--- license: apache-2.0 base_model: google/electra-base-discriminator tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: electra-base-discriminator-finetuned-ner-cadec-no-iob results: [] --- # electra-base-discriminator-finetuned-ner-cadec-no-iob This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3907 - Precision: 0.6459 - Recall: 0.6715 - F1: 0.6585 - Accuracy: 0.9355 - Adr Precision: 0.5961 - Adr Recall: 0.6330 - Adr F1: 0.614 - Disease Precision: 0.4615 - Disease Recall: 0.375 - Disease F1: 0.4138 - Drug Precision: 0.9022 - Drug Recall: 0.9222 - Drug F1: 0.9121 - Finding Precision: 0.2571 - Finding Recall: 0.2812 - Finding F1: 0.2687 - Symptom Precision: 0.5357 - Symptom Recall: 0.5172 - Symptom F1: 0.5263 - Macro Avg F1: 0.5470 - Weighted Avg F1: 0.6584 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Adr Precision | Adr Recall | Adr F1 | Disease Precision | Disease Recall | Disease F1 | Drug Precision | Drug Recall | Drug F1 | Finding Precision | Finding Recall | Finding F1 | Symptom Precision | Symptom Recall | Symptom F1 | Macro Avg F1 | Weighted Avg F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:------------:|:---------------:| | No log | 1.0 | 125 | 0.2178 | 0.5120 | 0.5646 | 0.5370 | 0.9209 | 0.4311 | 0.5608 | 0.4875 | 0.1 | 0.0312 | 0.0476 | 0.8158 | 0.8611 | 0.8378 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2746 | 0.5129 | | No log | 2.0 | 250 | 0.1864 | 0.5799 | 0.6227 | 0.6005 | 0.9296 | 0.5017 | 0.6 | 0.5465 | 0.4146 | 0.5312 | 0.4658 | 0.9011 | 0.9111 | 0.9061 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3837 | 0.5845 | | No log | 3.0 | 375 | 0.1845 | 0.5894 | 0.6306 | 0.6093 | 0.9310 | 0.5520 | 0.6021 | 0.5759 | 0.2353 | 0.375 | 0.2892 | 0.9016 | 0.9167 | 0.9091 | 0.0857 | 0.0938 | 0.0896 | 0.4615 | 0.2069 | 0.2857 | 0.4299 | 0.6113 | | 0.2159 | 4.0 | 500 | 0.1968 | 0.6002 | 0.6359 | 0.6176 | 0.9318 | 0.5570 | 0.6041 | 0.5796 | 0.0667 | 0.0312 | 0.0426 | 0.9121 | 0.9222 | 0.9171 | 0.1967 | 0.375 | 0.2581 | 0.5263 | 0.3448 | 0.4167 | 0.4428 | 0.6173 | | 0.2159 | 5.0 | 625 | 0.2026 | 0.6357 | 0.6583 | 0.6468 | 0.9338 | 0.5817 | 0.6165 | 0.5986 | 0.52 | 0.4062 | 0.4561 | 0.9071 | 0.9222 | 0.9146 | 0.1951 | 0.25 | 0.2192 | 0.5909 | 0.4483 | 0.5098 | 0.5397 | 0.6482 | | 0.2159 | 6.0 | 750 | 0.2142 | 0.6055 | 0.6359 | 0.6203 | 0.9319 | 0.5697 | 0.5814 | 0.5755 | 0.375 | 0.4688 | 0.4167 | 0.9066 | 0.9167 | 0.9116 | 0.1163 | 0.1562 | 0.1333 | 0.4167 | 0.5172 | 0.4615 | 0.4997 | 0.6256 | | 0.2159 | 7.0 | 875 | 0.2283 | 0.6420 | 0.6649 | 0.6533 | 0.9328 | 0.5867 | 0.6206 | 0.6032 | 0.5 | 0.3125 | 0.3846 | 0.9076 | 0.9278 | 0.9176 | 0.2444 | 0.3438 | 0.2857 | 0.6522 | 0.5172 | 0.5769 | 0.5536 | 0.6542 | | 0.0651 | 8.0 | 1000 | 0.2430 | 0.6103 | 0.6570 | 0.6328 | 0.9312 | 0.5632 | 0.6062 | 0.5839 | 0.5625 | 0.2812 | 0.375 | 0.9121 | 0.9222 | 0.9171 | 0.2241 | 0.4062 | 0.2889 | 0.4211 | 0.5517 | 0.4776 | 0.5285 | 0.6377 | | 0.0651 | 9.0 | 1125 | 0.2367 | 0.6308 | 0.6583 | 0.6443 | 0.9338 | 0.5800 | 0.6206 | 0.5996 | 0.48 | 0.375 | 0.4211 | 0.8811 | 0.9056 | 0.8932 | 0.2286 | 0.25 | 0.2388 | 0.5556 | 0.5172 | 0.5357 | 0.5377 | 0.6441 | | 0.0651 | 10.0 | 1250 | 0.2694 | 0.6241 | 0.6570 | 0.6401 | 0.9294 | 0.5639 | 0.6186 | 0.5900 | 0.4286 | 0.2812 | 0.3396 | 0.9171 | 0.9222 | 0.9197 | 0.2222 | 0.3125 | 0.2597 | 0.6842 | 0.4483 | 0.5417 | 0.5301 | 0.6419 | | 0.0651 | 11.0 | 1375 | 0.2731 | 0.61 | 0.6438 | 0.6264 | 0.9325 | 0.5626 | 0.6021 | 0.5817 | 0.4286 | 0.375 | 0.4000 | 0.8511 | 0.8889 | 0.8696 | 0.2812 | 0.2812 | 0.2812 | 0.4545 | 0.5172 | 0.4839 | 0.5233 | 0.6259 | | 0.0265 | 12.0 | 1500 | 0.2842 | 0.6221 | 0.6451 | 0.6334 | 0.9323 | 0.5697 | 0.5979 | 0.5835 | 0.3929 | 0.3438 | 0.3667 | 0.9027 | 0.9278 | 0.9151 | 0.2121 | 0.2188 | 0.2154 | 0.4516 | 0.4828 | 0.4667 | 0.5095 | 0.6331 | | 0.0265 | 13.0 | 1625 | 0.2861 | 0.6438 | 0.6675 | 0.6554 | 0.9348 | 0.5802 | 0.6268 | 0.6026 | 0.5 | 0.4375 | 0.4667 | 0.9278 | 0.9278 | 0.9278 | 0.3 | 0.2812 | 0.2903 | 0.5 | 0.4138 | 0.4528 | 0.5480 | 0.6552 | | 0.0265 | 14.0 | 1750 | 0.3202 | 0.6413 | 0.6557 | 0.6484 | 0.9323 | 0.5742 | 0.6144 | 0.5936 | 0.5263 | 0.3125 | 0.3922 | 0.9071 | 0.9222 | 0.9146 | 0.2647 | 0.2812 | 0.2727 | 0.7 | 0.4828 | 0.5714 | 0.5489 | 0.6469 | | 0.0265 | 15.0 | 1875 | 0.3118 | 0.6284 | 0.6491 | 0.6385 | 0.9336 | 0.5731 | 0.6062 | 0.5892 | 0.4348 | 0.3125 | 0.3636 | 0.9066 | 0.9167 | 0.9116 | 0.25 | 0.3125 | 0.2778 | 0.52 | 0.4483 | 0.4815 | 0.5247 | 0.6390 | | 0.0115 | 16.0 | 2000 | 0.3144 | 0.6293 | 0.6583 | 0.6435 | 0.9335 | 0.5747 | 0.6268 | 0.5996 | 0.3846 | 0.3125 | 0.3448 | 0.9061 | 0.9111 | 0.9086 | 0.2286 | 0.25 | 0.2388 | 0.5909 | 0.4483 | 0.5098 | 0.5203 | 0.6436 | | 0.0115 | 17.0 | 2125 | 0.3235 | 0.6411 | 0.6623 | 0.6515 | 0.9346 | 0.5918 | 0.6247 | 0.6078 | 0.4 | 0.375 | 0.3871 | 0.9171 | 0.9222 | 0.9197 | 0.2121 | 0.2188 | 0.2154 | 0.5185 | 0.4828 | 0.5 | 0.5260 | 0.6519 | | 0.0115 | 18.0 | 2250 | 0.3258 | 0.6230 | 0.6583 | 0.6402 | 0.9344 | 0.5948 | 0.6144 | 0.6045 | 0.3429 | 0.375 | 0.3582 | 0.8913 | 0.9111 | 0.9011 | 0.1957 | 0.2812 | 0.2308 | 0.4571 | 0.5517 | 0.5 | 0.5189 | 0.6447 | | 0.0115 | 19.0 | 2375 | 0.3347 | 0.6312 | 0.6728 | 0.6513 | 0.9361 | 0.5794 | 0.6392 | 0.6078 | 0.4348 | 0.3125 | 0.3636 | 0.9022 | 0.9222 | 0.9121 | 0.2564 | 0.3125 | 0.2817 | 0.5185 | 0.4828 | 0.5 | 0.5331 | 0.6519 | | 0.0076 | 20.0 | 2500 | 0.3334 | 0.6508 | 0.6662 | 0.6584 | 0.9328 | 0.6031 | 0.6330 | 0.6177 | 0.375 | 0.375 | 0.375 | 0.9231 | 0.9333 | 0.9282 | 0.1765 | 0.1875 | 0.1818 | 0.6316 | 0.4138 | 0.5 | 0.5205 | 0.6583 | | 0.0076 | 21.0 | 2625 | 0.3426 | 0.6385 | 0.6781 | 0.6577 | 0.9344 | 0.5943 | 0.6495 | 0.6207 | 0.4231 | 0.3438 | 0.3793 | 0.9071 | 0.9222 | 0.9146 | 0.1765 | 0.1875 | 0.1818 | 0.5 | 0.5517 | 0.5246 | 0.5242 | 0.6581 | | 0.0076 | 22.0 | 2750 | 0.3431 | 0.6269 | 0.6649 | 0.6453 | 0.9344 | 0.5862 | 0.6309 | 0.6077 | 0.3793 | 0.3438 | 0.3607 | 0.9071 | 0.9222 | 0.9146 | 0.1707 | 0.2188 | 0.1918 | 0.4828 | 0.4828 | 0.4828 | 0.5115 | 0.6478 | | 0.0076 | 23.0 | 2875 | 0.3374 | 0.6410 | 0.6689 | 0.6546 | 0.9348 | 0.5913 | 0.6412 | 0.6152 | 0.3913 | 0.2812 | 0.3273 | 0.9180 | 0.9333 | 0.9256 | 0.2368 | 0.2812 | 0.2571 | 0.4762 | 0.3448 | 0.4000 | 0.5051 | 0.6534 | | 0.0049 | 24.0 | 3000 | 0.3484 | 0.6338 | 0.6530 | 0.6433 | 0.9352 | 0.5819 | 0.6082 | 0.5948 | 0.4 | 0.375 | 0.3871 | 0.9126 | 0.9278 | 0.9201 | 0.2258 | 0.2188 | 0.2222 | 0.4667 | 0.4828 | 0.4746 | 0.5198 | 0.6429 | | 0.0049 | 25.0 | 3125 | 0.3441 | 0.6444 | 0.6741 | 0.6589 | 0.9362 | 0.6015 | 0.6412 | 0.6208 | 0.4194 | 0.4062 | 0.4127 | 0.9076 | 0.9278 | 0.9176 | 0.1935 | 0.1875 | 0.1905 | 0.4667 | 0.4828 | 0.4746 | 0.5232 | 0.6587 | | 0.0049 | 26.0 | 3250 | 0.3573 | 0.6330 | 0.6689 | 0.6504 | 0.9347 | 0.5802 | 0.6268 | 0.6026 | 0.4138 | 0.375 | 0.3934 | 0.9235 | 0.9389 | 0.9311 | 0.2286 | 0.25 | 0.2388 | 0.4667 | 0.4828 | 0.4746 | 0.5281 | 0.6515 | | 0.0049 | 27.0 | 3375 | 0.3589 | 0.6477 | 0.6596 | 0.6536 | 0.9336 | 0.5909 | 0.6165 | 0.6034 | 0.48 | 0.375 | 0.4211 | 0.9126 | 0.9278 | 0.9201 | 0.25 | 0.2812 | 0.2647 | 0.5909 | 0.4483 | 0.5098 | 0.5438 | 0.6531 | | 0.0037 | 28.0 | 3500 | 0.3629 | 0.6307 | 0.6715 | 0.6505 | 0.9354 | 0.5900 | 0.6351 | 0.6117 | 0.4444 | 0.375 | 0.4068 | 0.9016 | 0.9167 | 0.9091 | 0.2326 | 0.3125 | 0.2667 | 0.4375 | 0.4828 | 0.4590 | 0.5307 | 0.6533 | | 0.0037 | 29.0 | 3625 | 0.3694 | 0.6490 | 0.6781 | 0.6632 | 0.9358 | 0.6035 | 0.6433 | 0.6228 | 0.4286 | 0.375 | 0.4000 | 0.9071 | 0.9222 | 0.9146 | 0.2105 | 0.25 | 0.2286 | 0.6154 | 0.5517 | 0.5818 | 0.5495 | 0.6644 | | 0.0037 | 30.0 | 3750 | 0.3779 | 0.6456 | 0.6728 | 0.6589 | 0.9350 | 0.5946 | 0.6351 | 0.6142 | 0.5 | 0.375 | 0.4286 | 0.9071 | 0.9222 | 0.9146 | 0.2647 | 0.2812 | 0.2727 | 0.4839 | 0.5172 | 0.5000 | 0.5460 | 0.6589 | | 0.0037 | 31.0 | 3875 | 0.3776 | 0.6465 | 0.6636 | 0.6549 | 0.9360 | 0.5992 | 0.6227 | 0.6107 | 0.4615 | 0.375 | 0.4138 | 0.9022 | 0.9222 | 0.9121 | 0.2424 | 0.25 | 0.2462 | 0.4839 | 0.5172 | 0.5000 | 0.5366 | 0.6543 | | 0.0027 | 32.0 | 4000 | 0.3775 | 0.6429 | 0.6675 | 0.6550 | 0.9355 | 0.5984 | 0.6268 | 0.6123 | 0.4444 | 0.375 | 0.4068 | 0.9022 | 0.9222 | 0.9121 | 0.2368 | 0.2812 | 0.2571 | 0.5 | 0.5172 | 0.5085 | 0.5394 | 0.6558 | | 0.0027 | 33.0 | 4125 | 0.3784 | 0.6378 | 0.6715 | 0.6542 | 0.9363 | 0.5881 | 0.6330 | 0.6097 | 0.4783 | 0.3438 | 0.4 | 0.9022 | 0.9222 | 0.9121 | 0.2381 | 0.3125 | 0.2703 | 0.5556 | 0.5172 | 0.5357 | 0.5456 | 0.6555 | | 0.0027 | 34.0 | 4250 | 0.3824 | 0.6332 | 0.6741 | 0.6530 | 0.9356 | 0.5830 | 0.6371 | 0.6089 | 0.4286 | 0.375 | 0.4000 | 0.9022 | 0.9222 | 0.9121 | 0.2432 | 0.2812 | 0.2609 | 0.5357 | 0.5172 | 0.5263 | 0.5416 | 0.6542 | | 0.0027 | 35.0 | 4375 | 0.3825 | 0.6415 | 0.6728 | 0.6568 | 0.9358 | 0.5954 | 0.6371 | 0.6155 | 0.4444 | 0.375 | 0.4068 | 0.9022 | 0.9222 | 0.9121 | 0.225 | 0.2812 | 0.25 | 0.56 | 0.4828 | 0.5185 | 0.5406 | 0.6580 | | 0.0018 | 36.0 | 4500 | 0.3841 | 0.6390 | 0.6794 | 0.6586 | 0.9364 | 0.5928 | 0.6454 | 0.6180 | 0.4444 | 0.375 | 0.4068 | 0.9022 | 0.9222 | 0.9121 | 0.2368 | 0.2812 | 0.2571 | 0.5172 | 0.5172 | 0.5172 | 0.5422 | 0.6598 | | 0.0018 | 37.0 | 4625 | 0.3892 | 0.6477 | 0.6741 | 0.6606 | 0.9361 | 0.6008 | 0.6392 | 0.6194 | 0.4286 | 0.375 | 0.4000 | 0.9022 | 0.9222 | 0.9121 | 0.2571 | 0.2812 | 0.2687 | 0.5385 | 0.4828 | 0.5091 | 0.5418 | 0.6606 | | 0.0018 | 38.0 | 4750 | 0.3893 | 0.6480 | 0.6702 | 0.6589 | 0.9363 | 0.5988 | 0.6309 | 0.6145 | 0.4444 | 0.375 | 0.4068 | 0.9022 | 0.9222 | 0.9121 | 0.25 | 0.2812 | 0.2647 | 0.5769 | 0.5172 | 0.5455 | 0.5487 | 0.6590 | | 0.0018 | 39.0 | 4875 | 0.3911 | 0.6422 | 0.6702 | 0.6559 | 0.9354 | 0.5907 | 0.6309 | 0.6102 | 0.4615 | 0.375 | 0.4138 | 0.9022 | 0.9222 | 0.9121 | 0.2571 | 0.2812 | 0.2687 | 0.5357 | 0.5172 | 0.5263 | 0.5462 | 0.6559 | | 0.0016 | 40.0 | 5000 | 0.3907 | 0.6459 | 0.6715 | 0.6585 | 0.9355 | 0.5961 | 0.6330 | 0.614 | 0.4615 | 0.375 | 0.4138 | 0.9022 | 0.9222 | 0.9121 | 0.2571 | 0.2812 | 0.2687 | 0.5357 | 0.5172 | 0.5263 | 0.5470 | 0.6584 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0