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scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_delta-jason

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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
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  1. README.md +115 -115
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  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: 1.8042
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- - Accuracy: 0.8264
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- - F1: 0.8011
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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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- | 5.7284 | 0.27 | 5000 | 5.5547 | 0.3495 | 0.1646 |
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- | 3.9298 | 0.53 | 10000 | 3.9081 | 0.5831 | 0.4181 |
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- | 3.1458 | 0.8 | 15000 | 3.1900 | 0.6691 | 0.5676 |
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- | 2.53 | 1.07 | 20000 | 2.8419 | 0.7090 | 0.6229 |
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- | 2.2929 | 1.34 | 25000 | 2.6332 | 0.7336 | 0.6597 |
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- | 2.2133 | 1.6 | 30000 | 2.4526 | 0.7511 | 0.6781 |
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- | 2.0478 | 1.87 | 35000 | 2.3194 | 0.7676 | 0.6988 |
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- | 1.7304 | 2.14 | 40000 | 2.2969 | 0.7680 | 0.7101 |
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- | 1.6742 | 2.41 | 45000 | 2.2247 | 0.7798 | 0.7297 |
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- | 1.612 | 2.67 | 50000 | 2.1486 | 0.7869 | 0.7440 |
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- | 1.593 | 2.94 | 55000 | 2.0761 | 0.7922 | 0.7491 |
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- | 1.3076 | 3.21 | 60000 | 2.1148 | 0.7928 | 0.7532 |
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- | 1.3084 | 3.47 | 65000 | 2.0691 | 0.7955 | 0.7546 |
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- | 1.3439 | 3.74 | 70000 | 2.0406 | 0.7980 | 0.7577 |
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- | 1.238 | 4.01 | 75000 | 2.0845 | 0.7962 | 0.7591 |
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- | 1.1017 | 4.28 | 80000 | 2.0749 | 0.7999 | 0.7657 |
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- | 1.113 | 4.54 | 85000 | 2.0310 | 0.8027 | 0.7682 |
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- | 1.1233 | 4.81 | 90000 | 2.0350 | 0.8017 | 0.7667 |
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- | 0.9201 | 5.08 | 95000 | 2.0900 | 0.8051 | 0.7668 |
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- | 0.9371 | 5.34 | 100000 | 2.0517 | 0.8017 | 0.7716 |
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- | 0.9407 | 5.61 | 105000 | 2.0891 | 0.8036 | 0.7747 |
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- | 0.9562 | 5.88 | 110000 | 2.0128 | 0.8074 | 0.7743 |
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- | 0.8116 | 6.15 | 115000 | 2.0271 | 0.8070 | 0.7747 |
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- | 0.8406 | 6.41 | 120000 | 2.0704 | 0.8064 | 0.7772 |
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- | 0.8337 | 6.68 | 125000 | 2.0371 | 0.8079 | 0.7794 |
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- | 0.8457 | 6.95 | 130000 | 2.0188 | 0.8111 | 0.7883 |
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- | 0.733 | 7.22 | 135000 | 2.0236 | 0.8124 | 0.7857 |
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- | 0.7651 | 7.48 | 140000 | 2.0015 | 0.8093 | 0.7803 |
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- | 0.7825 | 7.75 | 145000 | 2.0183 | 0.8085 | 0.7821 |
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- | 0.675 | 8.02 | 150000 | 2.0356 | 0.8119 | 0.7844 |
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- | 0.6616 | 8.28 | 155000 | 2.0042 | 0.8133 | 0.7859 |
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- | 0.6763 | 8.55 | 160000 | 2.0247 | 0.8128 | 0.7856 |
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- | 0.7017 | 8.82 | 165000 | 2.0063 | 0.8145 | 0.7892 |
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- | 0.6238 | 9.09 | 170000 | 1.9923 | 0.8151 | 0.7870 |
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- | 0.6246 | 9.35 | 175000 | 1.9945 | 0.8128 | 0.7861 |
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- | 0.6149 | 9.62 | 180000 | 2.0137 | 0.8141 | 0.7857 |
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- | 0.6256 | 9.89 | 185000 | 2.0075 | 0.8144 | 0.7905 |
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- | 0.6024 | 10.15 | 190000 | 1.9870 | 0.8156 | 0.7887 |
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- | 0.5943 | 10.42 | 195000 | 2.0002 | 0.8154 | 0.7909 |
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- | 0.6087 | 10.69 | 200000 | 1.9796 | 0.8142 | 0.7877 |
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- | 0.5841 | 10.96 | 205000 | 1.9951 | 0.8158 | 0.7917 |
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- | 0.5565 | 11.22 | 210000 | 1.9997 | 0.8147 | 0.7901 |
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- | 0.5517 | 11.49 | 215000 | 2.0010 | 0.8152 | 0.7928 |
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- | 0.5594 | 11.76 | 220000 | 1.9900 | 0.8164 | 0.7885 |
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- | 0.5191 | 12.03 | 225000 | 1.9776 | 0.8139 | 0.7907 |
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- | 0.5224 | 12.29 | 230000 | 1.9566 | 0.8170 | 0.7921 |
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- | 0.5313 | 12.56 | 235000 | 1.9733 | 0.8188 | 0.7922 |
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- | 0.5279 | 12.83 | 240000 | 1.9505 | 0.8172 | 0.7910 |
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- | 0.502 | 13.09 | 245000 | 1.9851 | 0.8164 | 0.7921 |
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- | 0.5004 | 13.36 | 250000 | 1.9504 | 0.8172 | 0.7936 |
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- | 0.505 | 13.63 | 255000 | 1.9244 | 0.8194 | 0.7907 |
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- | 0.499 | 13.9 | 260000 | 1.9605 | 0.8163 | 0.7905 |
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- | 0.4874 | 14.16 | 265000 | 1.9346 | 0.8196 | 0.7915 |
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- | 0.4825 | 14.43 | 270000 | 1.9360 | 0.8181 | 0.7932 |
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- | 0.4973 | 14.7 | 275000 | 1.9251 | 0.8193 | 0.7952 |
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- | 0.4871 | 14.96 | 280000 | 1.9219 | 0.8200 | 0.7933 |
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- | 0.4524 | 15.23 | 285000 | 1.8977 | 0.8207 | 0.7955 |
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- | 0.4605 | 15.5 | 290000 | 1.9001 | 0.8201 | 0.7951 |
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- | 0.4625 | 15.77 | 295000 | 1.9227 | 0.8203 | 0.7966 |
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- | 0.4432 | 16.03 | 300000 | 1.9012 | 0.8214 | 0.7965 |
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- | 0.4448 | 16.3 | 305000 | 1.9161 | 0.8208 | 0.7978 |
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- | 0.4593 | 16.57 | 310000 | 1.8900 | 0.8211 | 0.7941 |
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- | 0.4518 | 16.84 | 315000 | 1.8960 | 0.8184 | 0.7936 |
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- | 0.4229 | 17.1 | 320000 | 1.9085 | 0.8208 | 0.7963 |
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- | 0.4366 | 17.37 | 325000 | 1.8767 | 0.8216 | 0.7981 |
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- | 0.4265 | 17.64 | 330000 | 1.8774 | 0.8233 | 0.7981 |
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- | 0.4342 | 17.9 | 335000 | 1.8915 | 0.8209 | 0.7960 |
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- | 0.4115 | 18.17 | 340000 | 1.8667 | 0.8236 | 0.7974 |
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- | 0.4147 | 18.44 | 345000 | 1.8752 | 0.8214 | 0.7936 |
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- | 0.4212 | 18.71 | 350000 | 1.8705 | 0.8220 | 0.7960 |
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- | 0.4196 | 18.97 | 355000 | 1.8663 | 0.8237 | 0.7992 |
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- | 0.3992 | 19.24 | 360000 | 1.8549 | 0.8236 | 0.7998 |
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- | 0.413 | 19.51 | 365000 | 1.8679 | 0.8229 | 0.7999 |
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- | 0.4048 | 19.77 | 370000 | 1.8594 | 0.8234 | 0.7977 |
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- | 0.396 | 20.04 | 375000 | 1.8530 | 0.8246 | 0.7981 |
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- | 0.398 | 20.31 | 380000 | 1.8536 | 0.8238 | 0.7995 |
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- | 0.3915 | 20.58 | 385000 | 1.8588 | 0.8233 | 0.7990 |
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- | 0.3961 | 20.84 | 390000 | 1.8726 | 0.8227 | 0.7981 |
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- | 0.3882 | 21.11 | 395000 | 1.8445 | 0.8236 | 0.7979 |
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- | 0.3844 | 21.38 | 400000 | 1.8465 | 0.8234 | 0.7983 |
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- | 0.3879 | 21.65 | 405000 | 1.8351 | 0.8244 | 0.7962 |
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- | 0.3904 | 21.91 | 410000 | 1.8457 | 0.8248 | 0.8002 |
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- | 0.3772 | 22.18 | 415000 | 1.8362 | 0.8255 | 0.8001 |
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- | 0.3802 | 22.45 | 420000 | 1.8277 | 0.8244 | 0.7989 |
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- | 0.3871 | 22.71 | 425000 | 1.8385 | 0.8236 | 0.7991 |
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- | 0.3806 | 22.98 | 430000 | 1.8390 | 0.8236 | 0.7985 |
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- | 0.3691 | 23.25 | 435000 | 1.8330 | 0.8237 | 0.7986 |
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- | 0.376 | 23.52 | 440000 | 1.8337 | 0.8247 | 0.7999 |
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- | 0.368 | 23.78 | 445000 | 1.8297 | 0.8254 | 0.8012 |
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- | 0.3607 | 24.05 | 450000 | 1.8294 | 0.8259 | 0.8008 |
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- | 0.3701 | 24.32 | 455000 | 1.8167 | 0.8254 | 0.7991 |
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- | 0.3621 | 24.58 | 460000 | 1.8322 | 0.8253 | 0.8004 |
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- | 0.3658 | 24.85 | 465000 | 1.8271 | 0.8249 | 0.7993 |
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- | 0.355 | 25.12 | 470000 | 1.8245 | 0.8267 | 0.8009 |
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- | 0.3662 | 25.39 | 475000 | 1.8205 | 0.8257 | 0.8004 |
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- | 0.3542 | 25.65 | 480000 | 1.8219 | 0.8256 | 0.7998 |
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- | 0.351 | 25.92 | 485000 | 1.8239 | 0.8246 | 0.7997 |
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- | 0.3536 | 26.19 | 490000 | 1.8072 | 0.8260 | 0.8008 |
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- | 0.3509 | 26.46 | 495000 | 1.8139 | 0.8255 | 0.8006 |
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- | 0.3503 | 26.72 | 500000 | 1.8121 | 0.8261 | 0.8008 |
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- | 0.359 | 26.99 | 505000 | 1.8133 | 0.8261 | 0.7993 |
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- | 0.3499 | 27.26 | 510000 | 1.8081 | 0.8265 | 0.8010 |
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- | 0.3428 | 27.52 | 515000 | 1.8061 | 0.8268 | 0.8011 |
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- | 0.3469 | 27.79 | 520000 | 1.8144 | 0.8258 | 0.8003 |
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- | 0.3364 | 28.06 | 525000 | 1.8063 | 0.8264 | 0.8011 |
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- | 0.347 | 28.33 | 530000 | 1.8071 | 0.8263 | 0.8006 |
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- | 0.3487 | 28.59 | 535000 | 1.8055 | 0.8262 | 0.8021 |
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- | 0.3406 | 28.86 | 540000 | 1.8062 | 0.8268 | 0.8022 |
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- | 0.3429 | 29.13 | 545000 | 1.8093 | 0.8264 | 0.8010 |
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- | 0.3392 | 29.39 | 550000 | 1.8011 | 0.8268 | 0.8014 |
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- | 0.3403 | 29.66 | 555000 | 1.8074 | 0.8264 | 0.8008 |
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- | 0.3424 | 29.93 | 560000 | 1.8042 | 0.8264 | 0.8011 |
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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: 1.7914
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+ - Accuracy: 0.8274
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+ - F1: 0.8029
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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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+ | 5.7233 | 0.27 | 5000 | 5.5374 | 0.3569 | 0.1678 |
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+ | 3.9329 | 0.53 | 10000 | 3.9116 | 0.5835 | 0.4241 |
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+ | 3.1699 | 0.8 | 15000 | 3.1794 | 0.6720 | 0.5600 |
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+ | 2.5004 | 1.07 | 20000 | 2.7930 | 0.7147 | 0.6299 |
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+ | 2.289 | 1.34 | 25000 | 2.5799 | 0.7385 | 0.6658 |
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+ | 2.1906 | 1.6 | 30000 | 2.4334 | 0.7534 | 0.6852 |
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+ | 2.0248 | 1.87 | 35000 | 2.3295 | 0.7659 | 0.6957 |
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+ | 1.7159 | 2.14 | 40000 | 2.2350 | 0.7731 | 0.7166 |
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+ | 1.669 | 2.41 | 45000 | 2.2103 | 0.7811 | 0.7321 |
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+ | 1.6191 | 2.67 | 50000 | 2.1297 | 0.7874 | 0.7426 |
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+ | 1.587 | 2.94 | 55000 | 2.0855 | 0.7890 | 0.7448 |
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+ | 1.3031 | 3.21 | 60000 | 2.1223 | 0.7920 | 0.7492 |
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+ | 1.3 | 3.47 | 65000 | 2.0614 | 0.7952 | 0.7563 |
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+ | 1.337 | 3.74 | 70000 | 2.0343 | 0.8002 | 0.7618 |
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+ | 1.2406 | 4.01 | 75000 | 2.0565 | 0.7999 | 0.7616 |
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+ | 1.1146 | 4.28 | 80000 | 2.0439 | 0.8006 | 0.7686 |
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+ | 1.0767 | 4.54 | 85000 | 2.0461 | 0.8001 | 0.7663 |
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+ | 1.1244 | 4.81 | 90000 | 2.0096 | 0.8032 | 0.7699 |
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+ | 0.91 | 5.08 | 95000 | 2.0492 | 0.8064 | 0.7710 |
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+ | 0.9446 | 5.34 | 100000 | 2.0278 | 0.8059 | 0.7742 |
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+ | 0.9581 | 5.61 | 105000 | 2.0434 | 0.8034 | 0.7750 |
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+ | 0.9525 | 5.88 | 110000 | 2.0068 | 0.8073 | 0.7794 |
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+ | 0.809 | 6.15 | 115000 | 2.0286 | 0.8072 | 0.7773 |
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+ | 0.8597 | 6.41 | 120000 | 2.0080 | 0.8104 | 0.7819 |
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+ | 0.8219 | 6.68 | 125000 | 2.0049 | 0.8093 | 0.7816 |
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+ | 0.8507 | 6.95 | 130000 | 2.0095 | 0.8094 | 0.7866 |
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+ | 0.7371 | 7.22 | 135000 | 2.0533 | 0.8105 | 0.7863 |
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+ | 0.754 | 7.48 | 140000 | 1.9987 | 0.8110 | 0.7827 |
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+ | 0.7829 | 7.75 | 145000 | 2.0120 | 0.8079 | 0.7825 |
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+ | 0.6798 | 8.02 | 150000 | 1.9928 | 0.8144 | 0.7883 |
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+ | 0.6619 | 8.28 | 155000 | 1.9941 | 0.8127 | 0.7861 |
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+ | 0.6869 | 8.55 | 160000 | 2.0368 | 0.8119 | 0.7889 |
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+ | 0.695 | 8.82 | 165000 | 2.0073 | 0.8133 | 0.7869 |
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+ | 0.6395 | 9.09 | 170000 | 2.0095 | 0.8110 | 0.7879 |
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+ | 0.6274 | 9.35 | 175000 | 1.9915 | 0.8156 | 0.7924 |
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+ | 0.6186 | 9.62 | 180000 | 2.0114 | 0.8158 | 0.7912 |
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+ | 0.643 | 9.89 | 185000 | 1.9917 | 0.8143 | 0.7936 |
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+ | 0.5898 | 10.15 | 190000 | 2.0036 | 0.8156 | 0.7905 |
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+ | 0.5948 | 10.42 | 195000 | 1.9868 | 0.8168 | 0.7904 |
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+ | 0.6093 | 10.69 | 200000 | 1.9822 | 0.8153 | 0.7892 |
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+ | 0.5942 | 10.96 | 205000 | 1.9939 | 0.8144 | 0.7912 |
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+ | 0.5497 | 11.22 | 210000 | 1.9786 | 0.8169 | 0.7961 |
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+ | 0.5516 | 11.49 | 215000 | 1.9650 | 0.8168 | 0.7913 |
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+ | 0.5591 | 11.76 | 220000 | 1.9793 | 0.8175 | 0.7927 |
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+ | 0.5103 | 12.03 | 225000 | 1.9715 | 0.8183 | 0.7942 |
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+ | 0.5165 | 12.29 | 230000 | 1.9620 | 0.8172 | 0.7936 |
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+ | 0.5248 | 12.56 | 235000 | 1.9760 | 0.8179 | 0.7950 |
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+ | 0.5289 | 12.83 | 240000 | 1.9459 | 0.8190 | 0.7952 |
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+ | 0.4995 | 13.09 | 245000 | 1.9564 | 0.8185 | 0.7959 |
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+ | 0.4906 | 13.36 | 250000 | 1.9484 | 0.8186 | 0.7940 |
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+ | 0.5011 | 13.63 | 255000 | 1.9320 | 0.8188 | 0.7923 |
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+ | 0.4996 | 13.9 | 260000 | 1.9477 | 0.8164 | 0.7929 |
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+ | 0.4844 | 14.16 | 265000 | 1.9110 | 0.8207 | 0.7942 |
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+ | 0.4814 | 14.43 | 270000 | 1.9303 | 0.8190 | 0.7927 |
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+ | 0.4953 | 14.7 | 275000 | 1.9211 | 0.8208 | 0.7951 |
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+ | 0.4897 | 14.96 | 280000 | 1.9206 | 0.8209 | 0.7940 |
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+ | 0.4473 | 15.23 | 285000 | 1.9059 | 0.8214 | 0.7959 |
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+ | 0.4615 | 15.5 | 290000 | 1.9021 | 0.8229 | 0.7985 |
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+ | 0.4687 | 15.77 | 295000 | 1.9177 | 0.8204 | 0.7960 |
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+ | 0.4425 | 16.03 | 300000 | 1.9065 | 0.8225 | 0.7994 |
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+ | 0.451 | 16.3 | 305000 | 1.8924 | 0.8219 | 0.7972 |
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+ | 0.458 | 16.57 | 310000 | 1.9036 | 0.8210 | 0.7953 |
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+ | 0.4514 | 16.84 | 315000 | 1.8810 | 0.8224 | 0.7960 |
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+ | 0.4263 | 17.1 | 320000 | 1.8826 | 0.8241 | 0.8003 |
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+ | 0.4355 | 17.37 | 325000 | 1.8685 | 0.8236 | 0.7991 |
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+ | 0.4234 | 17.64 | 330000 | 1.8634 | 0.8249 | 0.7994 |
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+ | 0.4346 | 17.9 | 335000 | 1.8640 | 0.8239 | 0.8001 |
123
+ | 0.4077 | 18.17 | 340000 | 1.8656 | 0.8245 | 0.8006 |
124
+ | 0.4156 | 18.44 | 345000 | 1.8666 | 0.8229 | 0.7990 |
125
+ | 0.4185 | 18.71 | 350000 | 1.8495 | 0.8235 | 0.8005 |
126
+ | 0.4211 | 18.97 | 355000 | 1.8784 | 0.8233 | 0.7982 |
127
+ | 0.3981 | 19.24 | 360000 | 1.8562 | 0.8235 | 0.7993 |
128
+ | 0.4139 | 19.51 | 365000 | 1.8417 | 0.8243 | 0.7986 |
129
+ | 0.4052 | 19.77 | 370000 | 1.8533 | 0.8249 | 0.7998 |
130
+ | 0.3915 | 20.04 | 375000 | 1.8413 | 0.8255 | 0.8020 |
131
+ | 0.4015 | 20.31 | 380000 | 1.8540 | 0.8232 | 0.7991 |
132
+ | 0.3923 | 20.58 | 385000 | 1.8592 | 0.8245 | 0.7995 |
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+ | 0.3984 | 20.84 | 390000 | 1.8613 | 0.8257 | 0.8026 |
134
+ | 0.3886 | 21.11 | 395000 | 1.8350 | 0.8248 | 0.7985 |
135
+ | 0.3888 | 21.38 | 400000 | 1.8343 | 0.8238 | 0.7984 |
136
+ | 0.3878 | 21.65 | 405000 | 1.8207 | 0.8263 | 0.8013 |
137
+ | 0.3901 | 21.91 | 410000 | 1.8394 | 0.8266 | 0.8034 |
138
+ | 0.3765 | 22.18 | 415000 | 1.8250 | 0.8257 | 0.8017 |
139
+ | 0.3793 | 22.45 | 420000 | 1.8159 | 0.8262 | 0.7997 |
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+ | 0.3825 | 22.71 | 425000 | 1.8220 | 0.8244 | 0.8009 |
141
+ | 0.383 | 22.98 | 430000 | 1.8325 | 0.8265 | 0.8013 |
142
+ | 0.3737 | 23.25 | 435000 | 1.8248 | 0.8259 | 0.8024 |
143
+ | 0.3741 | 23.52 | 440000 | 1.8139 | 0.8258 | 0.8014 |
144
+ | 0.3676 | 23.78 | 445000 | 1.8299 | 0.8264 | 0.8007 |
145
+ | 0.3611 | 24.05 | 450000 | 1.8136 | 0.8261 | 0.8018 |
146
+ | 0.3642 | 24.32 | 455000 | 1.8196 | 0.8263 | 0.8017 |
147
+ | 0.3654 | 24.58 | 460000 | 1.8241 | 0.8249 | 0.8012 |
148
+ | 0.3706 | 24.85 | 465000 | 1.8103 | 0.8255 | 0.8012 |
149
+ | 0.3585 | 25.12 | 470000 | 1.8137 | 0.8263 | 0.8029 |
150
+ | 0.3664 | 25.39 | 475000 | 1.8094 | 0.8260 | 0.8024 |
151
+ | 0.3544 | 25.65 | 480000 | 1.8000 | 0.8279 | 0.8041 |
152
+ | 0.3491 | 25.92 | 485000 | 1.8039 | 0.8264 | 0.8028 |
153
+ | 0.3523 | 26.19 | 490000 | 1.7989 | 0.8279 | 0.8037 |
154
+ | 0.3483 | 26.46 | 495000 | 1.8045 | 0.8276 | 0.8027 |
155
+ | 0.3482 | 26.72 | 500000 | 1.8058 | 0.8264 | 0.8022 |
156
+ | 0.3601 | 26.99 | 505000 | 1.8035 | 0.8269 | 0.8017 |
157
+ | 0.3461 | 27.26 | 510000 | 1.7959 | 0.8273 | 0.8041 |
158
+ | 0.3448 | 27.52 | 515000 | 1.8078 | 0.8271 | 0.8030 |
159
+ | 0.3454 | 27.79 | 520000 | 1.7968 | 0.8273 | 0.8035 |
160
+ | 0.3377 | 28.06 | 525000 | 1.7924 | 0.8270 | 0.8019 |
161
+ | 0.3497 | 28.33 | 530000 | 1.7950 | 0.8277 | 0.8041 |
162
+ | 0.3461 | 28.59 | 535000 | 1.7954 | 0.8282 | 0.8049 |
163
+ | 0.3448 | 28.86 | 540000 | 1.7968 | 0.8270 | 0.8031 |
164
+ | 0.3413 | 29.13 | 545000 | 1.7914 | 0.8279 | 0.8038 |
165
+ | 0.3367 | 29.39 | 550000 | 1.7976 | 0.8274 | 0.8027 |
166
+ | 0.3432 | 29.66 | 555000 | 1.7976 | 0.8271 | 0.8037 |
167
+ | 0.3429 | 29.93 | 560000 | 1.7914 | 0.8274 | 0.8029 |
168
 
169
 
170
  ### Framework versions
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