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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: xml-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. -->
# xml-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: 0.0777
- Precision: 0.9349
- Recall: 0.9537
- F1: 0.9442
- Accuracy: 0.9802
## 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: 2141
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.1318 | 0.0654 | 20 | 0.9601 | 0.5965 | 0.0370 | 0.0696 | 0.7295 |
| 0.6887 | 0.1307 | 40 | 0.3004 | 0.7758 | 0.7688 | 0.7723 | 0.9181 |
| 0.3473 | 0.1961 | 60 | 0.2058 | 0.8130 | 0.8602 | 0.8359 | 0.9424 |
| 0.25 | 0.2614 | 80 | 0.1636 | 0.8543 | 0.8732 | 0.8637 | 0.9519 |
| 0.2186 | 0.3268 | 100 | 0.1458 | 0.8713 | 0.8847 | 0.8780 | 0.9586 |
| 0.2276 | 0.3922 | 120 | 0.1288 | 0.8641 | 0.9096 | 0.8863 | 0.9630 |
| 0.1733 | 0.4575 | 140 | 0.1151 | 0.9050 | 0.9017 | 0.9034 | 0.9640 |
| 0.1584 | 0.5229 | 160 | 0.1083 | 0.8944 | 0.9414 | 0.9173 | 0.9701 |
| 0.1506 | 0.5882 | 180 | 0.1213 | 0.8635 | 0.9482 | 0.9039 | 0.9648 |
| 0.1303 | 0.6536 | 200 | 0.0963 | 0.8953 | 0.9409 | 0.9175 | 0.9699 |
| 0.1327 | 0.7190 | 220 | 0.1088 | 0.8808 | 0.9144 | 0.8973 | 0.9655 |
| 0.1416 | 0.7843 | 240 | 0.0903 | 0.9173 | 0.9429 | 0.9299 | 0.9748 |
| 0.1229 | 0.8497 | 260 | 0.0924 | 0.9197 | 0.9390 | 0.9292 | 0.9740 |
| 0.1228 | 0.9150 | 280 | 0.1105 | 0.8943 | 0.9463 | 0.9196 | 0.9680 |
| 0.1338 | 0.9804 | 300 | 0.0840 | 0.9174 | 0.9472 | 0.9320 | 0.9749 |
| 0.1141 | 1.0458 | 320 | 0.0906 | 0.9121 | 0.9488 | 0.9301 | 0.9744 |
| 0.0983 | 1.1111 | 340 | 0.0926 | 0.9112 | 0.9570 | 0.9336 | 0.9732 |
| 0.099 | 1.1765 | 360 | 0.0791 | 0.9204 | 0.9508 | 0.9354 | 0.9765 |
| 0.0947 | 1.2418 | 380 | 0.0852 | 0.9271 | 0.9469 | 0.9369 | 0.9769 |
| 0.1163 | 1.3072 | 400 | 0.0764 | 0.9231 | 0.9484 | 0.9356 | 0.9765 |
| 0.105 | 1.3725 | 420 | 0.0863 | 0.9062 | 0.9355 | 0.9206 | 0.9732 |
| 0.096 | 1.4379 | 440 | 0.0833 | 0.9282 | 0.9473 | 0.9377 | 0.9772 |
| 0.0927 | 1.5033 | 460 | 0.0714 | 0.9368 | 0.9520 | 0.9443 | 0.9788 |
| 0.0944 | 1.5686 | 480 | 0.0730 | 0.9296 | 0.9579 | 0.9435 | 0.9784 |
| 0.0797 | 1.6340 | 500 | 0.0837 | 0.9180 | 0.9493 | 0.9334 | 0.9756 |
| 0.0726 | 1.6993 | 520 | 0.0834 | 0.9173 | 0.9660 | 0.9410 | 0.9772 |
| 0.078 | 1.7647 | 540 | 0.0744 | 0.9292 | 0.9435 | 0.9363 | 0.9780 |
| 0.0902 | 1.8301 | 560 | 0.0793 | 0.9275 | 0.9564 | 0.9417 | 0.9767 |
| 0.0874 | 1.8954 | 580 | 0.0859 | 0.9180 | 0.9573 | 0.9372 | 0.9752 |
| 0.089 | 1.9608 | 600 | 0.0860 | 0.9146 | 0.9586 | 0.9361 | 0.9745 |
| 0.0766 | 2.0261 | 620 | 0.0821 | 0.9212 | 0.9569 | 0.9387 | 0.9772 |
| 0.067 | 2.0915 | 640 | 0.0746 | 0.9323 | 0.9583 | 0.9452 | 0.9797 |
| 0.0535 | 2.1569 | 660 | 0.0771 | 0.9236 | 0.9476 | 0.9354 | 0.9774 |
| 0.0794 | 2.2222 | 680 | 0.0779 | 0.9315 | 0.9544 | 0.9428 | 0.9782 |
| 0.0819 | 2.2876 | 700 | 0.0841 | 0.9111 | 0.9443 | 0.9274 | 0.9756 |
| 0.0642 | 2.3529 | 720 | 0.0671 | 0.9406 | 0.9589 | 0.9497 | 0.9818 |
| 0.0681 | 2.4183 | 740 | 0.0724 | 0.9354 | 0.9464 | 0.9409 | 0.9789 |
| 0.0881 | 2.4837 | 760 | 0.0689 | 0.9327 | 0.9575 | 0.9450 | 0.9810 |
| 0.0706 | 2.5490 | 780 | 0.0813 | 0.9242 | 0.9582 | 0.9409 | 0.9782 |
| 0.0765 | 2.6144 | 800 | 0.0689 | 0.9365 | 0.9551 | 0.9457 | 0.9797 |
| 0.062 | 2.6797 | 820 | 0.0716 | 0.9434 | 0.9478 | 0.9456 | 0.9804 |
| 0.093 | 2.7451 | 840 | 0.0754 | 0.9282 | 0.9490 | 0.9385 | 0.9783 |
| 0.0659 | 2.8105 | 860 | 0.0799 | 0.9227 | 0.9524 | 0.9373 | 0.9775 |
| 0.0806 | 2.8758 | 880 | 0.0775 | 0.9197 | 0.9533 | 0.9362 | 0.9777 |
| 0.0632 | 2.9412 | 900 | 0.0754 | 0.9229 | 0.9569 | 0.9396 | 0.9789 |
| 0.064 | 3.0065 | 920 | 0.0700 | 0.9387 | 0.9556 | 0.9471 | 0.9813 |
| 0.0499 | 3.0719 | 940 | 0.0725 | 0.9282 | 0.9573 | 0.9425 | 0.9804 |
| 0.0525 | 3.1373 | 960 | 0.0852 | 0.9258 | 0.9572 | 0.9412 | 0.9771 |
| 0.0488 | 3.2026 | 980 | 0.0740 | 0.9298 | 0.9577 | 0.9436 | 0.9792 |
| 0.0602 | 3.2680 | 1000 | 0.0785 | 0.9274 | 0.9507 | 0.9389 | 0.9777 |
| 0.0574 | 3.3333 | 1020 | 0.0746 | 0.9362 | 0.9537 | 0.9449 | 0.9796 |
| 0.0583 | 3.3987 | 1040 | 0.0768 | 0.9272 | 0.9641 | 0.9453 | 0.9798 |
| 0.0618 | 3.4641 | 1060 | 0.0774 | 0.9264 | 0.9546 | 0.9403 | 0.9783 |
| 0.0503 | 3.5294 | 1080 | 0.0724 | 0.9287 | 0.9484 | 0.9385 | 0.9783 |
| 0.0529 | 3.5948 | 1100 | 0.0777 | 0.9349 | 0.9556 | 0.9451 | 0.9787 |
| 0.0448 | 3.6601 | 1120 | 0.0686 | 0.9383 | 0.9563 | 0.9472 | 0.9815 |
| 0.0658 | 3.7255 | 1140 | 0.0683 | 0.9453 | 0.9576 | 0.9514 | 0.9817 |
| 0.0591 | 3.7908 | 1160 | 0.0650 | 0.9407 | 0.9586 | 0.9496 | 0.9822 |
| 0.0635 | 3.8562 | 1180 | 0.0781 | 0.9283 | 0.9551 | 0.9415 | 0.9779 |
| 0.063 | 3.9216 | 1200 | 0.0764 | 0.9330 | 0.9545 | 0.9436 | 0.9783 |
| 0.0586 | 3.9869 | 1220 | 0.0706 | 0.9334 | 0.9548 | 0.9440 | 0.9808 |
| 0.0446 | 4.0523 | 1240 | 0.0744 | 0.9319 | 0.9556 | 0.9436 | 0.9794 |
| 0.0373 | 4.1176 | 1260 | 0.0713 | 0.9351 | 0.9534 | 0.9442 | 0.9802 |
| 0.0387 | 4.1830 | 1280 | 0.0752 | 0.9371 | 0.9537 | 0.9453 | 0.9805 |
| 0.0449 | 4.2484 | 1300 | 0.0751 | 0.9360 | 0.9536 | 0.9447 | 0.9805 |
| 0.0415 | 4.3137 | 1320 | 0.0740 | 0.9419 | 0.9506 | 0.9462 | 0.9814 |
| 0.0484 | 4.3791 | 1340 | 0.0692 | 0.9409 | 0.9562 | 0.9485 | 0.9815 |
| 0.0414 | 4.4444 | 1360 | 0.0751 | 0.9288 | 0.9555 | 0.9419 | 0.9797 |
| 0.0346 | 4.5098 | 1380 | 0.0790 | 0.9267 | 0.9560 | 0.9411 | 0.9796 |
| 0.0466 | 4.5752 | 1400 | 0.0840 | 0.9187 | 0.9414 | 0.9299 | 0.9770 |
| 0.0467 | 4.6405 | 1420 | 0.0739 | 0.9342 | 0.9579 | 0.9459 | 0.9805 |
| 0.0401 | 4.7059 | 1440 | 0.0781 | 0.9293 | 0.9530 | 0.9410 | 0.9786 |
| 0.0502 | 4.7712 | 1460 | 0.0768 | 0.9323 | 0.9582 | 0.9451 | 0.9801 |
| 0.0403 | 4.8366 | 1480 | 0.0745 | 0.9431 | 0.9564 | 0.9497 | 0.9813 |
| 0.0471 | 4.9020 | 1500 | 0.0772 | 0.9316 | 0.9581 | 0.9447 | 0.9796 |
| 0.0556 | 4.9673 | 1520 | 0.0749 | 0.9324 | 0.9531 | 0.9426 | 0.9801 |
| 0.0398 | 5.0327 | 1540 | 0.0784 | 0.9310 | 0.9534 | 0.9421 | 0.9796 |
| 0.0422 | 5.0980 | 1560 | 0.0741 | 0.9386 | 0.9562 | 0.9473 | 0.9812 |
| 0.0545 | 5.1634 | 1580 | 0.0721 | 0.9398 | 0.9593 | 0.9495 | 0.9817 |
| 0.0367 | 5.2288 | 1600 | 0.0815 | 0.9241 | 0.9526 | 0.9381 | 0.9778 |
| 0.0333 | 5.2941 | 1620 | 0.0741 | 0.9381 | 0.9545 | 0.9463 | 0.9805 |
| 0.0324 | 5.3595 | 1640 | 0.0755 | 0.9368 | 0.9569 | 0.9468 | 0.9807 |
| 0.0343 | 5.4248 | 1660 | 0.0735 | 0.9412 | 0.9536 | 0.9473 | 0.9811 |
| 0.0405 | 5.4902 | 1680 | 0.0773 | 0.9344 | 0.9550 | 0.9446 | 0.9803 |
| 0.0343 | 5.5556 | 1700 | 0.0723 | 0.9412 | 0.9554 | 0.9482 | 0.9815 |
| 0.0379 | 5.6209 | 1720 | 0.0787 | 0.9284 | 0.9513 | 0.9397 | 0.9788 |
| 0.0346 | 5.6863 | 1740 | 0.0741 | 0.9405 | 0.9548 | 0.9476 | 0.9808 |
| 0.0376 | 5.7516 | 1760 | 0.0794 | 0.9224 | 0.9485 | 0.9353 | 0.9781 |
| 0.0288 | 5.8170 | 1780 | 0.0758 | 0.9367 | 0.9594 | 0.9479 | 0.9813 |
| 0.0394 | 5.8824 | 1800 | 0.0750 | 0.9394 | 0.9566 | 0.9479 | 0.9810 |
| 0.0296 | 5.9477 | 1820 | 0.0736 | 0.9396 | 0.9569 | 0.9482 | 0.9814 |
| 0.0335 | 6.0131 | 1840 | 0.0773 | 0.9355 | 0.9549 | 0.9451 | 0.9802 |
| 0.0297 | 6.0784 | 1860 | 0.0760 | 0.9361 | 0.9536 | 0.9447 | 0.9803 |
| 0.027 | 6.1438 | 1880 | 0.0770 | 0.9320 | 0.9528 | 0.9423 | 0.9798 |
| 0.0247 | 6.2092 | 1900 | 0.0788 | 0.9318 | 0.9514 | 0.9415 | 0.9795 |
| 0.0335 | 6.2745 | 1920 | 0.0770 | 0.9390 | 0.9566 | 0.9477 | 0.9810 |
| 0.0286 | 6.3399 | 1940 | 0.0770 | 0.9361 | 0.9558 | 0.9459 | 0.9805 |
| 0.0256 | 6.4052 | 1960 | 0.0765 | 0.9351 | 0.9546 | 0.9447 | 0.9802 |
| 0.0268 | 6.4706 | 1980 | 0.0773 | 0.9335 | 0.9520 | 0.9426 | 0.9801 |
| 0.0247 | 6.5359 | 2000 | 0.0770 | 0.9361 | 0.9550 | 0.9454 | 0.9807 |
| 0.0299 | 6.6013 | 2020 | 0.0773 | 0.9373 | 0.9550 | 0.9461 | 0.9807 |
| 0.024 | 6.6667 | 2040 | 0.0789 | 0.9350 | 0.9525 | 0.9437 | 0.9800 |
| 0.0278 | 6.7320 | 2060 | 0.0778 | 0.9367 | 0.9539 | 0.9452 | 0.9804 |
| 0.0378 | 6.7974 | 2080 | 0.0766 | 0.9372 | 0.9545 | 0.9458 | 0.9807 |
| 0.0232 | 6.8627 | 2100 | 0.0775 | 0.9361 | 0.9538 | 0.9449 | 0.9804 |
| 0.0259 | 6.9281 | 2120 | 0.0780 | 0.9353 | 0.9540 | 0.9446 | 0.9802 |
| 0.025 | 6.9935 | 2140 | 0.0777 | 0.9349 | 0.9537 | 0.9442 | 0.9802 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3 |