--- 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-mdeberta_data-univner_full66 results: [] --- # scenario-kd-po-ner-full-mdeberta_data-univner_full66 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: 46.8943 - Precision: 0.8216 - Recall: 0.8305 - F1: 0.8260 - 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: 8 - eval_batch_size: 32 - seed: 66 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 135.9225 | 0.2911 | 500 | 108.8223 | 0.6029 | 0.4263 | 0.4995 | 0.9530 | | 101.0224 | 0.5822 | 1000 | 94.7779 | 0.7231 | 0.7083 | 0.7156 | 0.9727 | | 91.1928 | 0.8732 | 1500 | 88.1561 | 0.7488 | 0.7565 | 0.7526 | 0.9760 | | 85.2395 | 1.1643 | 2000 | 83.2027 | 0.7757 | 0.7680 | 0.7718 | 0.9777 | | 80.2907 | 1.4554 | 2500 | 79.1339 | 0.7824 | 0.7925 | 0.7874 | 0.9788 | | 76.5231 | 1.7465 | 3000 | 75.6774 | 0.7942 | 0.7846 | 0.7894 | 0.9793 | | 73.1464 | 2.0375 | 3500 | 72.6010 | 0.8048 | 0.7973 | 0.8010 | 0.9801 | | 69.6437 | 2.3286 | 4000 | 69.6290 | 0.7955 | 0.8179 | 0.8066 | 0.9803 | | 67.0793 | 2.6197 | 4500 | 67.2226 | 0.8016 | 0.8085 | 0.8051 | 0.9808 | | 64.9103 | 2.9108 | 5000 | 65.1388 | 0.8012 | 0.8176 | 0.8093 | 0.9807 | | 62.5177 | 3.2019 | 5500 | 63.1765 | 0.8105 | 0.8160 | 0.8133 | 0.9813 | | 60.6079 | 3.4929 | 6000 | 61.4149 | 0.8158 | 0.8129 | 0.8143 | 0.9811 | | 58.9252 | 3.7840 | 6500 | 59.9050 | 0.8118 | 0.8212 | 0.8165 | 0.9810 | | 57.4544 | 4.0751 | 7000 | 58.3757 | 0.8063 | 0.8260 | 0.8161 | 0.9813 | | 55.9212 | 4.3662 | 7500 | 57.1185 | 0.8129 | 0.8254 | 0.8191 | 0.9815 | | 54.706 | 4.6573 | 8000 | 55.9905 | 0.8208 | 0.8197 | 0.8202 | 0.9818 | | 53.5567 | 4.9483 | 8500 | 54.9749 | 0.8117 | 0.8259 | 0.8187 | 0.9813 | | 52.4084 | 5.2394 | 9000 | 53.9236 | 0.8158 | 0.8228 | 0.8193 | 0.9815 | | 51.3684 | 5.5305 | 9500 | 52.9420 | 0.8148 | 0.8263 | 0.8205 | 0.9817 | | 50.5374 | 5.8216 | 10000 | 52.1205 | 0.8224 | 0.8209 | 0.8217 | 0.9819 | | 49.7012 | 6.1126 | 10500 | 51.3587 | 0.8195 | 0.8310 | 0.8252 | 0.9820 | | 48.8997 | 6.4037 | 11000 | 50.7199 | 0.8205 | 0.8270 | 0.8237 | 0.9819 | | 48.3307 | 6.6948 | 11500 | 50.0936 | 0.8238 | 0.8215 | 0.8227 | 0.9821 | | 47.765 | 6.9859 | 12000 | 49.5167 | 0.8177 | 0.8318 | 0.8247 | 0.9819 | | 47.1176 | 7.2770 | 12500 | 49.0615 | 0.8195 | 0.8305 | 0.8249 | 0.9821 | | 46.5727 | 7.5680 | 13000 | 48.6345 | 0.8176 | 0.8347 | 0.8260 | 0.9820 | | 46.2968 | 7.8591 | 13500 | 48.2124 | 0.8193 | 0.8282 | 0.8237 | 0.9821 | | 45.8193 | 8.1502 | 14000 | 47.8940 | 0.8236 | 0.8285 | 0.8260 | 0.9821 | | 45.4871 | 8.4413 | 14500 | 47.5967 | 0.8171 | 0.8362 | 0.8266 | 0.9819 | | 45.2671 | 8.7324 | 15000 | 47.3633 | 0.8252 | 0.8329 | 0.8290 | 0.9824 | | 45.0471 | 9.0234 | 15500 | 47.1393 | 0.8245 | 0.8280 | 0.8262 | 0.9821 | | 44.7971 | 9.3145 | 16000 | 47.0470 | 0.8234 | 0.8315 | 0.8274 | 0.9822 | | 44.7601 | 9.6056 | 16500 | 46.9315 | 0.8223 | 0.8331 | 0.8276 | 0.9825 | | 44.647 | 9.8967 | 17000 | 46.8943 | 0.8216 | 0.8305 | 0.8260 | 0.9824 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1