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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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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: scenario-non-kd-scr-ner-half-xlmr_data-univner_full66 |
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results: [] |
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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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# scenario-non-kd-scr-ner-half-xlmr_data-univner_full66 |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3518 |
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- Precision: 0.5316 |
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- Recall: 0.5425 |
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- F1: 0.5370 |
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- Accuracy: 0.9565 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 66 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.3589 | 0.2910 | 500 | 0.2912 | 0.4556 | 0.0998 | 0.1638 | 0.9285 | |
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| 0.2772 | 0.5821 | 1000 | 0.2813 | 0.4297 | 0.1296 | 0.1991 | 0.9314 | |
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| 0.2452 | 0.8731 | 1500 | 0.2362 | 0.3233 | 0.2021 | 0.2487 | 0.9349 | |
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| 0.2225 | 1.1641 | 2000 | 0.2304 | 0.3518 | 0.2447 | 0.2886 | 0.9377 | |
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| 0.2028 | 1.4552 | 2500 | 0.2316 | 0.3541 | 0.2378 | 0.2845 | 0.9385 | |
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| 0.1966 | 1.7462 | 3000 | 0.2155 | 0.3847 | 0.2730 | 0.3194 | 0.9401 | |
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| 0.1841 | 2.0373 | 3500 | 0.2109 | 0.3783 | 0.3202 | 0.3468 | 0.9408 | |
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| 0.1651 | 2.3283 | 4000 | 0.2105 | 0.3801 | 0.3388 | 0.3582 | 0.9404 | |
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| 0.1644 | 2.6193 | 4500 | 0.2056 | 0.3732 | 0.3506 | 0.3616 | 0.9417 | |
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| 0.1635 | 2.9104 | 5000 | 0.1966 | 0.3919 | 0.3705 | 0.3809 | 0.9429 | |
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| 0.1441 | 3.2014 | 5500 | 0.2022 | 0.4122 | 0.3985 | 0.4052 | 0.9432 | |
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| 0.1418 | 3.4924 | 6000 | 0.2040 | 0.4160 | 0.3760 | 0.3950 | 0.9443 | |
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| 0.1363 | 3.7835 | 6500 | 0.2053 | 0.4157 | 0.3682 | 0.3905 | 0.9446 | |
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| 0.1367 | 4.0745 | 7000 | 0.2032 | 0.4182 | 0.3929 | 0.4051 | 0.9448 | |
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| 0.1203 | 4.3655 | 7500 | 0.2053 | 0.4173 | 0.4007 | 0.4088 | 0.9448 | |
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| 0.1222 | 4.6566 | 8000 | 0.2040 | 0.4337 | 0.3936 | 0.4127 | 0.9459 | |
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| 0.1215 | 4.9476 | 8500 | 0.1982 | 0.4271 | 0.4227 | 0.4249 | 0.9455 | |
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| 0.1072 | 5.2386 | 9000 | 0.2090 | 0.4375 | 0.4162 | 0.4266 | 0.9470 | |
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| 0.1035 | 5.5297 | 9500 | 0.2049 | 0.4449 | 0.4233 | 0.4338 | 0.9477 | |
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| 0.0988 | 5.8207 | 10000 | 0.1970 | 0.4446 | 0.4558 | 0.4501 | 0.9469 | |
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| 0.0935 | 6.1118 | 10500 | 0.2087 | 0.4576 | 0.4489 | 0.4532 | 0.9485 | |
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| 0.0847 | 6.4028 | 11000 | 0.2098 | 0.4410 | 0.4639 | 0.4521 | 0.9487 | |
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| 0.0811 | 6.6938 | 11500 | 0.2072 | 0.4662 | 0.4699 | 0.4680 | 0.9506 | |
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| 0.0828 | 6.9849 | 12000 | 0.1986 | 0.4947 | 0.4657 | 0.4798 | 0.9510 | |
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| 0.0681 | 7.2759 | 12500 | 0.2098 | 0.4742 | 0.4797 | 0.4769 | 0.9515 | |
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| 0.0664 | 7.5669 | 13000 | 0.2018 | 0.4830 | 0.4887 | 0.4858 | 0.9511 | |
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| 0.0674 | 7.8580 | 13500 | 0.2066 | 0.4954 | 0.5084 | 0.5018 | 0.9532 | |
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| 0.0621 | 8.1490 | 14000 | 0.2088 | 0.4737 | 0.5086 | 0.4905 | 0.9513 | |
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| 0.0532 | 8.4400 | 14500 | 0.2197 | 0.4995 | 0.4777 | 0.4883 | 0.9528 | |
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| 0.0544 | 8.7311 | 15000 | 0.2195 | 0.5120 | 0.4793 | 0.4951 | 0.9528 | |
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| 0.0558 | 9.0221 | 15500 | 0.2174 | 0.4953 | 0.5044 | 0.4998 | 0.9533 | |
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| 0.0454 | 9.3132 | 16000 | 0.2241 | 0.5061 | 0.5095 | 0.5078 | 0.9536 | |
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| 0.0458 | 9.6042 | 16500 | 0.2215 | 0.5058 | 0.5227 | 0.5141 | 0.9540 | |
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| 0.0451 | 9.8952 | 17000 | 0.2181 | 0.4940 | 0.5200 | 0.5066 | 0.9525 | |
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| 0.0399 | 10.1863 | 17500 | 0.2318 | 0.5085 | 0.5194 | 0.5139 | 0.9538 | |
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| 0.0375 | 10.4773 | 18000 | 0.2378 | 0.5108 | 0.5240 | 0.5173 | 0.9541 | |
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| 0.0378 | 10.7683 | 18500 | 0.2312 | 0.5118 | 0.5255 | 0.5185 | 0.9543 | |
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| 0.0376 | 11.0594 | 19000 | 0.2445 | 0.5006 | 0.5074 | 0.5040 | 0.9540 | |
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| 0.0338 | 11.3504 | 19500 | 0.2455 | 0.5081 | 0.5120 | 0.5101 | 0.9543 | |
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| 0.0326 | 11.6414 | 20000 | 0.2442 | 0.5108 | 0.5321 | 0.5212 | 0.9546 | |
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| 0.0318 | 11.9325 | 20500 | 0.2495 | 0.5168 | 0.5171 | 0.5169 | 0.9550 | |
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| 0.0289 | 12.2235 | 21000 | 0.2487 | 0.5113 | 0.5350 | 0.5229 | 0.9550 | |
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| 0.0278 | 12.5146 | 21500 | 0.2522 | 0.5050 | 0.5263 | 0.5154 | 0.9543 | |
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| 0.0277 | 12.8056 | 22000 | 0.2608 | 0.5221 | 0.5138 | 0.5179 | 0.9548 | |
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| 0.0263 | 13.0966 | 22500 | 0.2561 | 0.5133 | 0.5269 | 0.5200 | 0.9551 | |
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| 0.024 | 13.3877 | 23000 | 0.2631 | 0.5196 | 0.5258 | 0.5227 | 0.9547 | |
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| 0.0246 | 13.6787 | 23500 | 0.2628 | 0.5110 | 0.5527 | 0.5311 | 0.9551 | |
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| 0.0241 | 13.9697 | 24000 | 0.2735 | 0.5161 | 0.5260 | 0.5210 | 0.9552 | |
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| 0.021 | 14.2608 | 24500 | 0.2737 | 0.5224 | 0.5256 | 0.5240 | 0.9551 | |
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| 0.0201 | 14.5518 | 25000 | 0.2743 | 0.5246 | 0.5360 | 0.5302 | 0.9554 | |
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| 0.0208 | 14.8428 | 25500 | 0.2776 | 0.5180 | 0.5266 | 0.5222 | 0.9552 | |
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| 0.0201 | 15.1339 | 26000 | 0.2801 | 0.5065 | 0.5370 | 0.5213 | 0.9549 | |
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| 0.018 | 15.4249 | 26500 | 0.2770 | 0.5168 | 0.5335 | 0.5250 | 0.9550 | |
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| 0.0176 | 15.7159 | 27000 | 0.2875 | 0.5185 | 0.5324 | 0.5253 | 0.9551 | |
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| 0.0177 | 16.0070 | 27500 | 0.2861 | 0.5267 | 0.5321 | 0.5294 | 0.9556 | |
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| 0.0148 | 16.2980 | 28000 | 0.2860 | 0.5079 | 0.5442 | 0.5254 | 0.9549 | |
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| 0.0156 | 16.5891 | 28500 | 0.2953 | 0.5188 | 0.5380 | 0.5282 | 0.9552 | |
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| 0.0165 | 16.8801 | 29000 | 0.2928 | 0.5261 | 0.5333 | 0.5297 | 0.9557 | |
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| 0.0135 | 17.1711 | 29500 | 0.2981 | 0.5171 | 0.5396 | 0.5281 | 0.9554 | |
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| 0.0142 | 17.4622 | 30000 | 0.3062 | 0.5269 | 0.5164 | 0.5216 | 0.9554 | |
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| 0.0134 | 17.7532 | 30500 | 0.2947 | 0.5211 | 0.5418 | 0.5312 | 0.9555 | |
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| 0.0134 | 18.0442 | 31000 | 0.3045 | 0.5188 | 0.5426 | 0.5305 | 0.9559 | |
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| 0.012 | 18.3353 | 31500 | 0.3070 | 0.5236 | 0.5380 | 0.5307 | 0.9558 | |
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| 0.0123 | 18.6263 | 32000 | 0.3071 | 0.5409 | 0.5328 | 0.5368 | 0.9567 | |
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| 0.0117 | 18.9173 | 32500 | 0.3094 | 0.5265 | 0.5357 | 0.5311 | 0.9560 | |
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| 0.0108 | 19.2084 | 33000 | 0.3167 | 0.5344 | 0.5305 | 0.5325 | 0.9565 | |
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| 0.0111 | 19.4994 | 33500 | 0.3162 | 0.5182 | 0.5302 | 0.5241 | 0.9556 | |
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| 0.011 | 19.7905 | 34000 | 0.3152 | 0.5243 | 0.5377 | 0.5309 | 0.9557 | |
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| 0.0106 | 20.0815 | 34500 | 0.3241 | 0.5354 | 0.5200 | 0.5276 | 0.9562 | |
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| 0.0094 | 20.3725 | 35000 | 0.3240 | 0.5223 | 0.5288 | 0.5255 | 0.9560 | |
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| 0.0094 | 20.6636 | 35500 | 0.3271 | 0.5293 | 0.5322 | 0.5308 | 0.9563 | |
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| 0.0099 | 20.9546 | 36000 | 0.3219 | 0.5256 | 0.5334 | 0.5295 | 0.9559 | |
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| 0.0085 | 21.2456 | 36500 | 0.3223 | 0.5245 | 0.5429 | 0.5335 | 0.9560 | |
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| 0.0081 | 21.5367 | 37000 | 0.3308 | 0.5170 | 0.5340 | 0.5254 | 0.9558 | |
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| 0.0095 | 21.8277 | 37500 | 0.3292 | 0.5333 | 0.5294 | 0.5313 | 0.9564 | |
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| 0.008 | 22.1187 | 38000 | 0.3326 | 0.5270 | 0.5416 | 0.5342 | 0.9563 | |
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| 0.007 | 22.4098 | 38500 | 0.3306 | 0.5252 | 0.5473 | 0.5360 | 0.9563 | |
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| 0.0083 | 22.7008 | 39000 | 0.3301 | 0.5354 | 0.5396 | 0.5375 | 0.9565 | |
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| 0.0079 | 22.9919 | 39500 | 0.3268 | 0.5357 | 0.5421 | 0.5389 | 0.9562 | |
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| 0.0072 | 23.2829 | 40000 | 0.3383 | 0.5367 | 0.5311 | 0.5339 | 0.9563 | |
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| 0.0068 | 23.5739 | 40500 | 0.3349 | 0.5281 | 0.5392 | 0.5336 | 0.9562 | |
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| 0.0069 | 23.8650 | 41000 | 0.3383 | 0.5280 | 0.5408 | 0.5343 | 0.9563 | |
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| 0.0073 | 24.1560 | 41500 | 0.3390 | 0.5217 | 0.5436 | 0.5324 | 0.9563 | |
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| 0.0057 | 24.4470 | 42000 | 0.3395 | 0.5279 | 0.5311 | 0.5295 | 0.9560 | |
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| 0.0064 | 24.7381 | 42500 | 0.3420 | 0.5403 | 0.5295 | 0.5349 | 0.9563 | |
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| 0.0065 | 25.0291 | 43000 | 0.3436 | 0.5372 | 0.5348 | 0.5360 | 0.9565 | |
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| 0.0053 | 25.3201 | 43500 | 0.3444 | 0.5259 | 0.5399 | 0.5328 | 0.9562 | |
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| 0.0058 | 25.6112 | 44000 | 0.3475 | 0.5160 | 0.5367 | 0.5261 | 0.9556 | |
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| 0.0061 | 25.9022 | 44500 | 0.3479 | 0.5393 | 0.5344 | 0.5369 | 0.9566 | |
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| 0.0051 | 26.1932 | 45000 | 0.3435 | 0.5266 | 0.5418 | 0.5341 | 0.9559 | |
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| 0.0055 | 26.4843 | 45500 | 0.3440 | 0.5282 | 0.5419 | 0.5350 | 0.9562 | |
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| 0.005 | 26.7753 | 46000 | 0.3466 | 0.5287 | 0.5423 | 0.5354 | 0.9564 | |
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| 0.0058 | 27.0664 | 46500 | 0.3470 | 0.5308 | 0.5490 | 0.5398 | 0.9565 | |
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| 0.0052 | 27.3574 | 47000 | 0.3506 | 0.5343 | 0.5379 | 0.5361 | 0.9564 | |
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| 0.0049 | 27.6484 | 47500 | 0.3475 | 0.5276 | 0.5473 | 0.5373 | 0.9563 | |
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| 0.0052 | 27.9395 | 48000 | 0.3496 | 0.5276 | 0.5483 | 0.5377 | 0.9565 | |
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| 0.0049 | 28.2305 | 48500 | 0.3507 | 0.5327 | 0.5422 | 0.5374 | 0.9564 | |
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| 0.0049 | 28.5215 | 49000 | 0.3528 | 0.5363 | 0.5399 | 0.5381 | 0.9565 | |
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| 0.0052 | 28.8126 | 49500 | 0.3516 | 0.5382 | 0.5385 | 0.5383 | 0.9565 | |
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| 0.0042 | 29.1036 | 50000 | 0.3499 | 0.5330 | 0.5454 | 0.5391 | 0.9565 | |
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| 0.0045 | 29.3946 | 50500 | 0.3514 | 0.5343 | 0.5389 | 0.5366 | 0.9565 | |
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| 0.0048 | 29.6857 | 51000 | 0.3517 | 0.5316 | 0.5418 | 0.5367 | 0.9564 | |
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| 0.0043 | 29.9767 | 51500 | 0.3518 | 0.5316 | 0.5425 | 0.5370 | 0.9565 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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