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English Verdict Classifier

This model is a fine-tuned version of roberta-base on 2,500 deduplicated verdicts from Google Fact Check Tools API, translated into English with the Google Cloud Translation API. It achieves the following results on the evaluation set, being 1,000 such verdicts translated into English, but here including duplicates to represent the true distribution:

  • Loss: 0.1290
  • F1 Macro: 0.9171
  • F1 Misinformation: 0.9896
  • F1 Factual: 0.9890
  • F1 Other: 0.7727
  • Precision Macro: 0.8940
  • Precision Misinformation: 0.9954
  • Precision Factual: 0.9783
  • Precision Other: 0.7083

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2500
  • num_epochs: 1000

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Misinformation F1 Factual F1 Other Precision Macro Precision Misinformation Precision Factual Precision Other
1.1493 0.16 50 1.1040 0.0550 0.0 0.1650 0.0 0.0300 0.0 0.0899 0.0
1.0899 0.32 100 1.0765 0.0619 0.0203 0.1654 0.0 0.2301 0.6 0.0903 0.0
1.0136 0.48 150 1.0487 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.9868 0.64 200 1.0221 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.9599 0.8 250 0.9801 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.9554 0.96 300 0.9500 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.935 1.12 350 0.9071 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.948 1.28 400 0.8809 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.9344 1.44 450 0.8258 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.9182 1.6 500 0.7687 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.8942 1.76 550 0.5787 0.3102 0.9306 0.0 0.0 0.2900 0.8701 0.0 0.0
0.8932 1.92 600 0.4506 0.4043 0.9628 0.0 0.25 0.3777 0.9753 0.0 0.1579
0.7448 2.08 650 0.2884 0.5323 0.9650 0.3303 0.3017 0.7075 0.9810 0.9474 0.1942
0.6616 2.24 700 0.2162 0.8161 0.9710 0.9724 0.5051 0.7910 0.9824 0.9670 0.4237
0.575 2.4 750 0.1754 0.8305 0.9714 0.9780 0.5421 0.7961 0.9881 0.9674 0.4328
0.5246 2.56 800 0.1641 0.8102 0.9659 0.9175 0.5472 0.7614 0.9892 0.8558 0.4394
0.481 2.72 850 0.1399 0.8407 0.9756 0.9780 0.5686 0.8082 0.9894 0.9674 0.4677
0.4588 2.88 900 0.1212 0.8501 0.9786 0.9783 0.5934 0.8247 0.9871 0.9574 0.5294
0.4512 3.04 950 0.1388 0.8270 0.9702 0.9836 0.5273 0.7904 0.9893 0.9677 0.4143
0.3894 3.2 1000 0.1270 0.8411 0.9737 0.9836 0.5660 0.8043 0.9905 0.9677 0.4545
0.3772 3.36 1050 0.1267 0.8336 0.9732 0.9890 0.5385 0.8013 0.9882 0.9783 0.4375
0.3528 3.52 1100 0.1073 0.8546 0.9791 0.9890 0.5957 0.8284 0.9883 0.9783 0.5185
0.3694 3.68 1150 0.1120 0.8431 0.9786 0.9890 0.5618 0.8244 0.9849 0.9783 0.5102
0.3146 3.84 1200 0.1189 0.8325 0.9738 0.9836 0.54 0.8016 0.9870 0.9677 0.45
0.3038 4.01 1250 0.1041 0.8648 0.9815 0.9836 0.6292 0.8425 0.9884 0.9677 0.5714
0.2482 4.17 1300 0.1245 0.8588 0.9773 0.9836 0.6154 0.8202 0.9929 0.9677 0.5
0.2388 4.33 1350 0.1167 0.8701 0.9808 0.9836 0.6458 0.8377 0.9918 0.9677 0.5536
0.2593 4.49 1400 0.1215 0.8654 0.9790 0.9836 0.6337 0.8284 0.9929 0.9677 0.5246
0.239 4.65 1450 0.1057 0.8621 0.9803 0.9890 0.6170 0.8349 0.9895 0.9783 0.5370
0.2397 4.81 1500 0.1256 0.8544 0.9761 0.9890 0.5981 0.8162 0.9929 0.9783 0.4776
0.2238 4.97 1550 0.1189 0.8701 0.9802 0.9836 0.6465 0.8343 0.9929 0.9677 0.5424
0.1811 5.13 1600 0.1456 0.8438 0.9737 0.9836 0.5741 0.8051 0.9917 0.9677 0.4559
0.1615 5.29 1650 0.1076 0.8780 0.9838 0.9836 0.6667 0.8581 0.9895 0.9677 0.6170
0.1783 5.45 1700 0.1217 0.8869 0.9831 0.9836 0.6939 0.8497 0.9953 0.9677 0.5862
0.1615 5.61 1750 0.1305 0.8770 0.9808 0.9836 0.6667 0.8371 0.9953 0.9677 0.5484
0.155 5.77 1800 0.1218 0.8668 0.9821 0.9890 0.6292 0.8460 0.9884 0.9783 0.5714
0.167 5.93 1850 0.1091 0.8991 0.9873 0.9890 0.7209 0.8814 0.9919 0.9783 0.6739
0.1455 6.09 1900 0.1338 0.8535 0.9773 0.9890 0.5941 0.8202 0.9906 0.9783 0.4918
0.1301 6.25 1950 0.1321 0.8792 0.9820 0.9890 0.6667 0.8439 0.9941 0.9783 0.5593
0.1049 6.41 2000 0.1181 0.9031 0.9879 0.9834 0.7381 0.8911 0.9908 0.9780 0.7045
0.1403 6.57 2050 0.1432 0.8608 0.9779 0.9890 0.6154 0.8237 0.9929 0.9783 0.5
0.1178 6.73 2100 0.1443 0.8937 0.9844 0.9945 0.7021 0.8644 0.9930 0.9890 0.6111
0.1267 6.89 2150 0.1346 0.8494 0.9786 0.9890 0.5806 0.8249 0.9871 0.9783 0.5094
0.1043 7.05 2200 0.1494 0.8905 0.9832 0.9945 0.6939 0.8564 0.9941 0.9890 0.5862
0.0886 7.21 2250 0.1180 0.8946 0.9873 0.9890 0.7073 0.8861 0.9896 0.9783 0.6905
0.1183 7.37 2300 0.1777 0.8720 0.9790 0.9890 0.6481 0.8298 0.9964 0.9783 0.5147
0.0813 7.53 2350 0.1405 0.8912 0.9856 0.9836 0.7045 0.8685 0.9919 0.9677 0.6458
0.111 7.69 2400 0.1379 0.8874 0.9838 0.9836 0.6947 0.8540 0.9941 0.9677 0.6
0.1199 7.85 2450 0.1301 0.9080 0.9879 0.9890 0.7473 0.8801 0.9953 0.9783 0.6667
0.1054 8.01 2500 0.1478 0.8845 0.9838 0.9890 0.6809 0.8546 0.9930 0.9783 0.5926
0.105 8.17 2550 0.1333 0.9021 0.9879 0.9890 0.7294 0.8863 0.9919 0.9783 0.6889
0.09 8.33 2600 0.1555 0.8926 0.9855 0.9890 0.7033 0.8662 0.9930 0.9783 0.6275
0.0947 8.49 2650 0.1572 0.8831 0.9856 0.9890 0.6747 0.8726 0.9885 0.9783 0.6512
0.0784 8.65 2700 0.1477 0.8969 0.9873 0.9890 0.7143 0.8836 0.9908 0.9783 0.6818
0.0814 8.81 2750 0.1700 0.8932 0.9861 0.9890 0.7045 0.8720 0.9919 0.9783 0.6458
0.0962 8.97 2800 0.1290 0.9171 0.9896 0.9890 0.7727 0.8940 0.9954 0.9783 0.7083
0.0802 9.13 2850 0.1721 0.8796 0.9832 0.9890 0.6667 0.8517 0.9918 0.9783 0.5849
0.0844 9.29 2900 0.1516 0.9023 0.9867 0.9890 0.7312 0.8717 0.9953 0.9783 0.6415
0.0511 9.45 2950 0.1544 0.9062 0.9879 0.9890 0.7416 0.8820 0.9942 0.9783 0.6735
0.0751 9.61 3000 0.1748 0.8884 0.9832 0.9945 0.6875 0.8571 0.9930 0.9890 0.5893
0.0707 9.77 3050 0.1743 0.8721 0.9802 0.9890 0.6471 0.8349 0.9941 0.9783 0.5323
0.0951 9.93 3100 0.1660 0.8899 0.9850 0.9890 0.6957 0.8622 0.9930 0.9783 0.6154
0.0576 10.1 3150 0.2029 0.8613 0.9766 0.9890 0.6182 0.8197 0.9952 0.9783 0.4857
0.0727 10.26 3200 0.1709 0.8920 0.9849 0.9890 0.7021 0.8612 0.9942 0.9783 0.6111
0.0654 10.42 3250 0.1599 0.8999 0.9861 0.9945 0.7191 0.8780 0.9919 0.9890 0.6531
0.0553 10.58 3300 0.2091 0.8920 0.9849 0.9890 0.7021 0.8612 0.9942 0.9783 0.6111

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.9.0+cu102
  • Datasets 1.9.0
  • Tokenizers 0.10.2
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