--- library_name: transformers license: mit base_model: haryoaw/scenario-TCR-NER_data-univner_full tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: scenario-kd-scr-ner-full-xlmr_data-univner_full44 results: [] --- # scenario-kd-scr-ner-full-xlmr_data-univner_full44 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: 1.3300 - Precision: 0.4985 - Recall: 0.4665 - F1: 0.4819 - Accuracy: 0.9525 ## 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: 44 - 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 | |:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 3.061 | 0.2911 | 500 | 2.5055 | 0.3093 | 0.0105 | 0.0204 | 0.9242 | | 2.362 | 0.5822 | 1000 | 2.2363 | 0.2161 | 0.1544 | 0.1801 | 0.9254 | | 2.1193 | 0.8732 | 1500 | 2.0821 | 0.2713 | 0.1587 | 0.2003 | 0.9303 | | 1.9507 | 1.1643 | 2000 | 1.9867 | 0.3366 | 0.1861 | 0.2397 | 0.9334 | | 1.8372 | 1.4554 | 2500 | 1.9044 | 0.3094 | 0.2479 | 0.2753 | 0.9344 | | 1.7489 | 1.7465 | 3000 | 1.8270 | 0.3812 | 0.2516 | 0.3031 | 0.9379 | | 1.6638 | 2.0375 | 3500 | 1.7473 | 0.3617 | 0.2910 | 0.3225 | 0.9391 | | 1.5358 | 2.3286 | 4000 | 1.7031 | 0.3968 | 0.3004 | 0.3419 | 0.9404 | | 1.5116 | 2.6197 | 4500 | 1.6644 | 0.3930 | 0.3282 | 0.3577 | 0.9414 | | 1.4648 | 2.9108 | 5000 | 1.6152 | 0.4069 | 0.3223 | 0.3597 | 0.9428 | | 1.3588 | 3.2019 | 5500 | 1.5809 | 0.4327 | 0.3487 | 0.3862 | 0.9440 | | 1.322 | 3.4929 | 6000 | 1.5435 | 0.4202 | 0.3956 | 0.4076 | 0.9435 | | 1.2743 | 3.7840 | 6500 | 1.5010 | 0.4373 | 0.3922 | 0.4135 | 0.9463 | | 1.2353 | 4.0751 | 7000 | 1.5160 | 0.4283 | 0.3868 | 0.4065 | 0.9458 | | 1.1701 | 4.3662 | 7500 | 1.4797 | 0.4519 | 0.3956 | 0.4219 | 0.9470 | | 1.1448 | 4.6573 | 8000 | 1.4640 | 0.4622 | 0.4053 | 0.4319 | 0.9473 | | 1.1258 | 4.9483 | 8500 | 1.4496 | 0.4240 | 0.4115 | 0.4177 | 0.9466 | | 1.0504 | 5.2394 | 9000 | 1.4419 | 0.4783 | 0.4160 | 0.4449 | 0.9486 | | 1.0311 | 5.5305 | 9500 | 1.4270 | 0.4562 | 0.4122 | 0.4331 | 0.9479 | | 1.0299 | 5.8216 | 10000 | 1.3948 | 0.4564 | 0.4294 | 0.4425 | 0.9497 | | 1.0016 | 6.1126 | 10500 | 1.4108 | 0.4675 | 0.4333 | 0.4498 | 0.9495 | | 0.9546 | 6.4037 | 11000 | 1.3817 | 0.4850 | 0.4418 | 0.4624 | 0.9508 | | 0.9384 | 6.6948 | 11500 | 1.3723 | 0.4672 | 0.4425 | 0.4545 | 0.9508 | | 0.9259 | 6.9859 | 12000 | 1.3574 | 0.4837 | 0.4398 | 0.4607 | 0.9508 | | 0.8941 | 7.2770 | 12500 | 1.3871 | 0.5094 | 0.4393 | 0.4718 | 0.9514 | | 0.8827 | 7.5680 | 13000 | 1.3698 | 0.4918 | 0.4262 | 0.4567 | 0.9514 | | 0.8776 | 7.8591 | 13500 | 1.3510 | 0.4885 | 0.4467 | 0.4667 | 0.9517 | | 0.8552 | 8.1502 | 14000 | 1.3549 | 0.4970 | 0.4530 | 0.4740 | 0.9519 | | 0.8392 | 8.4413 | 14500 | 1.3413 | 0.4934 | 0.4565 | 0.4742 | 0.9521 | | 0.8463 | 8.7324 | 15000 | 1.3523 | 0.5150 | 0.4409 | 0.4751 | 0.9521 | | 0.8301 | 9.0234 | 15500 | 1.3371 | 0.4934 | 0.4701 | 0.4815 | 0.9521 | | 0.8104 | 9.3145 | 16000 | 1.3385 | 0.4966 | 0.4667 | 0.4812 | 0.9527 | | 0.807 | 9.6056 | 16500 | 1.3347 | 0.4969 | 0.4615 | 0.4786 | 0.9522 | | 0.8125 | 9.8967 | 17000 | 1.3300 | 0.4985 | 0.4665 | 0.4819 | 0.9525 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1