--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 - precision - recall - accuracy model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: validation args: PAN-X.en metrics: - name: F1 type: f1 value: 0.8236654056326187 - name: Precision type: precision value: 0.8163449520899875 - name: Recall type: recall value: 0.8311183373391772 - name: Accuracy type: accuracy value: 0.8236654056326187 --- # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2487 - F1: 0.8237 - Precision: 0.8163 - Recall: 0.8311 - Accuracy: 0.8237 ## 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: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| | 1.2644 | 0.03 | 24 | 0.8175 | 0.4212 | 0.3696 | 0.4897 | 0.4212 | | 0.7209 | 0.06 | 48 | 0.5633 | 0.4817 | 0.4190 | 0.5665 | 0.4817 | | 0.5951 | 0.09 | 72 | 0.4670 | 0.6059 | 0.5588 | 0.6617 | 0.6059 | | 0.4475 | 0.12 | 96 | 0.4425 | 0.6659 | 0.6336 | 0.7016 | 0.6659 | | 0.4978 | 0.14 | 120 | 0.4469 | 0.6375 | 0.5930 | 0.6892 | 0.6375 | | 0.4383 | 0.17 | 144 | 0.4093 | 0.7003 | 0.6668 | 0.7374 | 0.7003 | | 0.4148 | 0.2 | 168 | 0.3688 | 0.7122 | 0.6877 | 0.7387 | 0.7122 | | 0.4513 | 0.23 | 192 | 0.3700 | 0.7236 | 0.7081 | 0.7397 | 0.7236 | | 0.3786 | 0.26 | 216 | 0.3666 | 0.7304 | 0.7125 | 0.7493 | 0.7304 | | 0.425 | 0.29 | 240 | 0.3652 | 0.7046 | 0.6874 | 0.7227 | 0.7046 | | 0.4014 | 0.32 | 264 | 0.3438 | 0.7246 | 0.6964 | 0.7552 | 0.7246 | | 0.3789 | 0.35 | 288 | 0.3533 | 0.7208 | 0.6922 | 0.7519 | 0.7208 | | 0.4032 | 0.37 | 312 | 0.3567 | 0.7252 | 0.7125 | 0.7383 | 0.7252 | | 0.371 | 0.4 | 336 | 0.3282 | 0.7433 | 0.7255 | 0.7620 | 0.7433 | | 0.3397 | 0.43 | 360 | 0.3304 | 0.7522 | 0.7312 | 0.7745 | 0.7522 | | 0.3871 | 0.46 | 384 | 0.3244 | 0.7427 | 0.7160 | 0.7715 | 0.7427 | | 0.3461 | 0.49 | 408 | 0.3284 | 0.7520 | 0.7298 | 0.7756 | 0.7520 | | 0.3504 | 0.52 | 432 | 0.3049 | 0.7574 | 0.7418 | 0.7737 | 0.7574 | | 0.3387 | 0.55 | 456 | 0.3178 | 0.7717 | 0.7537 | 0.7906 | 0.7717 | | 0.3259 | 0.58 | 480 | 0.3026 | 0.7738 | 0.7636 | 0.7843 | 0.7738 | | 0.3473 | 0.6 | 504 | 0.3254 | 0.7324 | 0.7090 | 0.7574 | 0.7324 | | 0.2893 | 0.63 | 528 | 0.3102 | 0.7689 | 0.7571 | 0.7810 | 0.7689 | | 0.3669 | 0.66 | 552 | 0.3119 | 0.7631 | 0.7528 | 0.7737 | 0.7631 | | 0.312 | 0.69 | 576 | 0.2963 | 0.7818 | 0.7734 | 0.7905 | 0.7818 | | 0.297 | 0.72 | 600 | 0.3217 | 0.7542 | 0.7332 | 0.7765 | 0.7542 | | 0.3095 | 0.75 | 624 | 0.3038 | 0.7732 | 0.7580 | 0.7891 | 0.7732 | | 0.3514 | 0.78 | 648 | 0.2913 | 0.7794 | 0.7669 | 0.7924 | 0.7794 | | 0.2824 | 0.81 | 672 | 0.3008 | 0.7813 | 0.7752 | 0.7876 | 0.7813 | | 0.3203 | 0.83 | 696 | 0.2915 | 0.7807 | 0.7641 | 0.7980 | 0.7807 | | 0.3089 | 0.86 | 720 | 0.2941 | 0.7838 | 0.7755 | 0.7923 | 0.7838 | | 0.3174 | 0.89 | 744 | 0.2986 | 0.7770 | 0.7609 | 0.7937 | 0.7770 | | 0.3264 | 0.92 | 768 | 0.2783 | 0.7788 | 0.7630 | 0.7951 | 0.7788 | | 0.2815 | 0.95 | 792 | 0.2861 | 0.7848 | 0.7704 | 0.7998 | 0.7848 | | 0.2895 | 0.98 | 816 | 0.2799 | 0.7842 | 0.7702 | 0.7988 | 0.7842 | | 0.3023 | 1.01 | 840 | 0.2818 | 0.7876 | 0.7722 | 0.8038 | 0.7876 | | 0.2358 | 1.04 | 864 | 0.2924 | 0.7836 | 0.7750 | 0.7925 | 0.7836 | | 0.2819 | 1.06 | 888 | 0.2861 | 0.7761 | 0.7696 | 0.7828 | 0.7761 | | 0.2692 | 1.09 | 912 | 0.2924 | 0.7756 | 0.7680 | 0.7833 | 0.7756 | | 0.2478 | 1.12 | 936 | 0.2963 | 0.7833 | 0.7599 | 0.8082 | 0.7833 | | 0.2557 | 1.15 | 960 | 0.2960 | 0.7783 | 0.7814 | 0.7751 | 0.7783 | | 0.3003 | 1.18 | 984 | 0.2656 | 0.7862 | 0.7727 | 0.8002 | 0.7862 | | 0.2254 | 1.21 | 1008 | 0.2791 | 0.8007 | 0.7890 | 0.8129 | 0.8007 | | 0.2496 | 1.24 | 1032 | 0.2702 | 0.7877 | 0.7701 | 0.8062 | 0.7877 | | 0.2124 | 1.27 | 1056 | 0.2888 | 0.7952 | 0.7895 | 0.8011 | 0.7952 | | 0.2841 | 1.29 | 1080 | 0.2761 | 0.7946 | 0.7870 | 0.8023 | 0.7946 | | 0.2517 | 1.32 | 1104 | 0.2659 | 0.8026 | 0.7909 | 0.8146 | 0.8026 | | 0.2355 | 1.35 | 1128 | 0.2681 | 0.8003 | 0.7876 | 0.8134 | 0.8003 | | 0.2402 | 1.38 | 1152 | 0.2701 | 0.7991 | 0.7892 | 0.8093 | 0.7991 | | 0.2296 | 1.41 | 1176 | 0.2753 | 0.7946 | 0.7819 | 0.8077 | 0.7946 | | 0.2453 | 1.44 | 1200 | 0.2696 | 0.8029 | 0.7912 | 0.8149 | 0.8029 | | 0.2689 | 1.47 | 1224 | 0.2700 | 0.7936 | 0.7819 | 0.8056 | 0.7936 | | 0.2362 | 1.5 | 1248 | 0.2705 | 0.8028 | 0.8005 | 0.8051 | 0.8028 | | 0.226 | 1.53 | 1272 | 0.2642 | 0.8042 | 0.7910 | 0.8180 | 0.8042 | | 0.2139 | 1.55 | 1296 | 0.2690 | 0.8013 | 0.7942 | 0.8084 | 0.8013 | | 0.2744 | 1.58 | 1320 | 0.2619 | 0.7999 | 0.7841 | 0.8163 | 0.7999 | | 0.2015 | 1.61 | 1344 | 0.2640 | 0.8066 | 0.8035 | 0.8098 | 0.8066 | | 0.1949 | 1.64 | 1368 | 0.2750 | 0.8075 | 0.8023 | 0.8129 | 0.8075 | | 0.2259 | 1.67 | 1392 | 0.2669 | 0.8092 | 0.7997 | 0.8189 | 0.8092 | | 0.1884 | 1.7 | 1416 | 0.2729 | 0.8061 | 0.7990 | 0.8133 | 0.8061 | | 0.1868 | 1.73 | 1440 | 0.2679 | 0.8083 | 0.8007 | 0.8161 | 0.8083 | | 0.2292 | 1.76 | 1464 | 0.2658 | 0.8055 | 0.7954 | 0.8158 | 0.8055 | | 0.22 | 1.78 | 1488 | 0.2610 | 0.8066 | 0.8006 | 0.8126 | 0.8066 | | 0.2335 | 1.81 | 1512 | 0.2613 | 0.7997 | 0.7816 | 0.8185 | 0.7997 | | 0.2379 | 1.84 | 1536 | 0.2495 | 0.8081 | 0.7975 | 0.8190 | 0.8081 | | 0.2394 | 1.87 | 1560 | 0.2619 | 0.8063 | 0.7951 | 0.8177 | 0.8063 | | 0.2526 | 1.9 | 1584 | 0.2502 | 0.8116 | 0.8032 | 0.8202 | 0.8116 | | 0.2167 | 1.93 | 1608 | 0.2528 | 0.8134 | 0.8000 | 0.8273 | 0.8134 | | 0.2354 | 1.96 | 1632 | 0.2449 | 0.8099 | 0.8013 | 0.8188 | 0.8099 | | 0.2808 | 1.99 | 1656 | 0.2469 | 0.8067 | 0.7938 | 0.8201 | 0.8067 | | 0.1924 | 2.01 | 1680 | 0.2487 | 0.8077 | 0.7930 | 0.8229 | 0.8077 | | 0.1498 | 2.04 | 1704 | 0.2619 | 0.8127 | 0.8015 | 0.8242 | 0.8127 | | 0.2 | 2.07 | 1728 | 0.2590 | 0.8133 | 0.8044 | 0.8224 | 0.8133 | | 0.151 | 2.1 | 1752 | 0.2623 | 0.8066 | 0.7949 | 0.8186 | 0.8066 | | 0.1646 | 2.13 | 1776 | 0.2632 | 0.8186 | 0.8137 | 0.8236 | 0.8186 | | 0.1659 | 2.16 | 1800 | 0.2561 | 0.8188 | 0.8096 | 0.8281 | 0.8188 | | 0.1888 | 2.19 | 1824 | 0.2549 | 0.8136 | 0.8038 | 0.8237 | 0.8136 | | 0.2084 | 2.22 | 1848 | 0.2557 | 0.8141 | 0.8087 | 0.8197 | 0.8141 | | 0.1571 | 2.24 | 1872 | 0.2697 | 0.8150 | 0.8053 | 0.8249 | 0.8150 | | 0.1541 | 2.27 | 1896 | 0.2605 | 0.8191 | 0.8121 | 0.8262 | 0.8191 | | 0.1586 | 2.3 | 1920 | 0.2742 | 0.8109 | 0.8073 | 0.8144 | 0.8109 | | 0.1641 | 2.33 | 1944 | 0.2679 | 0.8148 | 0.8104 | 0.8193 | 0.8148 | | 0.1914 | 2.36 | 1968 | 0.2596 | 0.8159 | 0.8056 | 0.8265 | 0.8159 | | 0.1441 | 2.39 | 1992 | 0.2644 | 0.8183 | 0.8139 | 0.8226 | 0.8183 | | 0.1672 | 2.42 | 2016 | 0.2652 | 0.8180 | 0.8081 | 0.8281 | 0.8180 | | 0.1852 | 2.45 | 2040 | 0.2576 | 0.8205 | 0.8101 | 0.8313 | 0.8205 | | 0.192 | 2.47 | 2064 | 0.2459 | 0.8179 | 0.8063 | 0.8298 | 0.8179 | | 0.1698 | 2.5 | 2088 | 0.2482 | 0.8213 | 0.8149 | 0.8277 | 0.8213 | | 0.1802 | 2.53 | 2112 | 0.2519 | 0.8155 | 0.8066 | 0.8247 | 0.8155 | | 0.1619 | 2.56 | 2136 | 0.2582 | 0.8175 | 0.8036 | 0.8319 | 0.8175 | | 0.1974 | 2.59 | 2160 | 0.2535 | 0.8184 | 0.8108 | 0.8261 | 0.8184 | | 0.1655 | 2.62 | 2184 | 0.2514 | 0.8229 | 0.8165 | 0.8295 | 0.8229 | | 0.1844 | 2.65 | 2208 | 0.2536 | 0.8208 | 0.8152 | 0.8264 | 0.8208 | | 0.1601 | 2.68 | 2232 | 0.2531 | 0.8194 | 0.8104 | 0.8286 | 0.8194 | | 0.161 | 2.71 | 2256 | 0.2508 | 0.8226 | 0.8145 | 0.8310 | 0.8226 | | 0.1672 | 2.73 | 2280 | 0.2527 | 0.8216 | 0.8137 | 0.8296 | 0.8216 | | 0.2053 | 2.76 | 2304 | 0.2482 | 0.8208 | 0.8112 | 0.8306 | 0.8208 | | 0.1776 | 2.79 | 2328 | 0.2486 | 0.8215 | 0.8143 | 0.8288 | 0.8215 | | 0.1559 | 2.82 | 2352 | 0.2495 | 0.8233 | 0.8156 | 0.8312 | 0.8233 | | 0.1509 | 2.85 | 2376 | 0.2472 | 0.8231 | 0.8142 | 0.8322 | 0.8231 | | 0.1695 | 2.88 | 2400 | 0.2465 | 0.8229 | 0.8134 | 0.8326 | 0.8229 | | 0.1523 | 2.91 | 2424 | 0.2466 | 0.8234 | 0.8154 | 0.8315 | 0.8234 | | 0.1525 | 2.94 | 2448 | 0.2478 | 0.8241 | 0.8165 | 0.8319 | 0.8241 | | 0.1386 | 2.96 | 2472 | 0.2486 | 0.8236 | 0.8164 | 0.8309 | 0.8236 | | 0.1532 | 2.99 | 2496 | 0.2487 | 0.8237 | 0.8163 | 0.8311 | 0.8237 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3