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verdict-classifier-en

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roberta
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metadata
license: mit
tags:
  - generated_from_trainer
model-index:
  - name: verdict-classifier-en
    results: []

verdict-classifier-en

This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:

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

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: 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 Prec Macro Prec Misinformation Prec Factual Prec 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