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---
library_name: peft
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-ner-qlorafinetune-runs-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-ner-qlorafinetune-runs-colab
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0806
- Precision: 0.9518
- Recall: 0.9720
- F1: 0.9618
- Accuracy: 0.9821
## 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: 0.0004
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 2135
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.0785 | 0.0654 | 20 | 1.0950 | 0.5789 | 0.0911 | 0.1574 | 0.7236 |
| 0.8123 | 0.1307 | 40 | 0.3799 | 0.8101 | 0.7616 | 0.7851 | 0.9053 |
| 0.4216 | 0.1961 | 60 | 0.2338 | 0.8720 | 0.8610 | 0.8665 | 0.9379 |
| 0.2799 | 0.2614 | 80 | 0.2287 | 0.8044 | 0.9026 | 0.8507 | 0.9318 |
| 0.233 | 0.3268 | 100 | 0.1659 | 0.8651 | 0.9243 | 0.8937 | 0.9526 |
| 0.2401 | 0.3922 | 120 | 0.1523 | 0.8654 | 0.9393 | 0.9009 | 0.9566 |
| 0.1876 | 0.4575 | 140 | 0.1301 | 0.9080 | 0.9384 | 0.9230 | 0.9612 |
| 0.1605 | 0.5229 | 160 | 0.1346 | 0.8872 | 0.9450 | 0.9152 | 0.9627 |
| 0.162 | 0.5882 | 180 | 0.1420 | 0.8839 | 0.9584 | 0.9197 | 0.9602 |
| 0.1422 | 0.6536 | 200 | 0.1108 | 0.9086 | 0.9521 | 0.9298 | 0.9679 |
| 0.1351 | 0.7190 | 220 | 0.1054 | 0.9222 | 0.9376 | 0.9298 | 0.9681 |
| 0.141 | 0.7843 | 240 | 0.1079 | 0.9177 | 0.9615 | 0.9391 | 0.9704 |
| 0.125 | 0.8497 | 260 | 0.1069 | 0.9229 | 0.9579 | 0.9401 | 0.9711 |
| 0.1344 | 0.9150 | 280 | 0.1069 | 0.9193 | 0.9558 | 0.9372 | 0.9709 |
| 0.1382 | 0.9804 | 300 | 0.0991 | 0.9320 | 0.9515 | 0.9416 | 0.9733 |
| 0.1139 | 1.0458 | 320 | 0.0962 | 0.9274 | 0.9604 | 0.9436 | 0.9733 |
| 0.0995 | 1.1111 | 340 | 0.0997 | 0.9339 | 0.9580 | 0.9458 | 0.9719 |
| 0.1086 | 1.1765 | 360 | 0.1040 | 0.9301 | 0.9584 | 0.9440 | 0.9715 |
| 0.1065 | 1.2418 | 380 | 0.0938 | 0.9407 | 0.9620 | 0.9512 | 0.9763 |
| 0.1231 | 1.3072 | 400 | 0.0893 | 0.9365 | 0.9609 | 0.9486 | 0.9754 |
| 0.1006 | 1.3725 | 420 | 0.1003 | 0.9170 | 0.9536 | 0.9349 | 0.9704 |
| 0.1056 | 1.4379 | 440 | 0.0837 | 0.9474 | 0.9580 | 0.9527 | 0.9776 |
| 0.0924 | 1.5033 | 460 | 0.0824 | 0.9411 | 0.9643 | 0.9526 | 0.9770 |
| 0.097 | 1.5686 | 480 | 0.0887 | 0.9335 | 0.9711 | 0.9519 | 0.9759 |
| 0.0928 | 1.6340 | 500 | 0.0886 | 0.9358 | 0.9669 | 0.9511 | 0.9760 |
| 0.0803 | 1.6993 | 520 | 0.0947 | 0.9220 | 0.9704 | 0.9456 | 0.9726 |
| 0.0908 | 1.7647 | 540 | 0.0833 | 0.9401 | 0.9616 | 0.9507 | 0.9777 |
| 0.0956 | 1.8301 | 560 | 0.1060 | 0.9161 | 0.9749 | 0.9446 | 0.9721 |
| 0.1087 | 1.8954 | 580 | 0.0904 | 0.9378 | 0.9620 | 0.9497 | 0.9748 |
| 0.0979 | 1.9608 | 600 | 0.0940 | 0.9293 | 0.9627 | 0.9457 | 0.9739 |
| 0.0828 | 2.0261 | 620 | 0.0874 | 0.9401 | 0.9621 | 0.9509 | 0.9760 |
| 0.0759 | 2.0915 | 640 | 0.0822 | 0.9401 | 0.9707 | 0.9552 | 0.9787 |
| 0.0597 | 2.1569 | 660 | 0.0838 | 0.9468 | 0.9605 | 0.9536 | 0.9780 |
| 0.0804 | 2.2222 | 680 | 0.0800 | 0.9447 | 0.9680 | 0.9562 | 0.9788 |
| 0.0821 | 2.2876 | 700 | 0.0883 | 0.9315 | 0.9669 | 0.9489 | 0.9759 |
| 0.0634 | 2.3529 | 720 | 0.0771 | 0.9494 | 0.9710 | 0.9601 | 0.9805 |
| 0.0723 | 2.4183 | 740 | 0.0753 | 0.9443 | 0.9705 | 0.9573 | 0.9799 |
| 0.093 | 2.4837 | 760 | 0.0762 | 0.9481 | 0.9705 | 0.9591 | 0.9805 |
| 0.0715 | 2.5490 | 780 | 0.0804 | 0.9487 | 0.9691 | 0.9588 | 0.9806 |
| 0.0688 | 2.6144 | 800 | 0.0783 | 0.9479 | 0.9697 | 0.9586 | 0.9803 |
| 0.0563 | 2.6797 | 820 | 0.0874 | 0.9469 | 0.9683 | 0.9575 | 0.9786 |
| 0.071 | 2.7451 | 840 | 0.0830 | 0.9363 | 0.9667 | 0.9513 | 0.9771 |
| 0.0681 | 2.8105 | 860 | 0.0833 | 0.9430 | 0.9736 | 0.9581 | 0.9791 |
| 0.074 | 2.8758 | 880 | 0.0809 | 0.9430 | 0.9705 | 0.9565 | 0.9785 |
| 0.0662 | 2.9412 | 900 | 0.0928 | 0.9283 | 0.9709 | 0.9491 | 0.9752 |
| 0.0718 | 3.0065 | 920 | 0.0771 | 0.9453 | 0.9753 | 0.9601 | 0.9809 |
| 0.0497 | 3.0719 | 940 | 0.0756 | 0.9487 | 0.9703 | 0.9594 | 0.9807 |
| 0.0559 | 3.1373 | 960 | 0.0887 | 0.9435 | 0.9662 | 0.9547 | 0.9772 |
| 0.0481 | 3.2026 | 980 | 0.0828 | 0.9438 | 0.9724 | 0.9579 | 0.9794 |
| 0.0541 | 3.2680 | 1000 | 0.0886 | 0.9376 | 0.9625 | 0.9499 | 0.9771 |
| 0.0582 | 3.3333 | 1020 | 0.0810 | 0.9443 | 0.9667 | 0.9554 | 0.9787 |
| 0.0714 | 3.3987 | 1040 | 0.0942 | 0.9295 | 0.9713 | 0.9499 | 0.9764 |
| 0.0693 | 3.4641 | 1060 | 0.0986 | 0.9256 | 0.9624 | 0.9436 | 0.9727 |
| 0.0559 | 3.5294 | 1080 | 0.0822 | 0.9457 | 0.9674 | 0.9564 | 0.9792 |
| 0.054 | 3.5948 | 1100 | 0.0879 | 0.9444 | 0.9650 | 0.9546 | 0.9766 |
| 0.0559 | 3.6601 | 1120 | 0.0821 | 0.9444 | 0.9705 | 0.9573 | 0.9794 |
| 0.0592 | 3.7255 | 1140 | 0.0800 | 0.9514 | 0.9694 | 0.9603 | 0.9803 |
| 0.0637 | 3.7908 | 1160 | 0.0797 | 0.9508 | 0.9665 | 0.9586 | 0.9804 |
| 0.0716 | 3.8562 | 1180 | 0.0799 | 0.9452 | 0.9700 | 0.9574 | 0.9792 |
| 0.0659 | 3.9216 | 1200 | 0.0821 | 0.9483 | 0.9692 | 0.9586 | 0.9802 |
| 0.0721 | 3.9869 | 1220 | 0.0770 | 0.9470 | 0.9704 | 0.9585 | 0.9800 |
| 0.0502 | 4.0523 | 1240 | 0.0797 | 0.9469 | 0.9719 | 0.9592 | 0.9803 |
| 0.0404 | 4.1176 | 1260 | 0.0855 | 0.9367 | 0.9704 | 0.9533 | 0.9776 |
| 0.0455 | 4.1830 | 1280 | 0.0805 | 0.9469 | 0.9669 | 0.9568 | 0.9790 |
| 0.0463 | 4.2484 | 1300 | 0.0774 | 0.9505 | 0.9701 | 0.9602 | 0.9808 |
| 0.0446 | 4.3137 | 1320 | 0.0753 | 0.9509 | 0.9645 | 0.9577 | 0.9806 |
| 0.0444 | 4.3791 | 1340 | 0.0799 | 0.9431 | 0.9709 | 0.9568 | 0.9793 |
| 0.0403 | 4.4444 | 1360 | 0.0792 | 0.9463 | 0.9689 | 0.9575 | 0.9801 |
| 0.0355 | 4.5098 | 1380 | 0.0804 | 0.9445 | 0.9693 | 0.9567 | 0.9797 |
| 0.0549 | 4.5752 | 1400 | 0.0795 | 0.9469 | 0.9666 | 0.9566 | 0.9797 |
| 0.0437 | 4.6405 | 1420 | 0.0780 | 0.9462 | 0.9722 | 0.9590 | 0.9803 |
| 0.0411 | 4.7059 | 1440 | 0.0849 | 0.9451 | 0.9706 | 0.9577 | 0.9796 |
| 0.05 | 4.7712 | 1460 | 0.0849 | 0.9425 | 0.9710 | 0.9565 | 0.9789 |
| 0.0436 | 4.8366 | 1480 | 0.0819 | 0.9492 | 0.9712 | 0.9601 | 0.9801 |
| 0.0454 | 4.9020 | 1500 | 0.0820 | 0.9432 | 0.9735 | 0.9581 | 0.9798 |
| 0.052 | 4.9673 | 1520 | 0.0804 | 0.9457 | 0.9708 | 0.9581 | 0.9803 |
| 0.0397 | 5.0327 | 1540 | 0.0828 | 0.9457 | 0.9717 | 0.9585 | 0.9801 |
| 0.0372 | 5.0980 | 1560 | 0.0782 | 0.9508 | 0.9731 | 0.9618 | 0.9816 |
| 0.0412 | 5.1634 | 1580 | 0.0796 | 0.9486 | 0.9739 | 0.9611 | 0.9807 |
| 0.0353 | 5.2288 | 1600 | 0.0840 | 0.9405 | 0.9730 | 0.9565 | 0.9789 |
| 0.0304 | 5.2941 | 1620 | 0.0778 | 0.9500 | 0.9704 | 0.9601 | 0.9805 |
| 0.0365 | 5.3595 | 1640 | 0.0819 | 0.9462 | 0.9708 | 0.9583 | 0.9796 |
| 0.0319 | 5.4248 | 1660 | 0.0770 | 0.9531 | 0.9694 | 0.9612 | 0.9817 |
| 0.0424 | 5.4902 | 1680 | 0.0827 | 0.9449 | 0.9671 | 0.9559 | 0.9801 |
| 0.0378 | 5.5556 | 1700 | 0.0772 | 0.9548 | 0.9705 | 0.9626 | 0.9820 |
| 0.0379 | 5.6209 | 1720 | 0.0842 | 0.9460 | 0.9700 | 0.9578 | 0.9796 |
| 0.0369 | 5.6863 | 1740 | 0.0779 | 0.9551 | 0.9694 | 0.9622 | 0.9820 |
| 0.0355 | 5.7516 | 1760 | 0.0825 | 0.9429 | 0.9701 | 0.9563 | 0.9799 |
| 0.0308 | 5.8170 | 1780 | 0.0790 | 0.9500 | 0.9732 | 0.9615 | 0.9816 |
| 0.0399 | 5.8824 | 1800 | 0.0808 | 0.9476 | 0.9696 | 0.9585 | 0.9805 |
| 0.0326 | 5.9477 | 1820 | 0.0777 | 0.9510 | 0.9709 | 0.9608 | 0.9813 |
| 0.0407 | 6.0131 | 1840 | 0.0800 | 0.9494 | 0.9703 | 0.9597 | 0.9811 |
| 0.0319 | 6.0784 | 1860 | 0.0810 | 0.9499 | 0.9705 | 0.9601 | 0.9812 |
| 0.0278 | 6.1438 | 1880 | 0.0822 | 0.9464 | 0.9703 | 0.9582 | 0.9805 |
| 0.0227 | 6.2092 | 1900 | 0.0852 | 0.9437 | 0.9684 | 0.9559 | 0.9797 |
| 0.0331 | 6.2745 | 1920 | 0.0813 | 0.9480 | 0.9708 | 0.9593 | 0.9810 |
| 0.0306 | 6.3399 | 1940 | 0.0823 | 0.9475 | 0.9707 | 0.9590 | 0.9809 |
| 0.0287 | 6.4052 | 1960 | 0.0814 | 0.9493 | 0.9704 | 0.9597 | 0.9812 |
| 0.0272 | 6.4706 | 1980 | 0.0826 | 0.9469 | 0.9691 | 0.9579 | 0.9808 |
| 0.0294 | 6.5359 | 2000 | 0.0821 | 0.9482 | 0.9710 | 0.9594 | 0.9813 |
| 0.0335 | 6.6013 | 2020 | 0.0814 | 0.9499 | 0.9710 | 0.9603 | 0.9816 |
| 0.0271 | 6.6667 | 2040 | 0.0809 | 0.9507 | 0.9695 | 0.9600 | 0.9817 |
| 0.0308 | 6.7320 | 2060 | 0.0807 | 0.9513 | 0.9705 | 0.9608 | 0.9820 |
| 0.0332 | 6.7974 | 2080 | 0.0798 | 0.9522 | 0.9708 | 0.9614 | 0.9822 |
| 0.0257 | 6.8627 | 2100 | 0.0802 | 0.9517 | 0.9714 | 0.9615 | 0.9821 |
| 0.0292 | 6.9281 | 2120 | 0.0806 | 0.9518 | 0.9720 | 0.9618 | 0.9821 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0 |