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scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_gamma-jason

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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,9 +20,9 @@ should probably proofread and complete it, then remove this comment. -->
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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 massive dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.8136
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- - Accuracy: 0.8249
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- - F1: 0.8012
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  ## Model description
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@@ -53,118 +53,118 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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- | 5.9639 | 0.27 | 5000 | 5.8995 | 0.2955 | 0.1146 |
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- | 4.2158 | 0.53 | 10000 | 4.1658 | 0.5475 | 0.3812 |
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- | 3.2469 | 0.8 | 15000 | 3.3199 | 0.6523 | 0.5316 |
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- | 2.5924 | 1.07 | 20000 | 2.8985 | 0.7018 | 0.6127 |
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- | 2.359 | 1.34 | 25000 | 2.6534 | 0.7297 | 0.6595 |
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- | 2.1928 | 1.6 | 30000 | 2.4466 | 0.7556 | 0.6969 |
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- | 2.0695 | 1.87 | 35000 | 2.2808 | 0.7719 | 0.7201 |
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- | 1.6893 | 2.14 | 40000 | 2.3052 | 0.7714 | 0.7085 |
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- | 1.6663 | 2.41 | 45000 | 2.2096 | 0.7796 | 0.7274 |
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- | 1.6035 | 2.67 | 50000 | 2.1603 | 0.7821 | 0.7388 |
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- | 1.5974 | 2.94 | 55000 | 2.1085 | 0.7886 | 0.7445 |
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- | 1.3064 | 3.21 | 60000 | 2.1031 | 0.7949 | 0.7541 |
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- | 1.2976 | 3.47 | 65000 | 2.0789 | 0.7939 | 0.7503 |
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- | 1.3204 | 3.74 | 70000 | 2.0467 | 0.7973 | 0.7608 |
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- | 1.2155 | 4.01 | 75000 | 2.0445 | 0.8029 | 0.7624 |
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- | 1.1162 | 4.28 | 80000 | 2.0914 | 0.8000 | 0.7638 |
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- | 1.0772 | 4.54 | 85000 | 2.0247 | 0.8022 | 0.7668 |
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- | 1.087 | 4.81 | 90000 | 1.9932 | 0.8044 | 0.7715 |
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- | 0.9072 | 5.08 | 95000 | 2.0550 | 0.8049 | 0.7720 |
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- | 0.948 | 5.34 | 100000 | 2.0583 | 0.8037 | 0.7737 |
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- | 0.9416 | 5.61 | 105000 | 2.0447 | 0.8048 | 0.7738 |
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- | 0.9495 | 5.88 | 110000 | 2.0402 | 0.8035 | 0.7725 |
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- | 0.8017 | 6.15 | 115000 | 2.0705 | 0.8043 | 0.7743 |
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- | 0.8167 | 6.41 | 120000 | 2.0509 | 0.8098 | 0.7820 |
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- | 0.8453 | 6.68 | 125000 | 2.0258 | 0.8083 | 0.7797 |
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- | 0.8504 | 6.95 | 130000 | 2.0039 | 0.8094 | 0.7815 |
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- | 0.7261 | 7.22 | 135000 | 2.0579 | 0.8099 | 0.7814 |
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- | 0.7351 | 7.48 | 140000 | 2.0215 | 0.8118 | 0.7814 |
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- | 0.7607 | 7.75 | 145000 | 1.9842 | 0.8126 | 0.7807 |
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- | 0.6825 | 8.02 | 150000 | 2.0317 | 0.8125 | 0.7833 |
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- | 0.6747 | 8.28 | 155000 | 2.0287 | 0.8103 | 0.7833 |
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- | 0.6759 | 8.55 | 160000 | 2.0570 | 0.8109 | 0.7850 |
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- | 0.6915 | 8.82 | 165000 | 2.0286 | 0.8107 | 0.7850 |
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- | 0.6121 | 9.09 | 170000 | 2.0231 | 0.8128 | 0.7871 |
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- | 0.6033 | 9.35 | 175000 | 1.9958 | 0.8132 | 0.7861 |
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- | 0.6238 | 9.62 | 180000 | 2.0033 | 0.8127 | 0.7893 |
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- | 0.6388 | 9.89 | 185000 | 1.9964 | 0.8126 | 0.7869 |
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- | 0.5685 | 10.15 | 190000 | 1.9832 | 0.8152 | 0.7915 |
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- | 0.5809 | 10.42 | 195000 | 1.9936 | 0.8143 | 0.7901 |
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- | 0.5918 | 10.69 | 200000 | 1.9799 | 0.8154 | 0.7921 |
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- | 0.5935 | 10.96 | 205000 | 2.0006 | 0.8142 | 0.7910 |
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- | 0.5453 | 11.22 | 210000 | 1.9909 | 0.8149 | 0.7906 |
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- | 0.5617 | 11.49 | 215000 | 1.9938 | 0.8163 | 0.7887 |
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- | 0.5612 | 11.76 | 220000 | 1.9629 | 0.8164 | 0.7911 |
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- | 0.5285 | 12.03 | 225000 | 1.9658 | 0.8162 | 0.7915 |
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- | 0.5264 | 12.29 | 230000 | 1.9494 | 0.8161 | 0.7916 |
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- | 0.5204 | 12.56 | 235000 | 1.9663 | 0.8162 | 0.7918 |
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- | 0.5371 | 12.83 | 240000 | 1.9591 | 0.8180 | 0.7938 |
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- | 0.4888 | 13.09 | 245000 | 1.9480 | 0.8188 | 0.7939 |
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- | 0.5061 | 13.36 | 250000 | 1.9505 | 0.8180 | 0.7936 |
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- | 0.5071 | 13.63 | 255000 | 1.9220 | 0.8185 | 0.7925 |
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- | 0.5169 | 13.9 | 260000 | 1.9130 | 0.8185 | 0.7931 |
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- | 0.4841 | 14.16 | 265000 | 1.9376 | 0.8176 | 0.7926 |
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- | 0.4848 | 14.43 | 270000 | 1.9286 | 0.8185 | 0.7970 |
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- | 0.4892 | 14.7 | 275000 | 1.9307 | 0.8192 | 0.7988 |
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- | 0.4812 | 14.96 | 280000 | 1.9178 | 0.8204 | 0.7992 |
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- | 0.4715 | 15.23 | 285000 | 1.9250 | 0.8183 | 0.7957 |
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- | 0.4555 | 15.5 | 290000 | 1.9070 | 0.8192 | 0.7945 |
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- | 0.459 | 15.77 | 295000 | 1.9043 | 0.8197 | 0.7988 |
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- | 0.4421 | 16.03 | 300000 | 1.9107 | 0.8200 | 0.7989 |
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- | 0.4409 | 16.3 | 305000 | 1.9116 | 0.8205 | 0.7965 |
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- | 0.4446 | 16.57 | 310000 | 1.9168 | 0.8187 | 0.7955 |
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- | 0.4555 | 16.84 | 315000 | 1.9092 | 0.8196 | 0.7955 |
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- | 0.4366 | 17.1 | 320000 | 1.8796 | 0.8226 | 0.7998 |
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- | 0.4281 | 17.37 | 325000 | 1.8841 | 0.8217 | 0.7965 |
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- | 0.4327 | 17.64 | 330000 | 1.8910 | 0.8214 | 0.7973 |
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- | 0.4334 | 17.9 | 335000 | 1.8906 | 0.8206 | 0.8002 |
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- | 0.4145 | 18.17 | 340000 | 1.8844 | 0.8210 | 0.7973 |
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- | 0.4159 | 18.44 | 345000 | 1.8848 | 0.8220 | 0.7992 |
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- | 0.4226 | 18.71 | 350000 | 1.8813 | 0.8213 | 0.7990 |
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- | 0.425 | 18.97 | 355000 | 1.8698 | 0.8226 | 0.7970 |
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- | 0.4056 | 19.24 | 360000 | 1.8689 | 0.8225 | 0.7990 |
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- | 0.4056 | 19.51 | 365000 | 1.8652 | 0.8206 | 0.7973 |
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- | 0.4034 | 19.77 | 370000 | 1.8561 | 0.8234 | 0.8002 |
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- | 0.3963 | 20.04 | 375000 | 1.8577 | 0.8224 | 0.7963 |
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- | 0.4002 | 20.31 | 380000 | 1.8347 | 0.8241 | 0.7974 |
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- | 0.3911 | 20.58 | 385000 | 1.8596 | 0.8229 | 0.8007 |
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- | 0.4026 | 20.84 | 390000 | 1.8512 | 0.8233 | 0.7996 |
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- | 0.3883 | 21.11 | 395000 | 1.8533 | 0.8239 | 0.7984 |
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- | 0.3909 | 21.38 | 400000 | 1.8491 | 0.8229 | 0.8001 |
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- | 0.3942 | 21.65 | 405000 | 1.8378 | 0.8239 | 0.7980 |
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- | 0.382 | 21.91 | 410000 | 1.8459 | 0.8234 | 0.7994 |
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- | 0.373 | 22.18 | 415000 | 1.8472 | 0.8238 | 0.8003 |
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- | 0.3842 | 22.45 | 420000 | 1.8446 | 0.8236 | 0.7992 |
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- | 0.3761 | 22.71 | 425000 | 1.8393 | 0.8252 | 0.8019 |
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- | 0.3742 | 22.98 | 430000 | 1.8391 | 0.8239 | 0.7995 |
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- | 0.3708 | 23.25 | 435000 | 1.8248 | 0.8253 | 0.8035 |
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- | 0.3705 | 23.52 | 440000 | 1.8345 | 0.8243 | 0.8001 |
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- | 0.3754 | 23.78 | 445000 | 1.8252 | 0.8247 | 0.8006 |
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- | 0.364 | 24.05 | 450000 | 1.8300 | 0.8249 | 0.7998 |
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- | 0.367 | 24.32 | 455000 | 1.8215 | 0.8252 | 0.8003 |
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- | 0.3602 | 24.58 | 460000 | 1.8280 | 0.8247 | 0.8014 |
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- | 0.3658 | 24.85 | 465000 | 1.8331 | 0.8249 | 0.8011 |
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- | 0.3543 | 25.12 | 470000 | 1.8189 | 0.8247 | 0.8004 |
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- | 0.3535 | 25.39 | 475000 | 1.8213 | 0.8243 | 0.8002 |
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- | 0.3584 | 25.65 | 480000 | 1.8162 | 0.8244 | 0.8010 |
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- | 0.3523 | 25.92 | 485000 | 1.8285 | 0.8244 | 0.8012 |
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- | 0.3497 | 26.19 | 490000 | 1.8153 | 0.8255 | 0.8016 |
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- | 0.3513 | 26.46 | 495000 | 1.8120 | 0.8251 | 0.8001 |
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- | 0.3547 | 26.72 | 500000 | 1.8170 | 0.8245 | 0.8007 |
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- | 0.3506 | 26.99 | 505000 | 1.8137 | 0.8247 | 0.8002 |
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- | 0.3518 | 27.26 | 510000 | 1.8188 | 0.8243 | 0.8006 |
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- | 0.34 | 27.52 | 515000 | 1.8223 | 0.8248 | 0.8003 |
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- | 0.3422 | 27.79 | 520000 | 1.8171 | 0.8249 | 0.8020 |
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- | 0.335 | 28.06 | 525000 | 1.8093 | 0.8258 | 0.8011 |
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- | 0.3451 | 28.33 | 530000 | 1.8112 | 0.8255 | 0.8005 |
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- | 0.3419 | 28.59 | 535000 | 1.8126 | 0.8251 | 0.8015 |
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- | 0.3532 | 28.86 | 540000 | 1.8114 | 0.8253 | 0.8011 |
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- | 0.3311 | 29.13 | 545000 | 1.8102 | 0.8252 | 0.8014 |
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- | 0.3355 | 29.39 | 550000 | 1.8104 | 0.8255 | 0.8006 |
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- | 0.3416 | 29.66 | 555000 | 1.8062 | 0.8254 | 0.8013 |
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- | 0.3387 | 29.93 | 560000 | 1.8136 | 0.8249 | 0.8012 |
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  ### Framework versions
 
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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 massive dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.8080
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+ - Accuracy: 0.8266
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+ - F1: 0.8031
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
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+ | 6.283 | 0.27 | 5000 | 6.2020 | 0.2676 | 0.1018 |
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+ | 4.4008 | 0.53 | 10000 | 4.4005 | 0.5229 | 0.3509 |
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+ | 3.3814 | 0.8 | 15000 | 3.4464 | 0.6349 | 0.5009 |
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+ | 2.6796 | 1.07 | 20000 | 2.9367 | 0.6972 | 0.6000 |
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+ | 2.4097 | 1.34 | 25000 | 2.6464 | 0.7279 | 0.6536 |
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+ | 2.2303 | 1.6 | 30000 | 2.4685 | 0.7522 | 0.6898 |
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+ | 2.0826 | 1.87 | 35000 | 2.3359 | 0.7657 | 0.7120 |
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+ | 1.6889 | 2.14 | 40000 | 2.3083 | 0.7710 | 0.7094 |
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+ | 1.6854 | 2.41 | 45000 | 2.2360 | 0.7767 | 0.7203 |
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+ | 1.621 | 2.67 | 50000 | 2.1137 | 0.7867 | 0.7391 |
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+ | 1.6051 | 2.94 | 55000 | 2.0718 | 0.7929 | 0.7467 |
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+ | 1.3071 | 3.21 | 60000 | 2.1140 | 0.7929 | 0.7508 |
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+ | 1.3165 | 3.47 | 65000 | 2.0525 | 0.7962 | 0.7574 |
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+ | 1.3278 | 3.74 | 70000 | 2.0554 | 0.7993 | 0.7620 |
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+ | 1.203 | 4.01 | 75000 | 2.0619 | 0.7989 | 0.7606 |
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+ | 1.1003 | 4.28 | 80000 | 2.0386 | 0.8011 | 0.7667 |
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+ | 1.0856 | 4.54 | 85000 | 2.0191 | 0.8024 | 0.7723 |
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+ | 1.0941 | 4.81 | 90000 | 2.0063 | 0.8019 | 0.7722 |
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+ | 0.9191 | 5.08 | 95000 | 2.0552 | 0.8051 | 0.7680 |
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+ | 0.9443 | 5.34 | 100000 | 2.0511 | 0.8023 | 0.7745 |
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+ | 0.9377 | 5.61 | 105000 | 2.0379 | 0.8059 | 0.7772 |
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+ | 0.9659 | 5.88 | 110000 | 2.0115 | 0.8058 | 0.7760 |
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+ | 0.7971 | 6.15 | 115000 | 2.0532 | 0.8083 | 0.7798 |
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+ | 0.8237 | 6.41 | 120000 | 2.0635 | 0.8070 | 0.7798 |
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+ | 0.8419 | 6.68 | 125000 | 2.0257 | 0.8079 | 0.7756 |
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+ | 0.8498 | 6.95 | 130000 | 2.0144 | 0.8117 | 0.7858 |
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+ | 0.7382 | 7.22 | 135000 | 2.0307 | 0.8101 | 0.7828 |
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+ | 0.7385 | 7.48 | 140000 | 2.0336 | 0.8117 | 0.7879 |
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+ | 0.7622 | 7.75 | 145000 | 1.9982 | 0.8126 | 0.7849 |
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+ | 0.6757 | 8.02 | 150000 | 2.0168 | 0.8147 | 0.7929 |
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+ | 0.667 | 8.28 | 155000 | 2.0176 | 0.8130 | 0.7882 |
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+ | 0.6735 | 8.55 | 160000 | 2.0328 | 0.8121 | 0.7888 |
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+ | 0.6927 | 8.82 | 165000 | 1.9887 | 0.8127 | 0.7877 |
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+ | 0.6113 | 9.09 | 170000 | 2.0148 | 0.8145 | 0.7885 |
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+ | 0.6098 | 9.35 | 175000 | 2.0184 | 0.8139 | 0.7898 |
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+ | 0.6308 | 9.62 | 180000 | 1.9917 | 0.8120 | 0.7870 |
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+ | 0.6361 | 9.89 | 185000 | 1.9818 | 0.8134 | 0.7877 |
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+ | 0.5727 | 10.15 | 190000 | 2.0203 | 0.8126 | 0.7888 |
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+ | 0.59 | 10.42 | 195000 | 1.9819 | 0.8143 | 0.7930 |
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+ | 0.5947 | 10.69 | 200000 | 2.0151 | 0.8143 | 0.7906 |
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+ | 0.602 | 10.96 | 205000 | 1.9809 | 0.8165 | 0.7923 |
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+ | 0.5482 | 11.22 | 210000 | 1.9816 | 0.8160 | 0.7935 |
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+ | 0.5669 | 11.49 | 215000 | 1.9793 | 0.8160 | 0.7904 |
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+ | 0.5724 | 11.76 | 220000 | 1.9677 | 0.8153 | 0.7905 |
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+ | 0.5295 | 12.03 | 225000 | 1.9569 | 0.8171 | 0.7924 |
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+ | 0.5277 | 12.29 | 230000 | 1.9549 | 0.8178 | 0.7959 |
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+ | 0.5324 | 12.56 | 235000 | 1.9477 | 0.8175 | 0.7929 |
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+ | 0.5374 | 12.83 | 240000 | 1.9587 | 0.8176 | 0.7960 |
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+ | 0.4818 | 13.09 | 245000 | 1.9764 | 0.8168 | 0.7935 |
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+ | 0.5064 | 13.36 | 250000 | 1.9439 | 0.8180 | 0.7945 |
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+ | 0.507 | 13.63 | 255000 | 1.9332 | 0.8160 | 0.7941 |
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+ | 0.5081 | 13.9 | 260000 | 1.9293 | 0.8180 | 0.7990 |
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+ | 0.4791 | 14.16 | 265000 | 1.9500 | 0.8183 | 0.7953 |
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+ | 0.4949 | 14.43 | 270000 | 1.9520 | 0.8181 | 0.7952 |
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+ | 0.4746 | 14.7 | 275000 | 1.9375 | 0.8197 | 0.7966 |
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+ | 0.4918 | 14.96 | 280000 | 1.9161 | 0.8210 | 0.7949 |
112
+ | 0.4758 | 15.23 | 285000 | 1.9281 | 0.8184 | 0.7939 |
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+ | 0.4605 | 15.5 | 290000 | 1.9164 | 0.8194 | 0.7934 |
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+ | 0.4637 | 15.77 | 295000 | 1.9372 | 0.8192 | 0.7986 |
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+ | 0.4387 | 16.03 | 300000 | 1.9123 | 0.8213 | 0.8005 |
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+ | 0.4405 | 16.3 | 305000 | 1.9115 | 0.8191 | 0.7966 |
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+ | 0.4455 | 16.57 | 310000 | 1.8867 | 0.8212 | 0.7981 |
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+ | 0.4562 | 16.84 | 315000 | 1.9136 | 0.8199 | 0.7967 |
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+ | 0.4316 | 17.1 | 320000 | 1.8907 | 0.8218 | 0.7986 |
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+ | 0.4281 | 17.37 | 325000 | 1.8942 | 0.8222 | 0.7990 |
121
+ | 0.4296 | 17.64 | 330000 | 1.9041 | 0.8215 | 0.7998 |
122
+ | 0.4327 | 17.9 | 335000 | 1.8844 | 0.8239 | 0.7999 |
123
+ | 0.4157 | 18.17 | 340000 | 1.8902 | 0.8219 | 0.8001 |
124
+ | 0.4184 | 18.44 | 345000 | 1.8874 | 0.8227 | 0.7991 |
125
+ | 0.4224 | 18.71 | 350000 | 1.8701 | 0.8224 | 0.7991 |
126
+ | 0.4264 | 18.97 | 355000 | 1.8816 | 0.8217 | 0.7974 |
127
+ | 0.4044 | 19.24 | 360000 | 1.8879 | 0.8212 | 0.7974 |
128
+ | 0.4119 | 19.51 | 365000 | 1.8577 | 0.8229 | 0.7991 |
129
+ | 0.4046 | 19.77 | 370000 | 1.8675 | 0.8235 | 0.8003 |
130
+ | 0.4011 | 20.04 | 375000 | 1.8604 | 0.8231 | 0.7997 |
131
+ | 0.4036 | 20.31 | 380000 | 1.8500 | 0.8240 | 0.8000 |
132
+ | 0.3887 | 20.58 | 385000 | 1.8624 | 0.8231 | 0.7999 |
133
+ | 0.4057 | 20.84 | 390000 | 1.8588 | 0.8222 | 0.7972 |
134
+ | 0.3883 | 21.11 | 395000 | 1.8524 | 0.8233 | 0.7990 |
135
+ | 0.3881 | 21.38 | 400000 | 1.8481 | 0.8245 | 0.8024 |
136
+ | 0.3956 | 21.65 | 405000 | 1.8503 | 0.8245 | 0.8005 |
137
+ | 0.3828 | 21.91 | 410000 | 1.8538 | 0.8240 | 0.7999 |
138
+ | 0.3776 | 22.18 | 415000 | 1.8495 | 0.8241 | 0.7999 |
139
+ | 0.3896 | 22.45 | 420000 | 1.8513 | 0.8226 | 0.7991 |
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+ | 0.3759 | 22.71 | 425000 | 1.8518 | 0.8251 | 0.8007 |
141
+ | 0.3769 | 22.98 | 430000 | 1.8388 | 0.8242 | 0.8019 |
142
+ | 0.3675 | 23.25 | 435000 | 1.8307 | 0.8245 | 0.8002 |
143
+ | 0.3704 | 23.52 | 440000 | 1.8402 | 0.8227 | 0.7992 |
144
+ | 0.3698 | 23.78 | 445000 | 1.8409 | 0.8238 | 0.7991 |
145
+ | 0.3672 | 24.05 | 450000 | 1.8180 | 0.8248 | 0.7979 |
146
+ | 0.3709 | 24.32 | 455000 | 1.8300 | 0.8235 | 0.8003 |
147
+ | 0.361 | 24.58 | 460000 | 1.8265 | 0.8252 | 0.8012 |
148
+ | 0.3649 | 24.85 | 465000 | 1.8288 | 0.8250 | 0.8012 |
149
+ | 0.3534 | 25.12 | 470000 | 1.8216 | 0.8253 | 0.8025 |
150
+ | 0.3535 | 25.39 | 475000 | 1.8240 | 0.8261 | 0.8017 |
151
+ | 0.3578 | 25.65 | 480000 | 1.8216 | 0.8259 | 0.8011 |
152
+ | 0.3569 | 25.92 | 485000 | 1.8257 | 0.8253 | 0.8025 |
153
+ | 0.3515 | 26.19 | 490000 | 1.8191 | 0.8263 | 0.8026 |
154
+ | 0.3551 | 26.46 | 495000 | 1.8209 | 0.8264 | 0.8036 |
155
+ | 0.3577 | 26.72 | 500000 | 1.8199 | 0.8254 | 0.8011 |
156
+ | 0.3548 | 26.99 | 505000 | 1.8190 | 0.8252 | 0.8006 |
157
+ | 0.3498 | 27.26 | 510000 | 1.8072 | 0.8257 | 0.8023 |
158
+ | 0.3419 | 27.52 | 515000 | 1.8131 | 0.8259 | 0.8019 |
159
+ | 0.3452 | 27.79 | 520000 | 1.8140 | 0.8253 | 0.8023 |
160
+ | 0.3364 | 28.06 | 525000 | 1.8145 | 0.8254 | 0.8017 |
161
+ | 0.346 | 28.33 | 530000 | 1.8087 | 0.8256 | 0.8019 |
162
+ | 0.3391 | 28.59 | 535000 | 1.8142 | 0.8259 | 0.8025 |
163
+ | 0.3535 | 28.86 | 540000 | 1.8044 | 0.8270 | 0.8029 |
164
+ | 0.333 | 29.13 | 545000 | 1.8150 | 0.8264 | 0.8026 |
165
+ | 0.3397 | 29.39 | 550000 | 1.8099 | 0.8266 | 0.8032 |
166
+ | 0.3429 | 29.66 | 555000 | 1.8090 | 0.8259 | 0.8017 |
167
+ | 0.3422 | 29.93 | 560000 | 1.8080 | 0.8266 | 0.8031 |
168
 
169
 
170
  ### Framework versions
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