--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: scenario-non-kd-scr-ner-half-xlmr_data-univner_full66 results: [] --- # scenario-non-kd-scr-ner-half-xlmr_data-univner_full66 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: 0.3523 - Precision: 0.5419 - Recall: 0.5460 - F1: 0.5439 - Accuracy: 0.9571 ## 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: 66 - 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 | |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3585 | 0.2910 | 500 | 0.2974 | 0.4893 | 0.0856 | 0.1456 | 0.9279 | | 0.2746 | 0.5821 | 1000 | 0.2792 | 0.4135 | 0.1262 | 0.1934 | 0.9310 | | 0.2447 | 0.8731 | 1500 | 0.2385 | 0.3118 | 0.2189 | 0.2572 | 0.9345 | | 0.2219 | 1.1641 | 2000 | 0.2317 | 0.3320 | 0.2402 | 0.2788 | 0.9370 | | 0.2027 | 1.4552 | 2500 | 0.2292 | 0.3533 | 0.2369 | 0.2836 | 0.9382 | | 0.1957 | 1.7462 | 3000 | 0.2157 | 0.3664 | 0.2812 | 0.3182 | 0.9398 | | 0.1835 | 2.0373 | 3500 | 0.2089 | 0.3606 | 0.3528 | 0.3566 | 0.9398 | | 0.1641 | 2.3283 | 4000 | 0.2153 | 0.3493 | 0.3210 | 0.3346 | 0.9382 | | 0.1642 | 2.6193 | 4500 | 0.2119 | 0.3507 | 0.3284 | 0.3392 | 0.9398 | | 0.1638 | 2.9104 | 5000 | 0.1977 | 0.3630 | 0.3696 | 0.3663 | 0.9419 | | 0.144 | 3.2014 | 5500 | 0.2035 | 0.3921 | 0.3922 | 0.3921 | 0.9420 | | 0.1408 | 3.4924 | 6000 | 0.2045 | 0.4286 | 0.3652 | 0.3944 | 0.9440 | | 0.1362 | 3.7835 | 6500 | 0.2080 | 0.4012 | 0.3520 | 0.3750 | 0.9439 | | 0.1359 | 4.0745 | 7000 | 0.2061 | 0.4102 | 0.3949 | 0.4024 | 0.9437 | | 0.1199 | 4.3655 | 7500 | 0.2102 | 0.4082 | 0.3854 | 0.3964 | 0.9445 | | 0.1226 | 4.6566 | 8000 | 0.2082 | 0.4303 | 0.3819 | 0.4047 | 0.9452 | | 0.1217 | 4.9476 | 8500 | 0.2006 | 0.4307 | 0.3947 | 0.4120 | 0.9463 | | 0.1079 | 5.2386 | 9000 | 0.2079 | 0.4370 | 0.4158 | 0.4261 | 0.9468 | | 0.1037 | 5.5297 | 9500 | 0.2046 | 0.4334 | 0.4171 | 0.4251 | 0.9472 | | 0.0998 | 5.8207 | 10000 | 0.1919 | 0.4363 | 0.4564 | 0.4461 | 0.9466 | | 0.0938 | 6.1118 | 10500 | 0.2085 | 0.4477 | 0.4333 | 0.4404 | 0.9477 | | 0.0858 | 6.4028 | 11000 | 0.2107 | 0.4499 | 0.4577 | 0.4537 | 0.9487 | | 0.0827 | 6.6938 | 11500 | 0.2065 | 0.4394 | 0.4810 | 0.4593 | 0.9484 | | 0.0845 | 6.9849 | 12000 | 0.2059 | 0.4668 | 0.4497 | 0.4581 | 0.9500 | | 0.0698 | 7.2759 | 12500 | 0.2204 | 0.4713 | 0.4546 | 0.4628 | 0.9506 | | 0.0677 | 7.5669 | 13000 | 0.2104 | 0.4597 | 0.4779 | 0.4686 | 0.9499 | | 0.0687 | 7.8580 | 13500 | 0.2106 | 0.4765 | 0.4858 | 0.4811 | 0.9515 | | 0.0628 | 8.1490 | 14000 | 0.2160 | 0.4793 | 0.4882 | 0.4837 | 0.9524 | | 0.0543 | 8.4400 | 14500 | 0.2129 | 0.4710 | 0.5076 | 0.4886 | 0.9509 | | 0.0551 | 8.7311 | 15000 | 0.2190 | 0.4965 | 0.4874 | 0.4919 | 0.9525 | | 0.0556 | 9.0221 | 15500 | 0.2149 | 0.5022 | 0.4999 | 0.5011 | 0.9532 | | 0.0455 | 9.3132 | 16000 | 0.2233 | 0.5080 | 0.5126 | 0.5103 | 0.9537 | | 0.0453 | 9.6042 | 16500 | 0.2235 | 0.4936 | 0.5167 | 0.5049 | 0.9532 | | 0.0459 | 9.8952 | 17000 | 0.2256 | 0.5156 | 0.5044 | 0.5100 | 0.9535 | | 0.0403 | 10.1863 | 17500 | 0.2380 | 0.5120 | 0.5090 | 0.5105 | 0.9542 | | 0.0378 | 10.4773 | 18000 | 0.2332 | 0.4895 | 0.5243 | 0.5063 | 0.9532 | | 0.0373 | 10.7683 | 18500 | 0.2402 | 0.5136 | 0.5139 | 0.5138 | 0.9542 | | 0.0383 | 11.0594 | 19000 | 0.2437 | 0.5165 | 0.5092 | 0.5128 | 0.9546 | | 0.033 | 11.3504 | 19500 | 0.2444 | 0.4994 | 0.5305 | 0.5145 | 0.9540 | | 0.0324 | 11.6414 | 20000 | 0.2489 | 0.5093 | 0.5194 | 0.5143 | 0.9542 | | 0.0313 | 11.9325 | 20500 | 0.2462 | 0.4995 | 0.5354 | 0.5168 | 0.9542 | | 0.0283 | 12.2235 | 21000 | 0.2513 | 0.5042 | 0.5340 | 0.5187 | 0.9540 | | 0.0274 | 12.5146 | 21500 | 0.2575 | 0.5023 | 0.5356 | 0.5184 | 0.9539 | | 0.0271 | 12.8056 | 22000 | 0.2670 | 0.5201 | 0.5210 | 0.5205 | 0.9551 | | 0.026 | 13.0966 | 22500 | 0.2643 | 0.5095 | 0.5198 | 0.5146 | 0.9543 | | 0.0231 | 13.3877 | 23000 | 0.2650 | 0.5129 | 0.5348 | 0.5237 | 0.9546 | | 0.0241 | 13.6787 | 23500 | 0.2675 | 0.5305 | 0.5308 | 0.5307 | 0.9554 | | 0.0238 | 13.9697 | 24000 | 0.2718 | 0.5249 | 0.5139 | 0.5194 | 0.9552 | | 0.0213 | 14.2608 | 24500 | 0.2713 | 0.5141 | 0.5273 | 0.5206 | 0.9547 | | 0.0197 | 14.5518 | 25000 | 0.2726 | 0.5235 | 0.5304 | 0.5269 | 0.9557 | | 0.0205 | 14.8428 | 25500 | 0.2745 | 0.5229 | 0.5359 | 0.5293 | 0.9558 | | 0.0193 | 15.1339 | 26000 | 0.2829 | 0.5130 | 0.5347 | 0.5236 | 0.9552 | | 0.0177 | 15.4249 | 26500 | 0.2794 | 0.5175 | 0.5370 | 0.5271 | 0.9555 | | 0.0175 | 15.7159 | 27000 | 0.2831 | 0.5369 | 0.5275 | 0.5321 | 0.9559 | | 0.0166 | 16.0070 | 27500 | 0.2869 | 0.5280 | 0.5276 | 0.5278 | 0.9556 | | 0.0143 | 16.2980 | 28000 | 0.2919 | 0.5234 | 0.5363 | 0.5298 | 0.9561 | | 0.0155 | 16.5891 | 28500 | 0.2957 | 0.5242 | 0.5392 | 0.5316 | 0.9558 | | 0.0159 | 16.8801 | 29000 | 0.2924 | 0.5148 | 0.5561 | 0.5346 | 0.9556 | | 0.0132 | 17.1711 | 29500 | 0.2929 | 0.5217 | 0.5490 | 0.5350 | 0.9559 | | 0.0141 | 17.4622 | 30000 | 0.3024 | 0.5302 | 0.5340 | 0.5321 | 0.9563 | | 0.0136 | 17.7532 | 30500 | 0.3012 | 0.5339 | 0.5314 | 0.5326 | 0.9563 | | 0.0128 | 18.0442 | 31000 | 0.3084 | 0.5364 | 0.5343 | 0.5353 | 0.9565 | | 0.0117 | 18.3353 | 31500 | 0.3098 | 0.5451 | 0.5219 | 0.5332 | 0.9562 | | 0.0115 | 18.6263 | 32000 | 0.3144 | 0.5454 | 0.5214 | 0.5332 | 0.9563 | | 0.0121 | 18.9173 | 32500 | 0.3112 | 0.5403 | 0.5324 | 0.5363 | 0.9565 | | 0.01 | 19.2084 | 33000 | 0.3180 | 0.5348 | 0.5400 | 0.5374 | 0.9564 | | 0.0111 | 19.4994 | 33500 | 0.3123 | 0.5348 | 0.5395 | 0.5371 | 0.9565 | | 0.0105 | 19.7905 | 34000 | 0.3103 | 0.5309 | 0.5458 | 0.5382 | 0.9562 | | 0.0101 | 20.0815 | 34500 | 0.3217 | 0.5326 | 0.5302 | 0.5314 | 0.9561 | | 0.0095 | 20.3725 | 35000 | 0.3184 | 0.5190 | 0.5510 | 0.5345 | 0.9561 | | 0.0093 | 20.6636 | 35500 | 0.3201 | 0.5386 | 0.5514 | 0.5449 | 0.9566 | | 0.0093 | 20.9546 | 36000 | 0.3246 | 0.5428 | 0.5464 | 0.5446 | 0.9569 | | 0.0085 | 21.2456 | 36500 | 0.3209 | 0.5421 | 0.5523 | 0.5471 | 0.9567 | | 0.0078 | 21.5367 | 37000 | 0.3293 | 0.5353 | 0.5356 | 0.5354 | 0.9567 | | 0.0091 | 21.8277 | 37500 | 0.3241 | 0.5348 | 0.5470 | 0.5408 | 0.9564 | | 0.0077 | 22.1187 | 38000 | 0.3336 | 0.5410 | 0.5434 | 0.5422 | 0.9568 | | 0.0064 | 22.4098 | 38500 | 0.3349 | 0.5376 | 0.5412 | 0.5394 | 0.9566 | | 0.0079 | 22.7008 | 39000 | 0.3343 | 0.5329 | 0.5473 | 0.5400 | 0.9566 | | 0.0081 | 22.9919 | 39500 | 0.3335 | 0.5377 | 0.5493 | 0.5434 | 0.9567 | | 0.0071 | 23.2829 | 40000 | 0.3370 | 0.5427 | 0.5442 | 0.5435 | 0.9569 | | 0.0062 | 23.5739 | 40500 | 0.3350 | 0.5296 | 0.5537 | 0.5414 | 0.9566 | | 0.0069 | 23.8650 | 41000 | 0.3383 | 0.5400 | 0.5399 | 0.5399 | 0.9569 | | 0.0071 | 24.1560 | 41500 | 0.3420 | 0.5356 | 0.5387 | 0.5372 | 0.9567 | | 0.0057 | 24.4470 | 42000 | 0.3418 | 0.5350 | 0.5439 | 0.5394 | 0.9567 | | 0.0065 | 24.7381 | 42500 | 0.3442 | 0.5375 | 0.5434 | 0.5404 | 0.9568 | | 0.0063 | 25.0291 | 43000 | 0.3467 | 0.5441 | 0.5373 | 0.5407 | 0.9568 | | 0.0051 | 25.3201 | 43500 | 0.3440 | 0.5312 | 0.5511 | 0.5410 | 0.9566 | | 0.0063 | 25.6112 | 44000 | 0.3452 | 0.5333 | 0.5475 | 0.5403 | 0.9569 | | 0.0062 | 25.9022 | 44500 | 0.3490 | 0.5405 | 0.5432 | 0.5418 | 0.9568 | | 0.005 | 26.1932 | 45000 | 0.3465 | 0.5365 | 0.5467 | 0.5416 | 0.9568 | | 0.0054 | 26.4843 | 45500 | 0.3477 | 0.5437 | 0.5425 | 0.5431 | 0.9569 | | 0.0046 | 26.7753 | 46000 | 0.3462 | 0.5413 | 0.5431 | 0.5422 | 0.9568 | | 0.006 | 27.0664 | 46500 | 0.3479 | 0.5466 | 0.5530 | 0.5498 | 0.9574 | | 0.0049 | 27.3574 | 47000 | 0.3515 | 0.5413 | 0.5434 | 0.5423 | 0.9569 | | 0.0046 | 27.6484 | 47500 | 0.3507 | 0.5399 | 0.5448 | 0.5423 | 0.9570 | | 0.0053 | 27.9395 | 48000 | 0.3497 | 0.541 | 0.5464 | 0.5437 | 0.9569 | | 0.0049 | 28.2305 | 48500 | 0.3527 | 0.5354 | 0.5480 | 0.5416 | 0.9568 | | 0.0048 | 28.5215 | 49000 | 0.3554 | 0.5449 | 0.5442 | 0.5445 | 0.9572 | | 0.0049 | 28.8126 | 49500 | 0.3532 | 0.5413 | 0.5473 | 0.5443 | 0.9571 | | 0.0041 | 29.1036 | 50000 | 0.3531 | 0.54 | 0.5454 | 0.5427 | 0.9569 | | 0.0043 | 29.3946 | 50500 | 0.3524 | 0.5414 | 0.5483 | 0.5448 | 0.9571 | | 0.0048 | 29.6857 | 51000 | 0.3519 | 0.5409 | 0.5480 | 0.5444 | 0.9571 | | 0.0041 | 29.9767 | 51500 | 0.3523 | 0.5419 | 0.5460 | 0.5439 | 0.9571 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1