094 | 0.8306 | 0.8225 | 0.8265 | 0.9819 |
0.1144 | 21.2456 | 36500 | 0.4150 | 0.8303 | 0.8194 | 0.8248 | 0.9817 |
0.1121 | 21.5367 | 37000 | 0.4096 | 0.8283 | 0.8225 | 0.8254 | 0.9819 |
0.1135 | 21.8277 | 37500 | 0.4085 | 0.8311 | 0.8250 | 0.8280 | 0.9819 |
0.1137 | 22.1187 | 38000 | 0.4079 | 0.8351 | 0.8233 | 0.8291 | 0.9822 |
0.1098 | 22.4098 | 38500 | 0.4067 | 0.8273 | 0.8293 | 0.8283 | 0.9821 |
0.1117 | 22.7008 | 39000 | 0.4083 | 0.8278 | 0.8266 | 0.8272 | 0.9821 |
0.1105 | 22.9919 | 39500 | 0.4155 | 0.8321 | 0.8251 | 0.8286 | 0.9819 |
0.1101 | 23.2829 | 40000 | 0.4100 | 0.8301 | 0.8241 | 0.8271 | 0.9817 |
0.1087 | 23.5739 | 40500 | 0.4091 | 0.8285 | 0.8173 | 0.8229 | 0.9815 |
0.1094 | 23.8650 | 41000 | 0.4092 | 0.8292 | 0.8208 | 0.8250 | 0.9819 |
0.1081 | 24.1560 | 41500 | 0.4101 | 0.8355 | 0.8212 | 0.8283 | 0.9819 |
0.1071 | 24.4470 | 42000 | 0.4110 | 0.8319 | 0.8243 | 0.8281 | 0.9819 |
0.1089 | 24.7381 | 42500 | 0.4098 | 0.8287 | 0.8173 | 0.8230 | 0.9816 |
0.1073 | 25.0291 | 43000 | 0.4080 | 0.8301 | 0.8199 | 0.8250 | 0.9818 |
0.1068 | 25.3201 | 43500 | 0.4018 | 0.8303 | 0.8237 | 0.8270 | 0.9822 |
0.1069 | 25.6112 | 44000 | 0.4036 | 0.8328 | 0.8256 | 0.8292 | 0.9821 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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Model tree for haryoaw/scenario-kd-po-ner-half_data-univner_full44
Base model
FacebookAI/xlm-roberta-base