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saattrupdan
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verdict-classifier-en

Text Classification
Transformers
PyTorch
TensorBoard
English
roberta
Generated from Trainer
Inference Endpoints
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  ---
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  license: mit
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- language: en
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  tags:
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  - generated_from_trainer
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  model-index:
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  - name: verdict-classifier-en
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- results:
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- - task:
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- type: text-classification
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- name: Verdict Classification
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- widget:
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- - "One might think that this is true, but it's taken out of context."
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  ---
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- # English Verdict Classifier
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on 2,500 deduplicated verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into English with the [Google Cloud Translation API](https://cloud.google.com/translate/docs/reference/rest/).
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- 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:
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- - Loss: 0.1304
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- - F1 Macro: 0.8868
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- - F1 Misinformation: 0.9832
 
 
 
 
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  - F1 Factual: 0.9890
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- - F1 Other: 0.6882
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- - Prec Macro: 0.8580
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- - Prec Misinformation: 0.9918
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  - Prec Factual: 0.9783
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- - Prec Other: 0.6038
 
 
 
 
 
 
 
 
 
 
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  ## Training procedure
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@@ -40,40 +49,79 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 32
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_steps: 625
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  - num_epochs: 1000
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  ### Training results
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48
  | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other |
49
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:|
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- | 1.0588 | 0.64 | 50 | 1.0803 | 0.0256 | 0.0 | 0.0 | 0.0768 | 0.0133 | 0.0 | 0.0 | 0.0400 |
51
- | 0.9885 | 1.28 | 100 | 1.0055 | 0.3497 | 0.9291 | 0.0 | 0.12 | 0.3910 | 0.8729 | 0.0 | 0.3 |
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- | 0.971 | 1.92 | 150 | 0.9218 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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- | 0.9263 | 2.56 | 200 | 0.6035 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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- | 0.8672 | 3.2 | 250 | 0.3639 | 0.4428 | 0.9337 | 0.0 | 0.3946 | 0.3976 | 0.9217 | 0.0 | 0.2710 |
55
- | 0.743 | 3.84 | 300 | 0.2396 | 0.7944 | 0.9698 | 0.9091 | 0.5043 | 0.7893 | 0.9812 | 1.0 | 0.3867 |
56
- | 0.5106 | 4.49 | 350 | 0.1579 | 0.8399 | 0.9733 | 0.9888 | 0.5577 | 0.8130 | 0.9859 | 1.0 | 0.4531 |
57
- | 0.4215 | 5.13 | 400 | 0.1245 | 0.8174 | 0.9747 | 0.9834 | 0.4941 | 0.8076 | 0.9780 | 0.9780 | 0.4667 |
58
- | 0.3941 | 5.77 | 450 | 0.1422 | 0.8298 | 0.9678 | 1.0 | 0.5217 | 0.7960 | 0.9880 | 1.0 | 0.4 |
59
- | 0.3105 | 6.41 | 500 | 0.1352 | 0.8223 | 0.9696 | 0.9836 | 0.5138 | 0.7872 | 0.9881 | 0.9677 | 0.4058 |
60
- | 0.3126 | 7.05 | 550 | 0.1126 | 0.8423 | 0.9756 | 0.9945 | 0.5567 | 0.8162 | 0.9859 | 0.9890 | 0.4737 |
61
- | 0.2206 | 7.69 | 600 | 0.1206 | 0.8557 | 0.9761 | 0.9890 | 0.6019 | 0.8203 | 0.9905 | 0.9783 | 0.4921 |
62
- | 0.2472 | 8.33 | 650 | 0.1296 | 0.8481 | 0.9731 | 0.9945 | 0.5766 | 0.8105 | 0.9917 | 0.9890 | 0.4507 |
63
- | 0.1839 | 8.97 | 700 | 0.1357 | 0.8582 | 0.9761 | 0.9890 | 0.6095 | 0.8208 | 0.9917 | 0.9783 | 0.4923 |
64
- | 0.1282 | 9.61 | 750 | 0.1465 | 0.8481 | 0.9756 | 0.9945 | 0.5743 | 0.8175 | 0.9882 | 0.9890 | 0.4754 |
65
- | 0.1447 | 10.26 | 800 | 0.1621 | 0.8602 | 0.9767 | 0.9945 | 0.6095 | 0.8243 | 0.9917 | 0.9890 | 0.4923 |
66
- | 0.1223 | 10.9 | 850 | 0.1304 | 0.8868 | 0.9832 | 0.9890 | 0.6882 | 0.8580 | 0.9918 | 0.9783 | 0.6038 |
67
- | 0.1053 | 11.54 | 900 | 0.1640 | 0.8714 | 0.9797 | 0.9945 | 0.64 | 0.8380 | 0.9918 | 0.9890 | 0.5333 |
68
- | 0.064 | 12.18 | 950 | 0.1983 | 0.8627 | 0.9791 | 0.9889 | 0.62 | 0.8321 | 0.9906 | 0.9889 | 0.5167 |
69
- | 0.1085 | 12.82 | 1000 | 0.1811 | 0.8688 | 0.9803 | 0.9945 | 0.6316 | 0.8413 | 0.9895 | 0.9890 | 0.5455 |
70
- | 0.0885 | 13.46 | 1050 | 0.2052 | 0.8710 | 0.9821 | 0.9945 | 0.6364 | 0.8532 | 0.9872 | 0.9890 | 0.5833 |
71
- | 0.0799 | 14.1 | 1100 | 0.1826 | 0.8801 | 0.9827 | 0.9836 | 0.6742 | 0.8565 | 0.9895 | 0.9677 | 0.6122 |
72
- | 0.0737 | 14.74 | 1150 | 0.2158 | 0.8556 | 0.9761 | 0.9945 | 0.5962 | 0.8213 | 0.9905 | 0.9890 | 0.4844 |
73
- | 0.0564 | 15.38 | 1200 | 0.2283 | 0.8637 | 0.9797 | 0.9945 | 0.6170 | 0.8381 | 0.9883 | 0.9890 | 0.5370 |
74
- | 0.0547 | 16.03 | 1250 | 0.2508 | 0.8693 | 0.9785 | 0.9888 | 0.6408 | 0.8381 | 0.9906 | 1.0 | 0.5238 |
75
- | 0.0602 | 16.67 | 1300 | 0.2320 | 0.8555 | 0.9798 | 0.9889 | 0.5977 | 0.8420 | 0.9838 | 0.9889 | 0.5532 |
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- | 0.0576 | 17.31 | 1350 | 0.2346 | 0.8737 | 0.9803 | 0.9945 | 0.6465 | 0.8411 | 0.9918 | 0.9890 | 0.5424 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
@@ -81,4 +129,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.11.3
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  - Pytorch 1.9.0+cu102
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  - Datasets 1.9.0
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- - Tokenizers 0.10.2
 
1
  ---
2
  license: mit
 
3
  tags:
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  - generated_from_trainer
5
  model-index:
6
  - name: verdict-classifier-en
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+ results: []
 
 
 
 
 
8
  ---
9
 
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # verdict-classifier-en
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1290
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+ - F1 Macro: 0.9171
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+ - F1 Misinformation: 0.9896
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  - F1 Factual: 0.9890
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+ - F1 Other: 0.7727
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+ - Prec Macro: 0.8940
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+ - Prec Misinformation: 0.9954
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  - Prec Factual: 0.9783
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+ - Prec Other: 0.7083
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+ More information needed
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39
  ## Training procedure
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49
  - total_train_batch_size: 32
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 2500
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  - num_epochs: 1000
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55
  ### Training results
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57
  | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:|
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+ | 1.1493 | 0.16 | 50 | 1.1040 | 0.0550 | 0.0 | 0.1650 | 0.0 | 0.0300 | 0.0 | 0.0899 | 0.0 |
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+ | 1.0899 | 0.32 | 100 | 1.0765 | 0.0619 | 0.0203 | 0.1654 | 0.0 | 0.2301 | 0.6 | 0.0903 | 0.0 |
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+ | 1.0136 | 0.48 | 150 | 1.0487 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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+ | 0.9868 | 0.64 | 200 | 1.0221 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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+ | 0.9599 | 0.8 | 250 | 0.9801 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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+ | 0.9554 | 0.96 | 300 | 0.9500 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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+ | 0.935 | 1.12 | 350 | 0.9071 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
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+ | 0.948 | 1.28 | 400 | 0.8809 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
67
+ | 0.9344 | 1.44 | 450 | 0.8258 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
68
+ | 0.9182 | 1.6 | 500 | 0.7687 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
69
+ | 0.8942 | 1.76 | 550 | 0.5787 | 0.3102 | 0.9306 | 0.0 | 0.0 | 0.2900 | 0.8701 | 0.0 | 0.0 |
70
+ | 0.8932 | 1.92 | 600 | 0.4506 | 0.4043 | 0.9628 | 0.0 | 0.25 | 0.3777 | 0.9753 | 0.0 | 0.1579 |
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+ | 0.7448 | 2.08 | 650 | 0.2884 | 0.5323 | 0.9650 | 0.3303 | 0.3017 | 0.7075 | 0.9810 | 0.9474 | 0.1942 |
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+ | 0.6616 | 2.24 | 700 | 0.2162 | 0.8161 | 0.9710 | 0.9724 | 0.5051 | 0.7910 | 0.9824 | 0.9670 | 0.4237 |
73
+ | 0.575 | 2.4 | 750 | 0.1754 | 0.8305 | 0.9714 | 0.9780 | 0.5421 | 0.7961 | 0.9881 | 0.9674 | 0.4328 |
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+ | 0.5246 | 2.56 | 800 | 0.1641 | 0.8102 | 0.9659 | 0.9175 | 0.5472 | 0.7614 | 0.9892 | 0.8558 | 0.4394 |
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+ | 0.481 | 2.72 | 850 | 0.1399 | 0.8407 | 0.9756 | 0.9780 | 0.5686 | 0.8082 | 0.9894 | 0.9674 | 0.4677 |
76
+ | 0.4588 | 2.88 | 900 | 0.1212 | 0.8501 | 0.9786 | 0.9783 | 0.5934 | 0.8247 | 0.9871 | 0.9574 | 0.5294 |
77
+ | 0.4512 | 3.04 | 950 | 0.1388 | 0.8270 | 0.9702 | 0.9836 | 0.5273 | 0.7904 | 0.9893 | 0.9677 | 0.4143 |
78
+ | 0.3894 | 3.2 | 1000 | 0.1270 | 0.8411 | 0.9737 | 0.9836 | 0.5660 | 0.8043 | 0.9905 | 0.9677 | 0.4545 |
79
+ | 0.3772 | 3.36 | 1050 | 0.1267 | 0.8336 | 0.9732 | 0.9890 | 0.5385 | 0.8013 | 0.9882 | 0.9783 | 0.4375 |
80
+ | 0.3528 | 3.52 | 1100 | 0.1073 | 0.8546 | 0.9791 | 0.9890 | 0.5957 | 0.8284 | 0.9883 | 0.9783 | 0.5185 |
81
+ | 0.3694 | 3.68 | 1150 | 0.1120 | 0.8431 | 0.9786 | 0.9890 | 0.5618 | 0.8244 | 0.9849 | 0.9783 | 0.5102 |
82
+ | 0.3146 | 3.84 | 1200 | 0.1189 | 0.8325 | 0.9738 | 0.9836 | 0.54 | 0.8016 | 0.9870 | 0.9677 | 0.45 |
83
+ | 0.3038 | 4.01 | 1250 | 0.1041 | 0.8648 | 0.9815 | 0.9836 | 0.6292 | 0.8425 | 0.9884 | 0.9677 | 0.5714 |
84
+ | 0.2482 | 4.17 | 1300 | 0.1245 | 0.8588 | 0.9773 | 0.9836 | 0.6154 | 0.8202 | 0.9929 | 0.9677 | 0.5 |
85
+ | 0.2388 | 4.33 | 1350 | 0.1167 | 0.8701 | 0.9808 | 0.9836 | 0.6458 | 0.8377 | 0.9918 | 0.9677 | 0.5536 |
86
+ | 0.2593 | 4.49 | 1400 | 0.1215 | 0.8654 | 0.9790 | 0.9836 | 0.6337 | 0.8284 | 0.9929 | 0.9677 | 0.5246 |
87
+ | 0.239 | 4.65 | 1450 | 0.1057 | 0.8621 | 0.9803 | 0.9890 | 0.6170 | 0.8349 | 0.9895 | 0.9783 | 0.5370 |
88
+ | 0.2397 | 4.81 | 1500 | 0.1256 | 0.8544 | 0.9761 | 0.9890 | 0.5981 | 0.8162 | 0.9929 | 0.9783 | 0.4776 |
89
+ | 0.2238 | 4.97 | 1550 | 0.1189 | 0.8701 | 0.9802 | 0.9836 | 0.6465 | 0.8343 | 0.9929 | 0.9677 | 0.5424 |
90
+ | 0.1811 | 5.13 | 1600 | 0.1456 | 0.8438 | 0.9737 | 0.9836 | 0.5741 | 0.8051 | 0.9917 | 0.9677 | 0.4559 |
91
+ | 0.1615 | 5.29 | 1650 | 0.1076 | 0.8780 | 0.9838 | 0.9836 | 0.6667 | 0.8581 | 0.9895 | 0.9677 | 0.6170 |
92
+ | 0.1783 | 5.45 | 1700 | 0.1217 | 0.8869 | 0.9831 | 0.9836 | 0.6939 | 0.8497 | 0.9953 | 0.9677 | 0.5862 |
93
+ | 0.1615 | 5.61 | 1750 | 0.1305 | 0.8770 | 0.9808 | 0.9836 | 0.6667 | 0.8371 | 0.9953 | 0.9677 | 0.5484 |
94
+ | 0.155 | 5.77 | 1800 | 0.1218 | 0.8668 | 0.9821 | 0.9890 | 0.6292 | 0.8460 | 0.9884 | 0.9783 | 0.5714 |
95
+ | 0.167 | 5.93 | 1850 | 0.1091 | 0.8991 | 0.9873 | 0.9890 | 0.7209 | 0.8814 | 0.9919 | 0.9783 | 0.6739 |
96
+ | 0.1455 | 6.09 | 1900 | 0.1338 | 0.8535 | 0.9773 | 0.9890 | 0.5941 | 0.8202 | 0.9906 | 0.9783 | 0.4918 |
97
+ | 0.1301 | 6.25 | 1950 | 0.1321 | 0.8792 | 0.9820 | 0.9890 | 0.6667 | 0.8439 | 0.9941 | 0.9783 | 0.5593 |
98
+ | 0.1049 | 6.41 | 2000 | 0.1181 | 0.9031 | 0.9879 | 0.9834 | 0.7381 | 0.8911 | 0.9908 | 0.9780 | 0.7045 |
99
+ | 0.1403 | 6.57 | 2050 | 0.1432 | 0.8608 | 0.9779 | 0.9890 | 0.6154 | 0.8237 | 0.9929 | 0.9783 | 0.5 |
100
+ | 0.1178 | 6.73 | 2100 | 0.1443 | 0.8937 | 0.9844 | 0.9945 | 0.7021 | 0.8644 | 0.9930 | 0.9890 | 0.6111 |
101
+ | 0.1267 | 6.89 | 2150 | 0.1346 | 0.8494 | 0.9786 | 0.9890 | 0.5806 | 0.8249 | 0.9871 | 0.9783 | 0.5094 |
102
+ | 0.1043 | 7.05 | 2200 | 0.1494 | 0.8905 | 0.9832 | 0.9945 | 0.6939 | 0.8564 | 0.9941 | 0.9890 | 0.5862 |
103
+ | 0.0886 | 7.21 | 2250 | 0.1180 | 0.8946 | 0.9873 | 0.9890 | 0.7073 | 0.8861 | 0.9896 | 0.9783 | 0.6905 |
104
+ | 0.1183 | 7.37 | 2300 | 0.1777 | 0.8720 | 0.9790 | 0.9890 | 0.6481 | 0.8298 | 0.9964 | 0.9783 | 0.5147 |
105
+ | 0.0813 | 7.53 | 2350 | 0.1405 | 0.8912 | 0.9856 | 0.9836 | 0.7045 | 0.8685 | 0.9919 | 0.9677 | 0.6458 |
106
+ | 0.111 | 7.69 | 2400 | 0.1379 | 0.8874 | 0.9838 | 0.9836 | 0.6947 | 0.8540 | 0.9941 | 0.9677 | 0.6 |
107
+ | 0.1199 | 7.85 | 2450 | 0.1301 | 0.9080 | 0.9879 | 0.9890 | 0.7473 | 0.8801 | 0.9953 | 0.9783 | 0.6667 |
108
+ | 0.1054 | 8.01 | 2500 | 0.1478 | 0.8845 | 0.9838 | 0.9890 | 0.6809 | 0.8546 | 0.9930 | 0.9783 | 0.5926 |
109
+ | 0.105 | 8.17 | 2550 | 0.1333 | 0.9021 | 0.9879 | 0.9890 | 0.7294 | 0.8863 | 0.9919 | 0.9783 | 0.6889 |
110
+ | 0.09 | 8.33 | 2600 | 0.1555 | 0.8926 | 0.9855 | 0.9890 | 0.7033 | 0.8662 | 0.9930 | 0.9783 | 0.6275 |
111
+ | 0.0947 | 8.49 | 2650 | 0.1572 | 0.8831 | 0.9856 | 0.9890 | 0.6747 | 0.8726 | 0.9885 | 0.9783 | 0.6512 |
112
+ | 0.0784 | 8.65 | 2700 | 0.1477 | 0.8969 | 0.9873 | 0.9890 | 0.7143 | 0.8836 | 0.9908 | 0.9783 | 0.6818 |
113
+ | 0.0814 | 8.81 | 2750 | 0.1700 | 0.8932 | 0.9861 | 0.9890 | 0.7045 | 0.8720 | 0.9919 | 0.9783 | 0.6458 |
114
+ | 0.0962 | 8.97 | 2800 | 0.1290 | 0.9171 | 0.9896 | 0.9890 | 0.7727 | 0.8940 | 0.9954 | 0.9783 | 0.7083 |
115
+ | 0.0802 | 9.13 | 2850 | 0.1721 | 0.8796 | 0.9832 | 0.9890 | 0.6667 | 0.8517 | 0.9918 | 0.9783 | 0.5849 |
116
+ | 0.0844 | 9.29 | 2900 | 0.1516 | 0.9023 | 0.9867 | 0.9890 | 0.7312 | 0.8717 | 0.9953 | 0.9783 | 0.6415 |
117
+ | 0.0511 | 9.45 | 2950 | 0.1544 | 0.9062 | 0.9879 | 0.9890 | 0.7416 | 0.8820 | 0.9942 | 0.9783 | 0.6735 |
118
+ | 0.0751 | 9.61 | 3000 | 0.1748 | 0.8884 | 0.9832 | 0.9945 | 0.6875 | 0.8571 | 0.9930 | 0.9890 | 0.5893 |
119
+ | 0.0707 | 9.77 | 3050 | 0.1743 | 0.8721 | 0.9802 | 0.9890 | 0.6471 | 0.8349 | 0.9941 | 0.9783 | 0.5323 |
120
+ | 0.0951 | 9.93 | 3100 | 0.1660 | 0.8899 | 0.9850 | 0.9890 | 0.6957 | 0.8622 | 0.9930 | 0.9783 | 0.6154 |
121
+ | 0.0576 | 10.1 | 3150 | 0.2029 | 0.8613 | 0.9766 | 0.9890 | 0.6182 | 0.8197 | 0.9952 | 0.9783 | 0.4857 |
122
+ | 0.0727 | 10.26 | 3200 | 0.1709 | 0.8920 | 0.9849 | 0.9890 | 0.7021 | 0.8612 | 0.9942 | 0.9783 | 0.6111 |
123
+ | 0.0654 | 10.42 | 3250 | 0.1599 | 0.8999 | 0.9861 | 0.9945 | 0.7191 | 0.8780 | 0.9919 | 0.9890 | 0.6531 |
124
+ | 0.0553 | 10.58 | 3300 | 0.2091 | 0.8920 | 0.9849 | 0.9890 | 0.7021 | 0.8612 | 0.9942 | 0.9783 | 0.6111 |
125
 
126
 
127
  ### Framework versions
 
129
  - Transformers 4.11.3
130
  - Pytorch 1.9.0+cu102
131
  - Datasets 1.9.0
132
+ - Tokenizers 0.10.2