File size: 13,095 Bytes
e619ab8 c791d95 e619ab8 b21b0b1 c791d95 4cba74d e619ab8 c791d95 0df5108 adcff3d 0df5108 c791d95 e619ab8 0df5108 e619ab8 c791d95 e619ab8 0df5108 e619ab8 c791d95 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
---
license: mit
language: en
tags:
- generated_from_trainer
model-index:
- name: verdict-classifier-en
results:
- task:
type: text-classification
name: Verdict Classification
widget:
- "Even though it might look true, it has been taken out of context."
---
# English Verdict Classifier
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/).
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 |