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---
base_model: FacebookAI/xlm-roberta-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-scr-ner-full-xlmr_data-univner_full44
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. -->
# scenario-non-kd-scr-ner-full-xlmr_data-univner_full44
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3854
- Precision: 0.5785
- Recall: 0.5835
- F1: 0.5810
- Accuracy: 0.9605
## 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: 32
- eval_batch_size: 32
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.338 | 0.2910 | 500 | 0.2757 | 0.375 | 0.1450 | 0.2091 | 0.9304 |
| 0.2636 | 0.5821 | 1000 | 0.2477 | 0.2864 | 0.2401 | 0.2612 | 0.9323 |
| 0.2294 | 0.8731 | 1500 | 0.2256 | 0.3407 | 0.2946 | 0.3160 | 0.9369 |
| 0.2017 | 1.1641 | 2000 | 0.2149 | 0.3468 | 0.3505 | 0.3486 | 0.9379 |
| 0.1872 | 1.4552 | 2500 | 0.2112 | 0.3721 | 0.3305 | 0.3501 | 0.9387 |
| 0.1753 | 1.7462 | 3000 | 0.2087 | 0.3904 | 0.3083 | 0.3445 | 0.9423 |
| 0.1623 | 2.0373 | 3500 | 0.1986 | 0.3818 | 0.3929 | 0.3873 | 0.9417 |
| 0.1324 | 2.3283 | 4000 | 0.1986 | 0.4314 | 0.4066 | 0.4186 | 0.9458 |
| 0.1307 | 2.6193 | 4500 | 0.1901 | 0.4309 | 0.4376 | 0.4342 | 0.9474 |
| 0.1199 | 2.9104 | 5000 | 0.1789 | 0.4454 | 0.4336 | 0.4394 | 0.9485 |
| 0.0995 | 3.2014 | 5500 | 0.1831 | 0.4761 | 0.4709 | 0.4735 | 0.9514 |
| 0.0895 | 3.4924 | 6000 | 0.1899 | 0.4646 | 0.5120 | 0.4872 | 0.9515 |
| 0.0827 | 3.7835 | 6500 | 0.1757 | 0.5053 | 0.5158 | 0.5105 | 0.9535 |
| 0.0761 | 4.0745 | 7000 | 0.2069 | 0.5092 | 0.4826 | 0.4956 | 0.9539 |
| 0.0616 | 4.3655 | 7500 | 0.1927 | 0.5246 | 0.5012 | 0.5127 | 0.9548 |
| 0.0609 | 4.6566 | 8000 | 0.1891 | 0.5251 | 0.4911 | 0.5076 | 0.9547 |
| 0.0577 | 4.9476 | 8500 | 0.1873 | 0.4978 | 0.5341 | 0.5153 | 0.9546 |
| 0.0451 | 5.2386 | 9000 | 0.2040 | 0.5239 | 0.5302 | 0.5270 | 0.9555 |
| 0.0419 | 5.5297 | 9500 | 0.2065 | 0.5249 | 0.5195 | 0.5222 | 0.9555 |
| 0.0433 | 5.8207 | 10000 | 0.2139 | 0.5171 | 0.5493 | 0.5327 | 0.9556 |
| 0.0392 | 6.1118 | 10500 | 0.2184 | 0.5140 | 0.5578 | 0.5350 | 0.9557 |
| 0.0308 | 6.4028 | 11000 | 0.2159 | 0.5110 | 0.5582 | 0.5335 | 0.9559 |
| 0.0312 | 6.6938 | 11500 | 0.2202 | 0.4900 | 0.5969 | 0.5382 | 0.9541 |
| 0.0296 | 6.9849 | 12000 | 0.2288 | 0.5260 | 0.5260 | 0.5260 | 0.9567 |
| 0.024 | 7.2759 | 12500 | 0.2368 | 0.5330 | 0.5667 | 0.5493 | 0.9572 |
| 0.0232 | 7.5669 | 13000 | 0.2438 | 0.5247 | 0.5399 | 0.5322 | 0.9565 |
| 0.0222 | 7.8580 | 13500 | 0.2483 | 0.5643 | 0.5266 | 0.5448 | 0.9573 |
| 0.0187 | 8.1490 | 14000 | 0.2476 | 0.5615 | 0.5333 | 0.5470 | 0.9567 |
| 0.0176 | 8.4400 | 14500 | 0.2473 | 0.5494 | 0.5445 | 0.5470 | 0.9571 |
| 0.0176 | 8.7311 | 15000 | 0.2452 | 0.5346 | 0.5715 | 0.5524 | 0.9570 |
| 0.0159 | 9.0221 | 15500 | 0.2794 | 0.5324 | 0.5569 | 0.5444 | 0.9577 |
| 0.0131 | 9.3132 | 16000 | 0.2672 | 0.5424 | 0.5917 | 0.5660 | 0.9582 |
| 0.0132 | 9.6042 | 16500 | 0.2716 | 0.5287 | 0.5566 | 0.5423 | 0.9572 |
| 0.0133 | 9.8952 | 17000 | 0.2668 | 0.5267 | 0.5669 | 0.5461 | 0.9569 |
| 0.0113 | 10.1863 | 17500 | 0.2779 | 0.5369 | 0.5770 | 0.5562 | 0.9578 |
| 0.0094 | 10.4773 | 18000 | 0.2717 | 0.5380 | 0.5881 | 0.5619 | 0.9578 |
| 0.0111 | 10.7683 | 18500 | 0.2861 | 0.5582 | 0.5465 | 0.5523 | 0.9587 |
| 0.0094 | 11.0594 | 19000 | 0.2803 | 0.5365 | 0.5833 | 0.5589 | 0.9582 |
| 0.008 | 11.3504 | 19500 | 0.2853 | 0.5262 | 0.5755 | 0.5498 | 0.9575 |
| 0.0077 | 11.6414 | 20000 | 0.2893 | 0.5366 | 0.5806 | 0.5577 | 0.9579 |
| 0.0083 | 11.9325 | 20500 | 0.2898 | 0.5415 | 0.5923 | 0.5657 | 0.9584 |
| 0.0067 | 12.2235 | 21000 | 0.3000 | 0.5635 | 0.5419 | 0.5525 | 0.9582 |
| 0.0066 | 12.5146 | 21500 | 0.3046 | 0.5574 | 0.5643 | 0.5608 | 0.9587 |
| 0.0065 | 12.8056 | 22000 | 0.3063 | 0.5495 | 0.5748 | 0.5619 | 0.9587 |
| 0.0062 | 13.0966 | 22500 | 0.3147 | 0.5619 | 0.5575 | 0.5597 | 0.9585 |
| 0.0056 | 13.3877 | 23000 | 0.3033 | 0.5440 | 0.5836 | 0.5631 | 0.9586 |
| 0.005 | 13.6787 | 23500 | 0.3083 | 0.5567 | 0.5741 | 0.5653 | 0.9585 |
| 0.0051 | 13.9697 | 24000 | 0.3201 | 0.5510 | 0.5891 | 0.5694 | 0.9592 |
| 0.0041 | 14.2608 | 24500 | 0.3265 | 0.5445 | 0.5687 | 0.5563 | 0.9586 |
| 0.0043 | 14.5518 | 25000 | 0.3202 | 0.5634 | 0.5641 | 0.5638 | 0.9586 |
| 0.0045 | 14.8428 | 25500 | 0.3200 | 0.5704 | 0.5677 | 0.5691 | 0.9597 |
| 0.0042 | 15.1339 | 26000 | 0.3285 | 0.5651 | 0.5770 | 0.5710 | 0.9595 |
| 0.0035 | 15.4249 | 26500 | 0.3259 | 0.5575 | 0.5846 | 0.5707 | 0.9590 |
| 0.0038 | 15.7159 | 27000 | 0.3300 | 0.5711 | 0.5685 | 0.5698 | 0.9595 |
| 0.0034 | 16.0070 | 27500 | 0.3244 | 0.5552 | 0.5771 | 0.5660 | 0.9586 |
| 0.003 | 16.2980 | 28000 | 0.3310 | 0.5683 | 0.5778 | 0.5730 | 0.9596 |
| 0.0031 | 16.5891 | 28500 | 0.3295 | 0.5629 | 0.5800 | 0.5713 | 0.9595 |
| 0.0029 | 16.8801 | 29000 | 0.3302 | 0.5396 | 0.5992 | 0.5679 | 0.9584 |
| 0.0028 | 17.1711 | 29500 | 0.3350 | 0.5519 | 0.5826 | 0.5668 | 0.9592 |
| 0.0029 | 17.4622 | 30000 | 0.3269 | 0.5360 | 0.6158 | 0.5732 | 0.9580 |
| 0.0023 | 17.7532 | 30500 | 0.3400 | 0.5731 | 0.5801 | 0.5766 | 0.9598 |
| 0.0022 | 18.0442 | 31000 | 0.3345 | 0.5716 | 0.5649 | 0.5682 | 0.9593 |
| 0.0022 | 18.3353 | 31500 | 0.3301 | 0.5589 | 0.5966 | 0.5771 | 0.9594 |
| 0.002 | 18.6263 | 32000 | 0.3406 | 0.5702 | 0.5774 | 0.5738 | 0.9596 |
| 0.0025 | 18.9173 | 32500 | 0.3422 | 0.5943 | 0.5380 | 0.5647 | 0.9595 |
| 0.0019 | 19.2084 | 33000 | 0.3476 | 0.5783 | 0.5728 | 0.5755 | 0.9600 |
| 0.0019 | 19.4994 | 33500 | 0.3449 | 0.5620 | 0.5910 | 0.5761 | 0.9596 |
| 0.0016 | 19.7905 | 34000 | 0.3518 | 0.5634 | 0.5926 | 0.5776 | 0.9595 |
| 0.0014 | 20.0815 | 34500 | 0.3522 | 0.5633 | 0.5820 | 0.5725 | 0.9598 |
| 0.0014 | 20.3725 | 35000 | 0.3486 | 0.5744 | 0.5739 | 0.5742 | 0.9598 |
| 0.0014 | 20.6636 | 35500 | 0.3513 | 0.5762 | 0.5737 | 0.5749 | 0.9596 |
| 0.0013 | 20.9546 | 36000 | 0.3608 | 0.5406 | 0.5851 | 0.5619 | 0.9586 |
| 0.0015 | 21.2456 | 36500 | 0.3548 | 0.5814 | 0.5696 | 0.5754 | 0.9602 |
| 0.0011 | 21.5367 | 37000 | 0.3552 | 0.5829 | 0.5700 | 0.5764 | 0.9600 |
| 0.0013 | 21.8277 | 37500 | 0.3565 | 0.5623 | 0.5797 | 0.5709 | 0.9593 |
| 0.0014 | 22.1187 | 38000 | 0.3608 | 0.5791 | 0.5693 | 0.5742 | 0.9603 |
| 0.001 | 22.4098 | 38500 | 0.3534 | 0.5706 | 0.5914 | 0.5808 | 0.9601 |
| 0.001 | 22.7008 | 39000 | 0.3639 | 0.5887 | 0.5719 | 0.5802 | 0.9603 |
| 0.0009 | 22.9919 | 39500 | 0.3650 | 0.5679 | 0.5898 | 0.5787 | 0.9597 |
| 0.0008 | 23.2829 | 40000 | 0.3676 | 0.5815 | 0.5744 | 0.5779 | 0.9602 |
| 0.0007 | 23.5739 | 40500 | 0.3738 | 0.5944 | 0.5680 | 0.5809 | 0.9606 |
| 0.001 | 23.8650 | 41000 | 0.3700 | 0.5804 | 0.5735 | 0.5769 | 0.9597 |
| 0.0009 | 24.1560 | 41500 | 0.3706 | 0.5774 | 0.5768 | 0.5771 | 0.9601 |
| 0.0008 | 24.4470 | 42000 | 0.3696 | 0.5838 | 0.5731 | 0.5784 | 0.9604 |
| 0.0007 | 24.7381 | 42500 | 0.3737 | 0.5656 | 0.5862 | 0.5757 | 0.9598 |
| 0.0007 | 25.0291 | 43000 | 0.3756 | 0.5617 | 0.5871 | 0.5741 | 0.9594 |
| 0.0006 | 25.3201 | 43500 | 0.3757 | 0.5668 | 0.5885 | 0.5774 | 0.9593 |
| 0.0004 | 25.6112 | 44000 | 0.3783 | 0.5865 | 0.5708 | 0.5785 | 0.9602 |
| 0.0006 | 25.9022 | 44500 | 0.3688 | 0.5724 | 0.5902 | 0.5812 | 0.9600 |
| 0.0004 | 26.1932 | 45000 | 0.3783 | 0.5851 | 0.5787 | 0.5819 | 0.9605 |
| 0.0004 | 26.4843 | 45500 | 0.3809 | 0.5773 | 0.5780 | 0.5776 | 0.9601 |
| 0.0004 | 26.7753 | 46000 | 0.3816 | 0.5823 | 0.5803 | 0.5813 | 0.9605 |
| 0.0004 | 27.0664 | 46500 | 0.3887 | 0.5762 | 0.5820 | 0.5791 | 0.9601 |
| 0.0003 | 27.3574 | 47000 | 0.3833 | 0.5834 | 0.5869 | 0.5852 | 0.9605 |
| 0.0004 | 27.6484 | 47500 | 0.3867 | 0.5791 | 0.5891 | 0.5840 | 0.9605 |
| 0.0004 | 27.9395 | 48000 | 0.3876 | 0.5814 | 0.5856 | 0.5835 | 0.9605 |
| 0.0003 | 28.2305 | 48500 | 0.3918 | 0.5810 | 0.5742 | 0.5776 | 0.9605 |
| 0.0003 | 28.5215 | 49000 | 0.3869 | 0.5804 | 0.5826 | 0.5815 | 0.9606 |
| 0.0003 | 28.8126 | 49500 | 0.3864 | 0.5760 | 0.5871 | 0.5815 | 0.9604 |
| 0.0004 | 29.1036 | 50000 | 0.3840 | 0.5732 | 0.5887 | 0.5808 | 0.9604 |
| 0.0003 | 29.3946 | 50500 | 0.3864 | 0.5833 | 0.5784 | 0.5808 | 0.9606 |
| 0.0002 | 29.6857 | 51000 | 0.3852 | 0.5781 | 0.5842 | 0.5811 | 0.9604 |
| 0.0002 | 29.9767 | 51500 | 0.3854 | 0.5785 | 0.5835 | 0.5810 | 0.9605 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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