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xml-roberta-large-ner-qlorafinetune-runs-colab-16size

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+ ---
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+ library_name: peft
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+ license: mit
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+ base_model: FacebookAI/xlm-roberta-large
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - biobert_json
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: xml-roberta-large-ner-qlorafinetune-runs-colab-16size
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # xml-roberta-large-ner-qlorafinetune-runs-colab-16size
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+
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+ This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0732
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+ - Precision: 0.9359
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+ - Recall: 0.9588
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+ - F1: 0.9472
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+ - Accuracy: 0.9801
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0004
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - training_steps: 2447
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 2.4135 | 0.0327 | 20 | 1.7070 | 0.0113 | 0.0002 | 0.0005 | 0.7167 |
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+ | 1.3764 | 0.0654 | 40 | 0.9462 | 0.6193 | 0.2169 | 0.3212 | 0.7637 |
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+ | 0.8476 | 0.0980 | 60 | 0.6483 | 0.6785 | 0.5300 | 0.5951 | 0.8525 |
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+ | 0.5547 | 0.1307 | 80 | 0.2995 | 0.7529 | 0.7890 | 0.7705 | 0.9201 |
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+ | 0.3767 | 0.1634 | 100 | 0.3207 | 0.6986 | 0.8501 | 0.7669 | 0.9055 |
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+ | 0.3529 | 0.1961 | 120 | 0.2094 | 0.7818 | 0.8816 | 0.8287 | 0.9408 |
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+ | 0.2858 | 0.2288 | 140 | 0.1758 | 0.8293 | 0.8917 | 0.8594 | 0.9466 |
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+ | 0.2258 | 0.2614 | 160 | 0.1775 | 0.8075 | 0.9034 | 0.8528 | 0.9493 |
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+ | 0.2338 | 0.2941 | 180 | 0.1420 | 0.8669 | 0.8967 | 0.8816 | 0.9558 |
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+ | 0.1759 | 0.3268 | 200 | 0.1300 | 0.8615 | 0.9230 | 0.8912 | 0.9626 |
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+ | 0.1856 | 0.3595 | 220 | 0.1103 | 0.8935 | 0.9275 | 0.9101 | 0.9668 |
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+ | 0.209 | 0.3922 | 240 | 0.1197 | 0.8891 | 0.9368 | 0.9123 | 0.9671 |
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+ | 0.1486 | 0.4248 | 260 | 0.1099 | 0.9134 | 0.9191 | 0.9162 | 0.9679 |
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+ | 0.1468 | 0.4575 | 280 | 0.1229 | 0.8681 | 0.9125 | 0.8898 | 0.9615 |
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+ | 0.1306 | 0.4902 | 300 | 0.0915 | 0.9178 | 0.9397 | 0.9286 | 0.9731 |
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+ | 0.1712 | 0.5229 | 320 | 0.1025 | 0.9076 | 0.9436 | 0.9252 | 0.9703 |
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+ | 0.1805 | 0.5556 | 340 | 0.1610 | 0.8527 | 0.9366 | 0.8927 | 0.9515 |
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+ | 0.1433 | 0.5882 | 360 | 0.0993 | 0.9059 | 0.9380 | 0.9217 | 0.9719 |
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+ | 0.1158 | 0.6209 | 380 | 0.1263 | 0.8851 | 0.9343 | 0.9090 | 0.9651 |
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+ | 0.1316 | 0.6536 | 400 | 0.1047 | 0.9001 | 0.9321 | 0.9159 | 0.9702 |
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+ | 0.1525 | 0.6863 | 420 | 0.1109 | 0.8890 | 0.9430 | 0.9152 | 0.9673 |
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+ | 0.1376 | 0.7190 | 440 | 0.0886 | 0.9130 | 0.9317 | 0.9222 | 0.9733 |
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+ | 0.1268 | 0.7516 | 460 | 0.0999 | 0.8913 | 0.9555 | 0.9223 | 0.9708 |
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+ | 0.13 | 0.7843 | 480 | 0.1133 | 0.8883 | 0.9470 | 0.9167 | 0.9686 |
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+ | 0.1762 | 0.8170 | 500 | 0.1018 | 0.9036 | 0.9244 | 0.9139 | 0.9707 |
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+ | 0.1074 | 0.8497 | 520 | 0.0933 | 0.9145 | 0.9457 | 0.9298 | 0.9739 |
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+ | 0.1307 | 0.8824 | 540 | 0.1172 | 0.8852 | 0.9497 | 0.9163 | 0.9649 |
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+ | 0.1258 | 0.9150 | 560 | 0.1191 | 0.8947 | 0.9491 | 0.9211 | 0.9687 |
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+ | 0.1426 | 0.9477 | 580 | 0.0916 | 0.9061 | 0.9452 | 0.9252 | 0.9727 |
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+ | 0.1182 | 0.9804 | 600 | 0.0916 | 0.9284 | 0.9342 | 0.9313 | 0.9750 |
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+ | 0.1364 | 1.0131 | 620 | 0.1373 | 0.9022 | 0.9161 | 0.9091 | 0.9662 |
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+ | 0.1141 | 1.0458 | 640 | 0.0935 | 0.9134 | 0.9527 | 0.9326 | 0.9741 |
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+ | 0.106 | 1.0784 | 660 | 0.0998 | 0.9141 | 0.9354 | 0.9246 | 0.9717 |
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+ | 0.1149 | 1.1111 | 680 | 0.0946 | 0.9081 | 0.9570 | 0.9319 | 0.9745 |
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+ | 0.0993 | 1.1438 | 700 | 0.0983 | 0.8995 | 0.9561 | 0.9270 | 0.9738 |
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+ | 0.1078 | 1.1765 | 720 | 0.0925 | 0.9158 | 0.9472 | 0.9312 | 0.9727 |
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+ | 0.0913 | 1.2092 | 740 | 0.0875 | 0.9265 | 0.9405 | 0.9335 | 0.9749 |
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+ | 0.1159 | 1.2418 | 760 | 0.0819 | 0.9189 | 0.9534 | 0.9359 | 0.9775 |
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+ | 0.1251 | 1.2745 | 780 | 0.0897 | 0.9299 | 0.9251 | 0.9275 | 0.9735 |
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+ | 0.0975 | 1.3072 | 800 | 0.0909 | 0.9234 | 0.9338 | 0.9286 | 0.9747 |
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+ | 0.1057 | 1.3399 | 820 | 0.0930 | 0.9042 | 0.9436 | 0.9235 | 0.9732 |
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+ | 0.1195 | 1.3725 | 840 | 0.0799 | 0.9112 | 0.9350 | 0.9230 | 0.9755 |
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+ | 0.1186 | 1.4052 | 860 | 0.0829 | 0.9136 | 0.9494 | 0.9311 | 0.9751 |
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+ | 0.0933 | 1.4379 | 880 | 0.0953 | 0.9098 | 0.9513 | 0.9301 | 0.9746 |
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+ | 0.1106 | 1.4706 | 900 | 0.0849 | 0.9116 | 0.9555 | 0.9330 | 0.9754 |
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+ | 0.0816 | 1.5033 | 920 | 0.0835 | 0.9234 | 0.9532 | 0.9380 | 0.9762 |
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+ | 0.0906 | 1.5359 | 940 | 0.0812 | 0.9197 | 0.9494 | 0.9343 | 0.9774 |
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+ | 0.1323 | 1.5686 | 960 | 0.0828 | 0.9227 | 0.9453 | 0.9338 | 0.9756 |
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+ | 0.0945 | 1.6013 | 980 | 0.0782 | 0.9283 | 0.9499 | 0.9389 | 0.9783 |
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+ | 0.0793 | 1.6340 | 1000 | 0.0900 | 0.9076 | 0.9526 | 0.9296 | 0.9739 |
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+ | 0.0775 | 1.6667 | 1020 | 0.1095 | 0.8976 | 0.9537 | 0.9248 | 0.9691 |
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+ | 0.0813 | 1.6993 | 1040 | 0.0902 | 0.9063 | 0.9577 | 0.9313 | 0.9746 |
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+ | 0.0733 | 1.7320 | 1060 | 0.1105 | 0.9038 | 0.9533 | 0.9279 | 0.9700 |
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+ | 0.0905 | 1.7647 | 1080 | 0.0890 | 0.8950 | 0.9256 | 0.9100 | 0.9722 |
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+ | 0.0838 | 1.7974 | 1100 | 0.0770 | 0.9212 | 0.9324 | 0.9268 | 0.9750 |
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+ | 0.0983 | 1.8301 | 1120 | 0.0810 | 0.9230 | 0.9564 | 0.9394 | 0.9776 |
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+ | 0.0803 | 1.8627 | 1140 | 0.0754 | 0.9322 | 0.9509 | 0.9415 | 0.9798 |
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+ | 0.091 | 1.8954 | 1160 | 0.0779 | 0.9299 | 0.9601 | 0.9448 | 0.9787 |
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+ | 0.0812 | 1.9281 | 1180 | 0.0784 | 0.9222 | 0.9563 | 0.9390 | 0.9786 |
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+ | 0.0845 | 1.9608 | 1200 | 0.0760 | 0.9315 | 0.9548 | 0.9430 | 0.9796 |
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+ | 0.0817 | 1.9935 | 1220 | 0.0765 | 0.9278 | 0.9625 | 0.9448 | 0.9796 |
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+ | 0.0531 | 2.0261 | 1240 | 0.0764 | 0.9344 | 0.9501 | 0.9422 | 0.9785 |
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+ | 0.0873 | 2.0588 | 1260 | 0.0809 | 0.9191 | 0.9574 | 0.9379 | 0.9771 |
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+ | 0.0682 | 2.0915 | 1280 | 0.0739 | 0.9256 | 0.9610 | 0.9430 | 0.9788 |
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+ | 0.0533 | 2.1242 | 1300 | 0.0724 | 0.9270 | 0.9567 | 0.9416 | 0.9797 |
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+ | 0.062 | 2.1569 | 1320 | 0.0705 | 0.9388 | 0.9507 | 0.9447 | 0.9800 |
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+ | 0.0961 | 2.1895 | 1340 | 0.0772 | 0.9216 | 0.9569 | 0.9389 | 0.9780 |
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+ | 0.0638 | 2.2222 | 1360 | 0.0806 | 0.9176 | 0.9589 | 0.9378 | 0.9760 |
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+ | 0.0672 | 2.2549 | 1380 | 0.0769 | 0.9346 | 0.9543 | 0.9443 | 0.9795 |
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+ | 0.072 | 2.2876 | 1400 | 0.0812 | 0.9294 | 0.9564 | 0.9427 | 0.9784 |
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+ | 0.0687 | 2.3203 | 1420 | 0.0873 | 0.9224 | 0.9509 | 0.9365 | 0.9760 |
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+ | 0.051 | 2.3529 | 1440 | 0.0736 | 0.9433 | 0.9520 | 0.9476 | 0.9808 |
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+ | 0.0668 | 2.3856 | 1460 | 0.0785 | 0.9320 | 0.9560 | 0.9438 | 0.9795 |
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+ | 0.0657 | 2.4183 | 1480 | 0.0775 | 0.9303 | 0.9540 | 0.9420 | 0.9788 |
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+ | 0.0897 | 2.4510 | 1500 | 0.0825 | 0.9093 | 0.9544 | 0.9313 | 0.9760 |
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+ | 0.0878 | 2.4837 | 1520 | 0.0717 | 0.9349 | 0.9579 | 0.9463 | 0.9805 |
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+ | 0.0595 | 2.5163 | 1540 | 0.0717 | 0.9376 | 0.9585 | 0.9479 | 0.9811 |
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+ | 0.0663 | 2.5490 | 1560 | 0.0730 | 0.9370 | 0.9580 | 0.9474 | 0.9805 |
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+ | 0.0716 | 2.5817 | 1580 | 0.0764 | 0.9277 | 0.9599 | 0.9435 | 0.9793 |
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+ | 0.0613 | 2.6144 | 1600 | 0.0713 | 0.9357 | 0.9574 | 0.9464 | 0.9810 |
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+ | 0.0703 | 2.6471 | 1620 | 0.0794 | 0.9291 | 0.9537 | 0.9412 | 0.9778 |
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+ | 0.0584 | 2.6797 | 1640 | 0.0707 | 0.9403 | 0.9506 | 0.9454 | 0.9804 |
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+ | 0.063 | 2.7124 | 1660 | 0.0748 | 0.9253 | 0.9591 | 0.9419 | 0.9781 |
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+ | 0.0666 | 2.7451 | 1680 | 0.0697 | 0.9336 | 0.9495 | 0.9415 | 0.9797 |
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+ | 0.0546 | 2.7778 | 1700 | 0.0753 | 0.9239 | 0.9579 | 0.9406 | 0.9788 |
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+ | 0.0705 | 2.8105 | 1720 | 0.0683 | 0.9357 | 0.9612 | 0.9483 | 0.9816 |
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+ | 0.0691 | 2.8431 | 1740 | 0.0716 | 0.9343 | 0.9594 | 0.9467 | 0.9804 |
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+ | 0.0573 | 2.8758 | 1760 | 0.0729 | 0.9300 | 0.9543 | 0.9420 | 0.9797 |
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+ | 0.0477 | 2.9085 | 1780 | 0.0775 | 0.9270 | 0.9528 | 0.9397 | 0.9784 |
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+ | 0.0605 | 2.9412 | 1800 | 0.0733 | 0.9317 | 0.9542 | 0.9428 | 0.9792 |
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+ | 0.0587 | 2.9739 | 1820 | 0.0703 | 0.9379 | 0.9630 | 0.9503 | 0.9807 |
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+ | 0.0473 | 3.0065 | 1840 | 0.0688 | 0.9398 | 0.9637 | 0.9516 | 0.9823 |
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+ | 0.05 | 3.0392 | 1860 | 0.0738 | 0.9253 | 0.9528 | 0.9389 | 0.9790 |
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+ | 0.0362 | 3.0719 | 1880 | 0.0743 | 0.9224 | 0.9569 | 0.9393 | 0.9784 |
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+ | 0.0401 | 3.1046 | 1900 | 0.0710 | 0.9336 | 0.9561 | 0.9447 | 0.9807 |
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+ | 0.0524 | 3.1373 | 1920 | 0.0815 | 0.9214 | 0.9543 | 0.9376 | 0.9771 |
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+ | 0.0484 | 3.1699 | 1940 | 0.0724 | 0.9290 | 0.9635 | 0.9459 | 0.9803 |
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+ | 0.0373 | 3.2026 | 1960 | 0.0687 | 0.9411 | 0.9579 | 0.9494 | 0.9817 |
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+ | 0.0441 | 3.2353 | 1980 | 0.0752 | 0.9231 | 0.9615 | 0.9419 | 0.9791 |
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+ | 0.0502 | 3.2680 | 2000 | 0.0755 | 0.9277 | 0.9597 | 0.9434 | 0.9785 |
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+ | 0.0359 | 3.3007 | 2020 | 0.0707 | 0.9362 | 0.9603 | 0.9481 | 0.9808 |
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+ | 0.057 | 3.3333 | 2040 | 0.0720 | 0.9338 | 0.9561 | 0.9448 | 0.9799 |
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+ | 0.0443 | 3.3660 | 2060 | 0.0692 | 0.9412 | 0.9594 | 0.9502 | 0.9813 |
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+ | 0.0466 | 3.3987 | 2080 | 0.0746 | 0.9288 | 0.9623 | 0.9453 | 0.9802 |
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+ | 0.0419 | 3.4314 | 2100 | 0.0696 | 0.9372 | 0.9607 | 0.9488 | 0.9812 |
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+ | 0.0586 | 3.4641 | 2120 | 0.0718 | 0.9324 | 0.9586 | 0.9453 | 0.9804 |
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+ | 0.0367 | 3.4967 | 2140 | 0.0724 | 0.9305 | 0.9568 | 0.9435 | 0.9799 |
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+ | 0.0411 | 3.5294 | 2160 | 0.0722 | 0.9312 | 0.9556 | 0.9432 | 0.9802 |
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+ | 0.0443 | 3.5621 | 2180 | 0.0716 | 0.9323 | 0.9507 | 0.9414 | 0.9796 |
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+ | 0.0429 | 3.5948 | 2200 | 0.0724 | 0.9307 | 0.9550 | 0.9427 | 0.9794 |
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+ | 0.0342 | 3.6275 | 2220 | 0.0696 | 0.9347 | 0.9595 | 0.9470 | 0.9811 |
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+ | 0.0338 | 3.6601 | 2240 | 0.0696 | 0.9387 | 0.9588 | 0.9487 | 0.9816 |
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+ | 0.0516 | 3.6928 | 2260 | 0.0723 | 0.9350 | 0.9587 | 0.9467 | 0.9804 |
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+ | 0.046 | 3.7255 | 2280 | 0.0705 | 0.9390 | 0.9593 | 0.9490 | 0.9814 |
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+ | 0.046 | 3.7582 | 2300 | 0.0717 | 0.9372 | 0.9593 | 0.9481 | 0.9810 |
177
+ | 0.0367 | 3.7908 | 2320 | 0.0711 | 0.9370 | 0.9605 | 0.9486 | 0.9815 |
178
+ | 0.0532 | 3.8235 | 2340 | 0.0717 | 0.9370 | 0.9599 | 0.9483 | 0.9811 |
179
+ | 0.0464 | 3.8562 | 2360 | 0.0725 | 0.9371 | 0.9601 | 0.9485 | 0.9809 |
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+ | 0.0503 | 3.8889 | 2380 | 0.0734 | 0.9351 | 0.9609 | 0.9478 | 0.9803 |
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+ | 0.0488 | 3.9216 | 2400 | 0.0741 | 0.9348 | 0.9604 | 0.9474 | 0.9802 |
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+ | 0.0418 | 3.9542 | 2420 | 0.0734 | 0.9357 | 0.9592 | 0.9473 | 0.9802 |
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+ | 0.0571 | 3.9869 | 2440 | 0.0732 | 0.9359 | 0.9588 | 0.9472 | 0.9801 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.13.2
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+ - Transformers 4.46.3
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.2.0
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+ - Tokenizers 0.20.3