--- base_model: haryoaw/scenario-TCR-NER_data-univner_half library_name: transformers license: mit metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: scenario-kd-po-ner-full_data-univner_full66 results: [] --- # scenario-kd-po-ner-full_data-univner_full66 This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4139 - Precision: 0.8074 - Recall: 0.7771 - F1: 0.7919 - Accuracy: 0.9789 ## 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: 66 - 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 | |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.9198 | 0.5828 | 500 | 0.6766 | 0.7412 | 0.7331 | 0.7371 | 0.9745 | | 0.5493 | 1.1655 | 1000 | 0.5975 | 0.7499 | 0.7560 | 0.7529 | 0.9759 | | 0.4453 | 1.7483 | 1500 | 0.5731 | 0.7583 | 0.7585 | 0.7584 | 0.9758 | | 0.3762 | 2.3310 | 2000 | 0.5606 | 0.7824 | 0.7492 | 0.7655 | 0.9764 | | 0.3436 | 2.9138 | 2500 | 0.5208 | 0.7708 | 0.7697 | 0.7703 | 0.9770 | | 0.3082 | 3.4965 | 3000 | 0.5114 | 0.7891 | 0.7491 | 0.7686 | 0.9769 | | 0.2933 | 4.0793 | 3500 | 0.5024 | 0.7873 | 0.7654 | 0.7762 | 0.9774 | | 0.2672 | 4.6620 | 4000 | 0.4971 | 0.7916 | 0.7552 | 0.7729 | 0.9775 | | 0.2525 | 5.2448 | 4500 | 0.4924 | 0.7733 | 0.7775 | 0.7754 | 0.9771 | | 0.2385 | 5.8275 | 5000 | 0.4891 | 0.7833 | 0.7725 | 0.7779 | 0.9775 | | 0.2269 | 6.4103 | 5500 | 0.4843 | 0.7828 | 0.7797 | 0.7813 | 0.9774 | | 0.2215 | 6.9930 | 6000 | 0.4729 | 0.7741 | 0.7862 | 0.7801 | 0.9778 | | 0.2076 | 7.5758 | 6500 | 0.4617 | 0.7838 | 0.7772 | 0.7805 | 0.9780 | | 0.201 | 8.1585 | 7000 | 0.4653 | 0.7975 | 0.7671 | 0.7820 | 0.9779 | | 0.1935 | 8.7413 | 7500 | 0.4574 | 0.7785 | 0.7922 | 0.7853 | 0.9778 | | 0.1869 | 9.3240 | 8000 | 0.4662 | 0.7905 | 0.7821 | 0.7863 | 0.9784 | | 0.1825 | 9.9068 | 8500 | 0.4539 | 0.7883 | 0.7807 | 0.7845 | 0.9782 | | 0.1748 | 10.4895 | 9000 | 0.4486 | 0.7975 | 0.7852 | 0.7913 | 0.9789 | | 0.1714 | 11.0723 | 9500 | 0.4499 | 0.7975 | 0.7829 | 0.7901 | 0.9787 | | 0.166 | 11.6550 | 10000 | 0.4429 | 0.7931 | 0.7852 | 0.7891 | 0.9787 | | 0.1612 | 12.2378 | 10500 | 0.4427 | 0.7913 | 0.7788 | 0.7850 | 0.9782 | | 0.1567 | 12.8205 | 11000 | 0.4413 | 0.8024 | 0.7762 | 0.7891 | 0.9786 | | 0.1544 | 13.4033 | 11500 | 0.4421 | 0.8068 | 0.7628 | 0.7842 | 0.9781 | | 0.1502 | 13.9860 | 12000 | 0.4388 | 0.8009 | 0.7843 | 0.7925 | 0.9788 | | 0.146 | 14.5688 | 12500 | 0.4295 | 0.8 | 0.7768 | 0.7882 | 0.9786 | | 0.1434 | 15.1515 | 13000 | 0.4402 | 0.8057 | 0.7755 | 0.7903 | 0.9784 | | 0.1404 | 15.7343 | 13500 | 0.4352 | 0.8106 | 0.7713 | 0.7905 | 0.9785 | | 0.1387 | 16.3170 | 14000 | 0.4360 | 0.7981 | 0.7729 | 0.7853 | 0.9783 | | 0.1356 | 16.8998 | 14500 | 0.4328 | 0.8071 | 0.7722 | 0.7893 | 0.9786 | | 0.1345 | 17.4825 | 15000 | 0.4278 | 0.7990 | 0.7736 | 0.7861 | 0.9786 | | 0.1313 | 18.0653 | 15500 | 0.4268 | 0.7985 | 0.7868 | 0.7926 | 0.9789 | | 0.1282 | 18.6480 | 16000 | 0.4219 | 0.7983 | 0.7818 | 0.7900 | 0.9789 | | 0.1284 | 19.2308 | 16500 | 0.4313 | 0.7968 | 0.7729 | 0.7847 | 0.9782 | | 0.1242 | 19.8135 | 17000 | 0.4255 | 0.8103 | 0.7803 | 0.7950 | 0.9790 | | 0.1239 | 20.3963 | 17500 | 0.4315 | 0.8060 | 0.7720 | 0.7887 | 0.9786 | | 0.124 | 20.9790 | 18000 | 0.4317 | 0.8117 | 0.7663 | 0.7883 | 0.9782 | | 0.1219 | 21.5618 | 18500 | 0.4198 | 0.7959 | 0.7758 | 0.7857 | 0.9783 | | 0.1199 | 22.1445 | 19000 | 0.4257 | 0.7976 | 0.7795 | 0.7885 | 0.9784 | | 0.1184 | 22.7273 | 19500 | 0.4271 | 0.8095 | 0.7664 | 0.7874 | 0.9784 | | 0.118 | 23.3100 | 20000 | 0.4169 | 0.8076 | 0.7769 | 0.7920 | 0.9789 | | 0.1176 | 23.8928 | 20500 | 0.4203 | 0.8069 | 0.7769 | 0.7916 | 0.9786 | | 0.1152 | 24.4755 | 21000 | 0.4180 | 0.8056 | 0.7816 | 0.7934 | 0.9790 | | 0.115 | 25.0583 | 21500 | 0.4206 | 0.8082 | 0.7765 | 0.7921 | 0.9791 | | 0.1126 | 25.6410 | 22000 | 0.4196 | 0.8047 | 0.7762 | 0.7902 | 0.9787 | | 0.1148 | 26.2238 | 22500 | 0.4176 | 0.8061 | 0.7820 | 0.7938 | 0.9789 | | 0.1123 | 26.8065 | 23000 | 0.4156 | 0.8086 | 0.7826 | 0.7954 | 0.9791 | | 0.1108 | 27.3893 | 23500 | 0.4133 | 0.8089 | 0.7829 | 0.7957 | 0.9792 | | 0.111 | 27.9720 | 24000 | 0.4114 | 0.8021 | 0.7768 | 0.7893 | 0.9790 | | 0.1099 | 28.5548 | 24500 | 0.4159 | 0.8066 | 0.7739 | 0.7899 | 0.9786 | | 0.1112 | 29.1375 | 25000 | 0.4151 | 0.8082 | 0.7804 | 0.7940 | 0.9789 | | 0.1091 | 29.7203 | 25500 | 0.4139 | 0.8074 | 0.7771 | 0.7919 | 0.9789 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1