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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sembr2023-distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sembr2023-distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2391
- Precision: 0.8025
- Recall: 0.8289
- F1: 0.8155
- Iou: 0.6885
- Accuracy: 0.9655
- Balanced Accuracy: 0.9041
- Overall Accuracy: 0.9498
## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Iou | Accuracy | Balanced Accuracy | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:--------:|:-----------------:|:----------------:|
| 0.4046 | 0.06 | 10 | 0.4081 | 0 | 0.0 | 0.0 | 0.0 | 0.9080 | 0.5 | 0.9080 |
| 0.376 | 0.12 | 20 | 0.3776 | 0 | 0.0 | 0.0 | 0.0 | 0.9080 | 0.5 | 0.9080 |
| 0.3291 | 0.18 | 30 | 0.2954 | 0.8417 | 0.0813 | 0.1482 | 0.0801 | 0.9141 | 0.5399 | 0.9134 |
| 0.2309 | 0.24 | 40 | 0.2165 | 0.8158 | 0.6008 | 0.6920 | 0.5290 | 0.9508 | 0.7935 | 0.9357 |
| 0.1704 | 0.3 | 50 | 0.1928 | 0.8703 | 0.6428 | 0.7394 | 0.5866 | 0.9583 | 0.8166 | 0.9420 |
| 0.1711 | 0.36 | 60 | 0.1810 | 0.7918 | 0.7620 | 0.7766 | 0.6348 | 0.9597 | 0.8709 | 0.9404 |
| 0.1716 | 0.42 | 70 | 0.1841 | 0.8143 | 0.7735 | 0.7934 | 0.6575 | 0.9629 | 0.8778 | 0.9426 |
| 0.1452 | 0.48 | 80 | 0.1610 | 0.9013 | 0.6827 | 0.7769 | 0.6352 | 0.9639 | 0.8376 | 0.9486 |
| 0.1233 | 0.55 | 90 | 0.1604 | 0.8569 | 0.7587 | 0.8048 | 0.6734 | 0.9662 | 0.8729 | 0.9487 |
| 0.1291 | 0.61 | 100 | 0.1694 | 0.8405 | 0.7794 | 0.8088 | 0.6789 | 0.9661 | 0.8822 | 0.9475 |
| 0.1335 | 0.67 | 110 | 0.1529 | 0.8730 | 0.7445 | 0.8036 | 0.6717 | 0.9665 | 0.8667 | 0.9494 |
| 0.0921 | 0.73 | 120 | 0.1675 | 0.8238 | 0.8090 | 0.8164 | 0.6897 | 0.9665 | 0.8958 | 0.9462 |
| 0.0936 | 0.79 | 130 | 0.1469 | 0.8672 | 0.7738 | 0.8178 | 0.6918 | 0.9683 | 0.8809 | 0.9520 |
| 0.1168 | 0.85 | 140 | 0.1570 | 0.8190 | 0.8088 | 0.8139 | 0.6862 | 0.9660 | 0.8953 | 0.9483 |
| 0.091 | 0.91 | 150 | 0.1630 | 0.8330 | 0.7842 | 0.8079 | 0.6777 | 0.9657 | 0.8841 | 0.9477 |
| 0.0795 | 0.97 | 160 | 0.1413 | 0.8477 | 0.7934 | 0.8196 | 0.6944 | 0.9679 | 0.8895 | 0.9539 |
| 0.0927 | 1.03 | 170 | 0.1678 | 0.8071 | 0.8218 | 0.8144 | 0.6869 | 0.9655 | 0.9009 | 0.9467 |
| 0.0748 | 1.09 | 180 | 0.1346 | 0.8785 | 0.7628 | 0.8166 | 0.6900 | 0.9685 | 0.8761 | 0.9563 |
| 0.0731 | 1.15 | 190 | 0.1691 | 0.8290 | 0.8018 | 0.8152 | 0.6880 | 0.9666 | 0.8925 | 0.9485 |
| 0.0842 | 1.21 | 200 | 0.1561 | 0.8264 | 0.8127 | 0.8195 | 0.6942 | 0.9671 | 0.8977 | 0.9512 |
| 0.0702 | 1.27 | 210 | 0.1419 | 0.8459 | 0.7931 | 0.8187 | 0.6930 | 0.9677 | 0.8892 | 0.9539 |
| 0.0577 | 1.33 | 220 | 0.1506 | 0.8375 | 0.8057 | 0.8213 | 0.6968 | 0.9678 | 0.8950 | 0.9521 |
| 0.0596 | 1.39 | 230 | 0.1628 | 0.7907 | 0.8377 | 0.8135 | 0.6856 | 0.9647 | 0.9076 | 0.9485 |
| 0.0562 | 1.45 | 240 | 0.1585 | 0.8413 | 0.7953 | 0.8176 | 0.6915 | 0.9674 | 0.8900 | 0.9518 |
| 0.0519 | 1.52 | 250 | 0.1740 | 0.8062 | 0.8189 | 0.8125 | 0.6842 | 0.9652 | 0.8995 | 0.9490 |
| 0.0547 | 1.58 | 260 | 0.1751 | 0.8593 | 0.7752 | 0.8151 | 0.6879 | 0.9677 | 0.8812 | 0.9521 |
| 0.0604 | 1.64 | 270 | 0.1693 | 0.8155 | 0.8116 | 0.8136 | 0.6857 | 0.9658 | 0.8965 | 0.9503 |
| 0.043 | 1.7 | 280 | 0.1939 | 0.8106 | 0.8264 | 0.8184 | 0.6926 | 0.9663 | 0.9034 | 0.9490 |
| 0.0538 | 1.76 | 290 | 0.1800 | 0.8320 | 0.8146 | 0.8232 | 0.6996 | 0.9678 | 0.8990 | 0.9515 |
| 0.0557 | 1.82 | 300 | 0.1762 | 0.7867 | 0.8289 | 0.8073 | 0.6768 | 0.9636 | 0.9031 | 0.9473 |
| 0.0493 | 1.88 | 310 | 0.1646 | 0.8411 | 0.7894 | 0.8144 | 0.6870 | 0.9669 | 0.8872 | 0.9519 |
| 0.0524 | 1.94 | 320 | 0.1674 | 0.8155 | 0.8205 | 0.8180 | 0.6921 | 0.9664 | 0.9009 | 0.9507 |
| 0.0445 | 2.0 | 330 | 0.1616 | 0.8186 | 0.8075 | 0.8130 | 0.6849 | 0.9658 | 0.8947 | 0.9511 |
| 0.0399 | 2.06 | 340 | 0.2141 | 0.7818 | 0.8352 | 0.8076 | 0.6773 | 0.9634 | 0.9058 | 0.9455 |
| 0.039 | 2.12 | 350 | 0.1765 | 0.8189 | 0.8134 | 0.8161 | 0.6894 | 0.9663 | 0.8976 | 0.9509 |
| 0.0353 | 2.18 | 360 | 0.1900 | 0.8097 | 0.8130 | 0.8113 | 0.6826 | 0.9652 | 0.8968 | 0.9495 |
| 0.0269 | 2.24 | 370 | 0.2080 | 0.8338 | 0.7953 | 0.8141 | 0.6865 | 0.9666 | 0.8896 | 0.9508 |
| 0.0333 | 2.3 | 380 | 0.2032 | 0.7753 | 0.8341 | 0.8036 | 0.6717 | 0.9625 | 0.9048 | 0.9466 |
| 0.0339 | 2.36 | 390 | 0.1867 | 0.8309 | 0.8045 | 0.8175 | 0.6913 | 0.9670 | 0.8939 | 0.9520 |
| 0.035 | 2.42 | 400 | 0.1826 | 0.8078 | 0.8213 | 0.8145 | 0.6870 | 0.9656 | 0.9007 | 0.9500 |
| 0.039 | 2.48 | 410 | 0.2028 | 0.7762 | 0.8335 | 0.8038 | 0.6720 | 0.9626 | 0.9046 | 0.9466 |
| 0.0303 | 2.55 | 420 | 0.1964 | 0.8096 | 0.8153 | 0.8124 | 0.6841 | 0.9654 | 0.8979 | 0.9495 |
| 0.0296 | 2.61 | 430 | 0.1908 | 0.7883 | 0.8349 | 0.8109 | 0.6820 | 0.9642 | 0.9061 | 0.9485 |
| 0.0356 | 2.67 | 440 | 0.2016 | 0.8151 | 0.8140 | 0.8145 | 0.6871 | 0.9659 | 0.8977 | 0.9497 |
| 0.0343 | 2.73 | 450 | 0.1963 | 0.8016 | 0.8248 | 0.8130 | 0.6850 | 0.9651 | 0.9021 | 0.9497 |
| 0.0271 | 2.79 | 460 | 0.2077 | 0.8172 | 0.8127 | 0.8150 | 0.6877 | 0.9661 | 0.8972 | 0.9500 |
| 0.0311 | 2.85 | 470 | 0.1889 | 0.8080 | 0.8254 | 0.8166 | 0.6900 | 0.9659 | 0.9027 | 0.9512 |
| 0.029 | 2.91 | 480 | 0.1995 | 0.7698 | 0.8443 | 0.8053 | 0.6741 | 0.9625 | 0.9094 | 0.9469 |
| 0.0265 | 2.97 | 490 | 0.1828 | 0.8245 | 0.8148 | 0.8196 | 0.6944 | 0.9670 | 0.8986 | 0.9528 |
| 0.0281 | 3.03 | 500 | 0.2061 | 0.8321 | 0.8070 | 0.8194 | 0.6940 | 0.9673 | 0.8953 | 0.9520 |
| 0.0273 | 3.09 | 510 | 0.2072 | 0.8105 | 0.8218 | 0.8161 | 0.6894 | 0.9659 | 0.9012 | 0.9502 |
| 0.0268 | 3.15 | 520 | 0.2113 | 0.8084 | 0.8222 | 0.8152 | 0.6881 | 0.9657 | 0.9012 | 0.9502 |
| 0.0241 | 3.21 | 530 | 0.2015 | 0.8318 | 0.8038 | 0.8176 | 0.6914 | 0.9670 | 0.8937 | 0.9524 |
| 0.0198 | 3.27 | 540 | 0.2252 | 0.7922 | 0.8331 | 0.8122 | 0.6837 | 0.9646 | 0.9055 | 0.9483 |
| 0.0268 | 3.33 | 550 | 0.2107 | 0.7931 | 0.8343 | 0.8132 | 0.6852 | 0.9647 | 0.9061 | 0.9493 |
| 0.0185 | 3.39 | 560 | 0.2202 | 0.7874 | 0.8311 | 0.8086 | 0.6787 | 0.9638 | 0.9042 | 0.9485 |
| 0.0224 | 3.45 | 570 | 0.2256 | 0.8057 | 0.8247 | 0.8151 | 0.6879 | 0.9656 | 0.9023 | 0.9497 |
| 0.0189 | 3.52 | 580 | 0.2125 | 0.8111 | 0.8204 | 0.8157 | 0.6888 | 0.9659 | 0.9005 | 0.9504 |
| 0.026 | 3.58 | 590 | 0.2163 | 0.8122 | 0.8182 | 0.8152 | 0.6881 | 0.9659 | 0.8995 | 0.9506 |
| 0.0184 | 3.64 | 600 | 0.2143 | 0.8057 | 0.8204 | 0.8130 | 0.6849 | 0.9653 | 0.9002 | 0.9502 |
| 0.0196 | 3.7 | 610 | 0.2113 | 0.8171 | 0.8164 | 0.8167 | 0.6903 | 0.9663 | 0.8990 | 0.9509 |
| 0.0205 | 3.76 | 620 | 0.2081 | 0.8127 | 0.8213 | 0.8170 | 0.6906 | 0.9662 | 0.9011 | 0.9512 |
| 0.0231 | 3.82 | 630 | 0.2200 | 0.8170 | 0.8225 | 0.8198 | 0.6946 | 0.9667 | 0.9019 | 0.9508 |
| 0.0209 | 3.88 | 640 | 0.2179 | 0.8148 | 0.8261 | 0.8204 | 0.6955 | 0.9667 | 0.9035 | 0.9512 |
| 0.0164 | 3.94 | 650 | 0.2201 | 0.8338 | 0.8124 | 0.8229 | 0.6992 | 0.9679 | 0.8980 | 0.9522 |
| 0.0222 | 4.0 | 660 | 0.2174 | 0.8193 | 0.8124 | 0.8158 | 0.6890 | 0.9663 | 0.8971 | 0.9505 |
| 0.0209 | 4.06 | 670 | 0.2169 | 0.7975 | 0.8336 | 0.8151 | 0.6880 | 0.9652 | 0.9061 | 0.9502 |
| 0.0165 | 4.12 | 680 | 0.2248 | 0.7973 | 0.8317 | 0.8141 | 0.6865 | 0.9651 | 0.9052 | 0.9494 |
| 0.0136 | 4.18 | 690 | 0.2254 | 0.8073 | 0.8245 | 0.8158 | 0.6889 | 0.9658 | 0.9023 | 0.9501 |
| 0.016 | 4.24 | 700 | 0.2259 | 0.8029 | 0.8231 | 0.8129 | 0.6847 | 0.9651 | 0.9013 | 0.9499 |
| 0.018 | 4.3 | 710 | 0.2291 | 0.8013 | 0.8264 | 0.8136 | 0.6858 | 0.9652 | 0.9028 | 0.9496 |
| 0.0148 | 4.36 | 720 | 0.2316 | 0.7923 | 0.8283 | 0.8099 | 0.6806 | 0.9642 | 0.9031 | 0.9483 |
| 0.0163 | 4.42 | 730 | 0.2423 | 0.7882 | 0.8321 | 0.8096 | 0.6801 | 0.9640 | 0.9047 | 0.9475 |
| 0.0162 | 4.48 | 740 | 0.2312 | 0.8042 | 0.8243 | 0.8141 | 0.6865 | 0.9654 | 0.9020 | 0.9496 |
| 0.0121 | 4.55 | 750 | 0.2355 | 0.8003 | 0.8257 | 0.8128 | 0.6847 | 0.9650 | 0.9024 | 0.9494 |
| 0.0182 | 4.61 | 760 | 0.2393 | 0.7911 | 0.8313 | 0.8107 | 0.6817 | 0.9643 | 0.9046 | 0.9485 |
| 0.0182 | 4.67 | 770 | 0.2292 | 0.7954 | 0.8308 | 0.8127 | 0.6845 | 0.9648 | 0.9046 | 0.9494 |
| 0.0153 | 4.73 | 780 | 0.2312 | 0.8043 | 0.8266 | 0.8153 | 0.6882 | 0.9656 | 0.9031 | 0.9498 |
| 0.0182 | 4.79 | 790 | 0.2410 | 0.7953 | 0.8326 | 0.8135 | 0.6857 | 0.9649 | 0.9055 | 0.9489 |
| 0.0133 | 4.85 | 800 | 0.2350 | 0.8110 | 0.8219 | 0.8164 | 0.6898 | 0.9660 | 0.9013 | 0.9505 |
| 0.0185 | 4.91 | 810 | 0.2432 | 0.7912 | 0.8363 | 0.8132 | 0.6851 | 0.9647 | 0.9070 | 0.9486 |
| 0.0157 | 4.97 | 820 | 0.2318 | 0.8103 | 0.8214 | 0.8158 | 0.6889 | 0.9659 | 0.9010 | 0.9503 |
| 0.0171 | 5.03 | 830 | 0.2392 | 0.7992 | 0.8294 | 0.8140 | 0.6864 | 0.9651 | 0.9042 | 0.9492 |
| 0.0121 | 5.09 | 840 | 0.2422 | 0.8043 | 0.8271 | 0.8155 | 0.6885 | 0.9656 | 0.9034 | 0.9497 |
| 0.0103 | 5.15 | 850 | 0.2382 | 0.8009 | 0.8296 | 0.8150 | 0.6877 | 0.9654 | 0.9043 | 0.9495 |
| 0.0148 | 5.21 | 860 | 0.2358 | 0.8057 | 0.8256 | 0.8155 | 0.6885 | 0.9656 | 0.9027 | 0.9501 |
| 0.0115 | 5.27 | 870 | 0.2398 | 0.8008 | 0.8290 | 0.8147 | 0.6873 | 0.9653 | 0.9041 | 0.9497 |
| 0.0115 | 5.33 | 880 | 0.2385 | 0.8035 | 0.8245 | 0.8138 | 0.6861 | 0.9653 | 0.9020 | 0.9496 |
| 0.0122 | 5.39 | 890 | 0.2387 | 0.7983 | 0.8303 | 0.8140 | 0.6863 | 0.9651 | 0.9045 | 0.9494 |
| 0.0134 | 5.45 | 900 | 0.2395 | 0.8009 | 0.8279 | 0.8142 | 0.6866 | 0.9652 | 0.9035 | 0.9495 |
| 0.0169 | 5.52 | 910 | 0.2391 | 0.8012 | 0.8282 | 0.8145 | 0.6870 | 0.9653 | 0.9037 | 0.9496 |
| 0.0178 | 5.58 | 920 | 0.2403 | 0.7997 | 0.8290 | 0.8141 | 0.6865 | 0.9652 | 0.9040 | 0.9493 |
| 0.0162 | 5.64 | 930 | 0.2391 | 0.8017 | 0.8274 | 0.8143 | 0.6868 | 0.9653 | 0.9033 | 0.9496 |
| 0.0115 | 5.7 | 940 | 0.2384 | 0.8035 | 0.8265 | 0.8148 | 0.6875 | 0.9655 | 0.9030 | 0.9498 |
| 0.0175 | 5.76 | 950 | 0.2386 | 0.8029 | 0.8282 | 0.8153 | 0.6882 | 0.9655 | 0.9038 | 0.9498 |
| 0.0165 | 5.82 | 960 | 0.2390 | 0.8017 | 0.8288 | 0.8150 | 0.6878 | 0.9654 | 0.9040 | 0.9497 |
| 0.0186 | 5.88 | 970 | 0.2389 | 0.8021 | 0.8289 | 0.8153 | 0.6882 | 0.9655 | 0.9041 | 0.9498 |
| 0.0124 | 5.94 | 980 | 0.2390 | 0.8024 | 0.8288 | 0.8154 | 0.6883 | 0.9655 | 0.9041 | 0.9498 |
| 0.0138 | 6.0 | 990 | 0.2391 | 0.8025 | 0.8289 | 0.8155 | 0.6885 | 0.9655 | 0.9041 | 0.9498 |
| 0.0113 | 6.06 | 1000 | 0.2391 | 0.8025 | 0.8289 | 0.8155 | 0.6885 | 0.9655 | 0.9041 | 0.9498 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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