--- base_model: haryoaw/scenario-TCR-NER_data-univner_full library_name: transformers license: mit metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: scenario-kd-po-ner-full_data-univner_full55 results: [] --- # scenario-kd-po-ner-full_data-univner_full55 This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3977 - Precision: 0.8322 - Recall: 0.8250 - F1: 0.8286 - Accuracy: 0.9824 ## 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: 55 - 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 | |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.9599 | 0.2910 | 500 | 0.7292 | 0.7506 | 0.7468 | 0.7487 | 0.9756 | | 0.5565 | 0.5821 | 1000 | 0.6142 | 0.7746 | 0.7775 | 0.7761 | 0.9783 | | 0.5029 | 0.8731 | 1500 | 0.5821 | 0.7811 | 0.7804 | 0.7807 | 0.9786 | | 0.4411 | 1.1641 | 2000 | 0.5625 | 0.7944 | 0.7860 | 0.7902 | 0.9793 | | 0.3891 | 1.4552 | 2500 | 0.5504 | 0.7850 | 0.7990 | 0.7919 | 0.9791 | | 0.3893 | 1.7462 | 3000 | 0.5271 | 0.7792 | 0.8126 | 0.7955 | 0.9791 | | 0.3654 | 2.0373 | 3500 | 0.5237 | 0.7939 | 0.8031 | 0.7985 | 0.9797 | | 0.3332 | 2.3283 | 4000 | 0.5146 | 0.7948 | 0.8204 | 0.8074 | 0.9803 | | 0.3191 | 2.6193 | 4500 | 0.5136 | 0.7956 | 0.8100 | 0.8027 | 0.9801 | | 0.3158 | 2.9104 | 5000 | 0.5068 | 0.7983 | 0.8136 | 0.8059 | 0.9803 | | 0.2954 | 3.2014 | 5500 | 0.5005 | 0.7992 | 0.8158 | 0.8074 | 0.9804 | | 0.282 | 3.4924 | 6000 | 0.4871 | 0.8028 | 0.8204 | 0.8115 | 0.9804 | | 0.2806 | 3.7835 | 6500 | 0.4767 | 0.8069 | 0.8104 | 0.8087 | 0.9803 | | 0.2693 | 4.0745 | 7000 | 0.4729 | 0.7969 | 0.8150 | 0.8058 | 0.9801 | | 0.2524 | 4.3655 | 7500 | 0.4877 | 0.8169 | 0.8038 | 0.8103 | 0.9806 | | 0.2499 | 4.6566 | 8000 | 0.4748 | 0.8036 | 0.8134 | 0.8085 | 0.9803 | | 0.2513 | 4.9476 | 8500 | 0.4685 | 0.8154 | 0.8080 | 0.8117 | 0.9806 | | 0.2311 | 5.2386 | 9000 | 0.4729 | 0.8205 | 0.8065 | 0.8134 | 0.9808 | | 0.228 | 5.5297 | 9500 | 0.4614 | 0.8142 | 0.8126 | 0.8134 | 0.9812 | | 0.2281 | 5.8207 | 10000 | 0.4755 | 0.8128 | 0.8124 | 0.8126 | 0.9808 | | 0.2231 | 6.1118 | 10500 | 0.4550 | 0.8135 | 0.8160 | 0.8148 | 0.9810 | | 0.2096 | 6.4028 | 11000 | 0.4833 | 0.8254 | 0.7918 | 0.8082 | 0.9802 | | 0.2114 | 6.6938 | 11500 | 0.4562 | 0.8091 | 0.8091 | 0.8091 | 0.9807 | | 0.2085 | 6.9849 | 12000 | 0.4630 | 0.8141 | 0.8083 | 0.8112 | 0.9807 | | 0.1921 | 7.2759 | 12500 | 0.4601 | 0.8149 | 0.8201 | 0.8175 | 0.9809 | | 0.1954 | 7.5669 | 13000 | 0.4489 | 0.8231 | 0.8117 | 0.8174 | 0.9810 | | 0.1945 | 7.8580 | 13500 | 0.4572 | 0.8169 | 0.8038 | 0.8103 | 0.9806 | | 0.1889 | 8.1490 | 14000 | 0.4604 | 0.8202 | 0.8091 | 0.8146 | 0.9807 | | 0.1821 | 8.4400 | 14500 | 0.4510 | 0.8178 | 0.8204 | 0.8191 | 0.9810 | | 0.1815 | 8.7311 | 15000 | 0.4462 | 0.8179 | 0.8270 | 0.8224 | 0.9814 | | 0.1795 | 9.0221 | 15500 | 0.4396 | 0.8125 | 0.8160 | 0.8143 | 0.9810 | | 0.1711 | 9.3132 | 16000 | 0.4452 | 0.8179 | 0.8139 | 0.8159 | 0.9811 | | 0.1703 | 9.6042 | 16500 | 0.4469 | 0.8178 | 0.8140 | 0.8159 | 0.9813 | | 0.1705 | 9.8952 | 17000 | 0.4361 | 0.8110 | 0.8234 | 0.8172 | 0.9811 | | 0.1662 | 10.1863 | 17500 | 0.4399 | 0.8241 | 0.8204 | 0.8222 | 0.9816 | | 0.1618 | 10.4773 | 18000 | 0.4344 | 0.8123 | 0.8247 | 0.8184 | 0.9817 | | 0.1601 | 10.7683 | 18500 | 0.4435 | 0.8184 | 0.8116 | 0.8150 | 0.9810 | | 0.16 | 11.0594 | 19000 | 0.4434 | 0.8417 | 0.8029 | 0.8218 | 0.9814 | | 0.1511 | 11.3504 | 19500 | 0.4432 | 0.8323 | 0.8191 | 0.8256 | 0.9816 | | 0.1531 | 11.6414 | 20000 | 0.4323 | 0.8223 | 0.8194 | 0.8208 | 0.9816 | | 0.1532 | 11.9325 | 20500 | 0.4339 | 0.8201 | 0.8189 | 0.8195 | 0.9811 | | 0.1481 | 12.2235 | 21000 | 0.4232 | 0.8148 | 0.8238 | 0.8193 | 0.9817 | | 0.1455 | 12.5146 | 21500 | 0.4300 | 0.8196 | 0.8260 | 0.8228 | 0.9819 | | 0.1469 | 12.8056 | 22000 | 0.4328 | 0.8244 | 0.8182 | 0.8213 | 0.9813 | | 0.1428 | 13.0966 | 22500 | 0.4262 | 0.8236 | 0.8300 | 0.8268 | 0.9817 | | 0.1402 | 13.3877 | 23000 | 0.4295 | 0.8181 | 0.8198 | 0.8190 | 0.9812 | | 0.1407 | 13.6787 | 23500 | 0.4317 | 0.8180 | 0.8224 | 0.8202 | 0.9813 | | 0.1413 | 13.9697 | 24000 | 0.4279 | 0.8240 | 0.8160 | 0.8200 | 0.9816 | | 0.1342 | 14.2608 | 24500 | 0.4316 | 0.8184 | 0.8153 | 0.8169 | 0.9812 | | 0.1371 | 14.5518 | 25000 | 0.4215 | 0.8278 | 0.8254 | 0.8266 | 0.9817 | | 0.1337 | 14.8428 | 25500 | 0.4395 | 0.8251 | 0.8120 | 0.8185 | 0.9811 | | 0.1343 | 15.1339 | 26000 | 0.4257 | 0.8211 | 0.8235 | 0.8223 | 0.9814 | | 0.131 | 15.4249 | 26500 | 0.4299 | 0.8345 | 0.8072 | 0.8206 | 0.9812 | | 0.1315 | 15.7159 | 27000 | 0.4187 | 0.8298 | 0.8199 | 0.8248 | 0.9817 | | 0.1305 | 16.0070 | 27500 | 0.4220 | 0.8269 | 0.8175 | 0.8222 | 0.9817 | | 0.1268 | 16.2980 | 28000 | 0.4201 | 0.8257 | 0.8162 | 0.8209 | 0.9814 | | 0.1258 | 16.5891 | 28500 | 0.4210 | 0.8204 | 0.8286 | 0.8245 | 0.9819 | | 0.1275 | 16.8801 | 29000 | 0.4212 | 0.8279 | 0.8169 | 0.8224 | 0.9813 | | 0.1256 | 17.1711 | 29500 | 0.4155 | 0.8269 | 0.8166 | 0.8217 | 0.9814 | | 0.1218 | 17.4622 | 30000 | 0.4119 | 0.8236 | 0.8218 | 0.8227 | 0.9816 | | 0.1234 | 17.7532 | 30500 | 0.4223 | 0.8295 | 0.8194 | 0.8244 | 0.9818 | | 0.1216 | 18.0442 | 31000 | 0.4184 | 0.8353 | 0.8218 | 0.8285 | 0.9820 | | 0.1189 | 18.3353 | 31500 | 0.4211 | 0.8323 | 0.8184 | 0.8253 | 0.9820 | | 0.1201 | 18.6263 | 32000 | 0.4168 | 0.8302 | 0.8198 | 0.8250 | 0.9818 | | 0.1209 | 18.9173 | 32500 | 0.4124 | 0.8303 | 0.8204 | 0.8253 | 0.9817 | | 0.1187 | 19.2084 | 33000 | 0.4209 | 0.8304 | 0.8087 | 0.8194 | 0.9814 | | 0.1178 | 19.4994 | 33500 | 0.4143 | 0.8271 | 0.8199 | 0.8235 | 0.9817 | | 0.1168 | 19.7905 | 34000 | 0.4179 | 0.8304 | 0.8169 | 0.8236 | 0.9815 | | 0.118 | 20.0815 | 34500 | 0.4162 | 0.8254 | 0.8181 | 0.8217 | 0.9816 | | 0.1161 | 20.3725 | 35000 | 0.4174 | 0.8287 | 0.8150 | 0.8218 | 0.9817 | | 0.1147 | 20.6636 | 35500 | 0.4129 | 0.8278 | 0.8246 | 0.8262 | 0.9821 | | 0.114 | 20.9546 | 36000 | 0.4091 | 0.8303 | 0.8217 | 0.8260 | 0.9819 | | 0.1133 | 21.2456 | 36500 | 0.4167 | 0.8248 | 0.8224 | 0.8236 | 0.9816 | | 0.1126 | 21.5367 | 37000 | 0.4105 | 0.8314 | 0.8176 | 0.8245 | 0.9817 | | 0.1126 | 21.8277 | 37500 | 0.4052 | 0.8302 | 0.8225 | 0.8264 | 0.9820 | | 0.1111 | 22.1187 | 38000 | 0.4073 | 0.8298 | 0.8221 | 0.8259 | 0.9822 | | 0.1108 | 22.4098 | 38500 | 0.4100 | 0.8324 | 0.8257 | 0.8291 | 0.9820 | | 0.1103 | 22.7008 | 39000 | 0.4092 | 0.8296 | 0.8160 | 0.8228 | 0.9816 | | 0.1114 | 22.9919 | 39500 | 0.4029 | 0.8322 | 0.8243 | 0.8282 | 0.9821 | | 0.1096 | 23.2829 | 40000 | 0.4051 | 0.8292 | 0.8238 | 0.8265 | 0.9820 | | 0.1088 | 23.5739 | 40500 | 0.4020 | 0.8275 | 0.8195 | 0.8235 | 0.9818 | | 0.1091 | 23.8650 | 41000 | 0.4063 | 0.8256 | 0.8194 | 0.8224 | 0.9815 | | 0.1079 | 24.1560 | 41500 | 0.4015 | 0.8284 | 0.8218 | 0.8251 | 0.9819 | | 0.108 | 24.4470 | 42000 | 0.4058 | 0.8349 | 0.8217 | 0.8282 | 0.9822 | | 0.1081 | 24.7381 | 42500 | 0.4035 | 0.8301 | 0.8207 | 0.8254 | 0.9817 | | 0.1079 | 25.0291 | 43000 | 0.4029 | 0.8306 | 0.8235 | 0.8271 | 0.9820 | | 0.1065 | 25.3201 | 43500 | 0.4053 | 0.8309 | 0.8235 | 0.8272 | 0.9818 | | 0.1062 | 25.6112 | 44000 | 0.4046 | 0.8276 | 0.8188 | 0.8232 | 0.9818 | | 0.1063 | 25.9022 | 44500 | 0.4050 | 0.8323 | 0.8197 | 0.8259 | 0.9820 | | 0.1054 | 26.1932 | 45000 | 0.4000 | 0.8310 | 0.8267 | 0.8289 | 0.9820 | | 0.1048 | 26.4843 | 45500 | 0.4063 | 0.8311 | 0.8227 | 0.8269 | 0.9819 | | 0.105 | 26.7753 | 46000 | 0.3994 | 0.8315 | 0.8233 | 0.8274 | 0.9821 | | 0.104 | 27.0664 | 46500 | 0.4046 | 0.8320 | 0.8198 | 0.8259 | 0.9820 | | 0.1038 | 27.3574 | 47000 | 0.4042 | 0.8312 | 0.8243 | 0.8277 | 0.9820 | | 0.1042 | 27.6484 | 47500 | 0.4006 | 0.8340 | 0.8276 | 0.8308 | 0.9821 | | 0.1041 | 27.9395 | 48000 | 0.4000 | 0.8344 | 0.8244 | 0.8294 | 0.9823 | | 0.1036 | 28.2305 | 48500 | 0.4027 | 0.8325 | 0.8220 | 0.8272 | 0.9819 | | 0.1039 | 28.5215 | 49000 | 0.3972 | 0.8339 | 0.8257 | 0.8298 | 0.9823 | | 0.1023 | 28.8126 | 49500 | 0.3960 | 0.8310 | 0.8267 | 0.8289 | 0.9824 | | 0.103 | 29.1036 | 50000 | 0.4038 | 0.8320 | 0.8188 | 0.8253 | 0.9817 | | 0.102 | 29.3946 | 50500 | 0.4010 | 0.8287 | 0.8211 | 0.8249 | 0.9818 | | 0.1025 | 29.6857 | 51000 | 0.3960 | 0.8361 | 0.8277 | 0.8319 | 0.9823 | | 0.1025 | 29.9767 | 51500 | 0.3977 | 0.8322 | 0.8250 | 0.8286 | 0.9824 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1