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xlm-roberta-base-finetuned-panx-en

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metadata
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 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