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---
library_name: peft
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2002
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 conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0862
- Precision: 0.8825
- Recall: 0.8853
- F1: 0.8839
- Accuracy: 0.9813
## 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 OptimizerNames.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: 1820
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.335 | 0.0766 | 20 | 0.4433 | 0.1426 | 0.0857 | 0.1071 | 0.8719 |
| 0.3122 | 0.1533 | 40 | 0.2540 | 0.4993 | 0.5722 | 0.5332 | 0.9326 |
| 0.1508 | 0.2299 | 60 | 0.1346 | 0.7299 | 0.7569 | 0.7431 | 0.9647 |
| 0.0954 | 0.3065 | 80 | 0.1159 | 0.7791 | 0.8024 | 0.7906 | 0.9708 |
| 0.095 | 0.3831 | 100 | 0.0957 | 0.8206 | 0.8323 | 0.8264 | 0.9747 |
| 0.085 | 0.4598 | 120 | 0.0941 | 0.8307 | 0.8447 | 0.8376 | 0.9762 |
| 0.0963 | 0.5364 | 140 | 0.0859 | 0.8188 | 0.8421 | 0.8303 | 0.9769 |
| 0.0861 | 0.6130 | 160 | 0.0863 | 0.8080 | 0.8449 | 0.8260 | 0.9755 |
| 0.0775 | 0.6897 | 180 | 0.0798 | 0.8417 | 0.8587 | 0.8501 | 0.9790 |
| 0.0706 | 0.7663 | 200 | 0.0806 | 0.8446 | 0.8428 | 0.8437 | 0.9775 |
| 0.0613 | 0.8429 | 220 | 0.0805 | 0.8377 | 0.8573 | 0.8474 | 0.9786 |
| 0.0609 | 0.9195 | 240 | 0.0805 | 0.8395 | 0.8483 | 0.8439 | 0.9774 |
| 0.054 | 0.9962 | 260 | 0.0819 | 0.8529 | 0.8683 | 0.8605 | 0.9786 |
| 0.0449 | 1.0728 | 280 | 0.0789 | 0.8353 | 0.8660 | 0.8504 | 0.9799 |
| 0.0504 | 1.1494 | 300 | 0.0769 | 0.8571 | 0.8686 | 0.8628 | 0.9788 |
| 0.0568 | 1.2261 | 320 | 0.0803 | 0.8719 | 0.8771 | 0.8745 | 0.9798 |
| 0.0528 | 1.3027 | 340 | 0.1051 | 0.7653 | 0.7852 | 0.7751 | 0.9691 |
| 0.0484 | 1.3793 | 360 | 0.1179 | 0.7920 | 0.7989 | 0.7955 | 0.9724 |
| 0.0531 | 1.4559 | 380 | 0.1006 | 0.8043 | 0.8148 | 0.8095 | 0.9728 |
| 0.0521 | 1.5326 | 400 | 0.0945 | 0.8060 | 0.8394 | 0.8224 | 0.9742 |
| 0.0574 | 1.6092 | 420 | 0.0840 | 0.8166 | 0.8493 | 0.8326 | 0.9774 |
| 0.07 | 1.6858 | 440 | 0.0772 | 0.8262 | 0.8500 | 0.8379 | 0.9782 |
| 0.0657 | 1.7625 | 460 | 0.0745 | 0.8573 | 0.8601 | 0.8587 | 0.9787 |
| 0.0512 | 1.8391 | 480 | 0.0795 | 0.8320 | 0.8513 | 0.8416 | 0.9780 |
| 0.0648 | 1.9157 | 500 | 0.0670 | 0.8427 | 0.8764 | 0.8592 | 0.9804 |
| 0.0438 | 1.9923 | 520 | 0.0726 | 0.8530 | 0.8640 | 0.8584 | 0.9799 |
| 0.0604 | 2.0690 | 540 | 0.0711 | 0.8525 | 0.8752 | 0.8637 | 0.9792 |
| 0.0379 | 2.1456 | 560 | 0.0724 | 0.8573 | 0.8725 | 0.8648 | 0.9792 |
| 0.0323 | 2.2222 | 580 | 0.0719 | 0.8625 | 0.8720 | 0.8672 | 0.9799 |
| 0.0389 | 2.2989 | 600 | 0.0772 | 0.8477 | 0.8681 | 0.8578 | 0.9791 |
| 0.0333 | 2.3755 | 620 | 0.0759 | 0.8407 | 0.8571 | 0.8488 | 0.9784 |
| 0.0345 | 2.4521 | 640 | 0.0758 | 0.8473 | 0.8693 | 0.8581 | 0.9802 |
| 0.0366 | 2.5287 | 660 | 0.0730 | 0.8562 | 0.8644 | 0.8603 | 0.9790 |
| 0.0414 | 2.6054 | 680 | 0.0820 | 0.8548 | 0.8631 | 0.8589 | 0.9780 |
| 0.0392 | 2.6820 | 700 | 0.0773 | 0.8549 | 0.8649 | 0.8599 | 0.9780 |
| 0.0353 | 2.7586 | 720 | 0.0707 | 0.8549 | 0.8653 | 0.8601 | 0.9794 |
| 0.0325 | 2.8352 | 740 | 0.0717 | 0.8595 | 0.8686 | 0.8640 | 0.9797 |
| 0.0337 | 2.9119 | 760 | 0.0752 | 0.8650 | 0.8761 | 0.8705 | 0.9803 |
| 0.0405 | 2.9885 | 780 | 0.0698 | 0.8623 | 0.8720 | 0.8671 | 0.9799 |
| 0.0407 | 3.0651 | 800 | 0.0805 | 0.8557 | 0.8791 | 0.8673 | 0.9817 |
| 0.0288 | 3.1418 | 820 | 0.0691 | 0.8753 | 0.8821 | 0.8787 | 0.9807 |
| 0.0277 | 3.2184 | 840 | 0.0829 | 0.8588 | 0.8775 | 0.8681 | 0.9799 |
| 0.0264 | 3.2950 | 860 | 0.0725 | 0.8699 | 0.8773 | 0.8736 | 0.9801 |
| 0.0248 | 3.3716 | 880 | 0.0749 | 0.8568 | 0.8716 | 0.8641 | 0.9794 |
| 0.0267 | 3.4483 | 900 | 0.0740 | 0.8587 | 0.8716 | 0.8651 | 0.9793 |
| 0.0295 | 3.5249 | 920 | 0.0701 | 0.8691 | 0.8833 | 0.8761 | 0.9812 |
| 0.0231 | 3.6015 | 940 | 0.0704 | 0.8710 | 0.8794 | 0.8751 | 0.9815 |
| 0.0256 | 3.6782 | 960 | 0.0722 | 0.8758 | 0.8865 | 0.8811 | 0.9814 |
| 0.0223 | 3.7548 | 980 | 0.0721 | 0.8756 | 0.8849 | 0.8802 | 0.9807 |
| 0.0312 | 3.8314 | 1000 | 0.0773 | 0.8667 | 0.8736 | 0.8701 | 0.9802 |
| 0.0275 | 3.9080 | 1020 | 0.0744 | 0.8655 | 0.8766 | 0.8710 | 0.9800 |
| 0.0323 | 3.9847 | 1040 | 0.0738 | 0.8819 | 0.8819 | 0.8819 | 0.9813 |
| 0.0193 | 4.0613 | 1060 | 0.0772 | 0.8787 | 0.8853 | 0.8820 | 0.9811 |
| 0.0213 | 4.1379 | 1080 | 0.0806 | 0.8778 | 0.8801 | 0.8789 | 0.9812 |
| 0.0177 | 4.2146 | 1100 | 0.0785 | 0.8723 | 0.8789 | 0.8756 | 0.9807 |
| 0.0193 | 4.2912 | 1120 | 0.0808 | 0.8764 | 0.8817 | 0.8790 | 0.9810 |
| 0.0191 | 4.3678 | 1140 | 0.0728 | 0.8783 | 0.8936 | 0.8859 | 0.9816 |
| 0.0222 | 4.4444 | 1160 | 0.0772 | 0.8754 | 0.8830 | 0.8792 | 0.9811 |
| 0.0189 | 4.5211 | 1180 | 0.0763 | 0.8795 | 0.8892 | 0.8844 | 0.9812 |
| 0.0185 | 4.5977 | 1200 | 0.0793 | 0.8808 | 0.8895 | 0.8851 | 0.9820 |
| 0.0195 | 4.6743 | 1220 | 0.0796 | 0.8783 | 0.8874 | 0.8828 | 0.9820 |
| 0.0248 | 4.7510 | 1240 | 0.0765 | 0.8659 | 0.8711 | 0.8685 | 0.9805 |
| 0.0205 | 4.8276 | 1260 | 0.0786 | 0.8667 | 0.8693 | 0.8680 | 0.9801 |
| 0.0186 | 4.9042 | 1280 | 0.0799 | 0.8684 | 0.875 | 0.8717 | 0.9804 |
| 0.0162 | 4.9808 | 1300 | 0.0780 | 0.8824 | 0.8828 | 0.8826 | 0.9816 |
| 0.0147 | 5.0575 | 1320 | 0.0787 | 0.8767 | 0.8869 | 0.8818 | 0.9819 |
| 0.013 | 5.1341 | 1340 | 0.0823 | 0.8777 | 0.8837 | 0.8807 | 0.9811 |
| 0.0126 | 5.2107 | 1360 | 0.0826 | 0.8796 | 0.8849 | 0.8822 | 0.9811 |
| 0.0149 | 5.2874 | 1380 | 0.0869 | 0.8783 | 0.8771 | 0.8777 | 0.9803 |
| 0.0126 | 5.3640 | 1400 | 0.0859 | 0.8708 | 0.875 | 0.8729 | 0.9804 |
| 0.0158 | 5.4406 | 1420 | 0.0842 | 0.8738 | 0.8782 | 0.8760 | 0.9802 |
| 0.0135 | 5.5172 | 1440 | 0.0839 | 0.8777 | 0.8805 | 0.8791 | 0.9806 |
| 0.0173 | 5.5939 | 1460 | 0.0866 | 0.8711 | 0.8761 | 0.8736 | 0.9800 |
| 0.0124 | 5.6705 | 1480 | 0.0831 | 0.8715 | 0.8819 | 0.8767 | 0.9807 |
| 0.012 | 5.7471 | 1500 | 0.0827 | 0.8801 | 0.8858 | 0.8830 | 0.9813 |
| 0.0147 | 5.8238 | 1520 | 0.0825 | 0.8784 | 0.8847 | 0.8815 | 0.9809 |
| 0.0154 | 5.9004 | 1540 | 0.0827 | 0.8771 | 0.8810 | 0.8791 | 0.9808 |
| 0.0101 | 5.9770 | 1560 | 0.0833 | 0.8779 | 0.8842 | 0.8811 | 0.9812 |
| 0.008 | 6.0536 | 1580 | 0.0883 | 0.8782 | 0.8833 | 0.8807 | 0.9810 |
| 0.0096 | 6.1303 | 1600 | 0.0875 | 0.8820 | 0.8849 | 0.8835 | 0.9810 |
| 0.0113 | 6.2069 | 1620 | 0.0893 | 0.8816 | 0.8844 | 0.8830 | 0.9810 |
| 0.0126 | 6.2835 | 1640 | 0.0841 | 0.8846 | 0.8892 | 0.8869 | 0.9817 |
| 0.0115 | 6.3602 | 1660 | 0.0825 | 0.8861 | 0.8865 | 0.8863 | 0.9814 |
| 0.0108 | 6.4368 | 1680 | 0.0855 | 0.8828 | 0.8881 | 0.8855 | 0.9814 |
| 0.0089 | 6.5134 | 1700 | 0.0845 | 0.8803 | 0.8874 | 0.8839 | 0.9813 |
| 0.0132 | 6.5900 | 1720 | 0.0829 | 0.8827 | 0.8867 | 0.8847 | 0.9814 |
| 0.0094 | 6.6667 | 1740 | 0.0848 | 0.8833 | 0.8853 | 0.8843 | 0.9814 |
| 0.0091 | 6.7433 | 1760 | 0.0853 | 0.8826 | 0.8849 | 0.8838 | 0.9813 |
| 0.0104 | 6.8199 | 1780 | 0.0862 | 0.8820 | 0.8849 | 0.8835 | 0.9812 |
| 0.0103 | 6.8966 | 1800 | 0.0863 | 0.8818 | 0.8844 | 0.8831 | 0.9813 |
| 0.0075 | 6.9732 | 1820 | 0.0862 | 0.8825 | 0.8853 | 0.8839 | 0.9813 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0 |