File size: 15,608 Bytes
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license: mit
base_model: prajjwal1/bert-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sembr2023-bert-small
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-bert-small
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2324
- Precision: 0.7915
- Recall: 0.8418
- F1: 0.8159
- Iou: 0.6890
- Accuracy: 0.9651
- Balanced Accuracy: 0.9097
- Overall Accuracy: 0.9481
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:--------:|:-----------------:|:----------------:|
| 0.4134 | 0.06 | 10 | 0.4107 | 0 | 0.0 | 0.0 | 0.0 | 0.9080 | 0.5 | 0.9080 |
| 0.371 | 0.12 | 20 | 0.3698 | 0 | 0.0 | 0.0 | 0.0 | 0.9080 | 0.5 | 0.9080 |
| 0.2913 | 0.18 | 30 | 0.2672 | 0.8443 | 0.4167 | 0.5580 | 0.3870 | 0.9393 | 0.7045 | 0.9283 |
| 0.2315 | 0.24 | 40 | 0.2184 | 0.8043 | 0.6761 | 0.7346 | 0.5806 | 0.9551 | 0.8297 | 0.9364 |
| 0.1693 | 0.3 | 50 | 0.2021 | 0.8064 | 0.7375 | 0.7704 | 0.6265 | 0.9596 | 0.8598 | 0.9396 |
| 0.1812 | 0.36 | 60 | 0.1869 | 0.8727 | 0.6847 | 0.7674 | 0.6225 | 0.9618 | 0.8373 | 0.9437 |
| 0.1745 | 0.42 | 70 | 0.1855 | 0.8021 | 0.7744 | 0.7880 | 0.6502 | 0.9617 | 0.8775 | 0.9421 |
| 0.1577 | 0.48 | 80 | 0.1817 | 0.8207 | 0.7641 | 0.7914 | 0.6548 | 0.9630 | 0.8736 | 0.9431 |
| 0.1458 | 0.55 | 90 | 0.1763 | 0.8183 | 0.7869 | 0.8023 | 0.6698 | 0.9643 | 0.8846 | 0.9449 |
| 0.1343 | 0.61 | 100 | 0.1772 | 0.8721 | 0.7372 | 0.7990 | 0.6652 | 0.9659 | 0.8631 | 0.9477 |
| 0.1442 | 0.67 | 110 | 0.1647 | 0.8388 | 0.7795 | 0.8081 | 0.6779 | 0.9659 | 0.8822 | 0.9483 |
| 0.1104 | 0.73 | 120 | 0.1678 | 0.8488 | 0.7679 | 0.8063 | 0.6755 | 0.9661 | 0.8770 | 0.9479 |
| 0.1089 | 0.79 | 130 | 0.1745 | 0.7882 | 0.8262 | 0.8068 | 0.6761 | 0.9636 | 0.9019 | 0.9434 |
| 0.1437 | 0.85 | 140 | 0.1768 | 0.7970 | 0.8206 | 0.8086 | 0.6787 | 0.9643 | 0.8997 | 0.9440 |
| 0.1104 | 0.91 | 150 | 0.1710 | 0.7961 | 0.8275 | 0.8115 | 0.6828 | 0.9646 | 0.9030 | 0.9446 |
| 0.0941 | 0.97 | 160 | 0.1647 | 0.8007 | 0.8167 | 0.8086 | 0.6787 | 0.9644 | 0.8980 | 0.9456 |
| 0.1146 | 1.03 | 170 | 0.1744 | 0.8026 | 0.8250 | 0.8136 | 0.6858 | 0.9652 | 0.9022 | 0.9456 |
| 0.0982 | 1.09 | 180 | 0.1636 | 0.8175 | 0.8191 | 0.8183 | 0.6925 | 0.9666 | 0.9003 | 0.9468 |
| 0.0875 | 1.15 | 190 | 0.1653 | 0.8305 | 0.8064 | 0.8183 | 0.6924 | 0.9671 | 0.8948 | 0.9476 |
| 0.0962 | 1.21 | 200 | 0.1610 | 0.8340 | 0.8076 | 0.8206 | 0.6958 | 0.9675 | 0.8957 | 0.9490 |
| 0.084 | 1.27 | 210 | 0.1671 | 0.8232 | 0.8177 | 0.8204 | 0.6955 | 0.9671 | 0.9000 | 0.9476 |
| 0.07 | 1.33 | 220 | 0.1665 | 0.7909 | 0.8545 | 0.8215 | 0.6971 | 0.9658 | 0.9158 | 0.9454 |
| 0.0785 | 1.39 | 230 | 0.1612 | 0.8411 | 0.8004 | 0.8202 | 0.6953 | 0.9677 | 0.8925 | 0.9496 |
| 0.0712 | 1.45 | 240 | 0.1638 | 0.8251 | 0.8161 | 0.8205 | 0.6957 | 0.9672 | 0.8993 | 0.9491 |
| 0.0683 | 1.52 | 250 | 0.1823 | 0.8097 | 0.8262 | 0.8179 | 0.6919 | 0.9662 | 0.9033 | 0.9463 |
| 0.0694 | 1.58 | 260 | 0.1717 | 0.8028 | 0.8408 | 0.8214 | 0.6969 | 0.9664 | 0.9099 | 0.9474 |
| 0.0809 | 1.64 | 270 | 0.1681 | 0.8304 | 0.8102 | 0.8202 | 0.6952 | 0.9673 | 0.8967 | 0.9491 |
| 0.0586 | 1.7 | 280 | 0.1811 | 0.8096 | 0.8391 | 0.8241 | 0.7008 | 0.9671 | 0.9096 | 0.9478 |
| 0.069 | 1.76 | 290 | 0.1855 | 0.8088 | 0.8284 | 0.8185 | 0.6928 | 0.9662 | 0.9043 | 0.9478 |
| 0.0739 | 1.82 | 300 | 0.1876 | 0.8148 | 0.8209 | 0.8178 | 0.6918 | 0.9664 | 0.9010 | 0.9476 |
| 0.0691 | 1.88 | 310 | 0.1741 | 0.8173 | 0.8206 | 0.8190 | 0.6934 | 0.9666 | 0.9010 | 0.9485 |
| 0.0728 | 1.94 | 320 | 0.1765 | 0.7941 | 0.8346 | 0.8139 | 0.6861 | 0.9649 | 0.9064 | 0.9469 |
| 0.0585 | 2.0 | 330 | 0.1800 | 0.8118 | 0.8166 | 0.8142 | 0.6866 | 0.9657 | 0.8987 | 0.9483 |
| 0.0602 | 2.06 | 340 | 0.1930 | 0.7969 | 0.8366 | 0.8162 | 0.6895 | 0.9654 | 0.9075 | 0.9461 |
| 0.0557 | 2.12 | 350 | 0.1832 | 0.7915 | 0.8401 | 0.8151 | 0.6879 | 0.9649 | 0.9089 | 0.9472 |
| 0.0491 | 2.18 | 360 | 0.1914 | 0.8131 | 0.8136 | 0.8134 | 0.6854 | 0.9657 | 0.8973 | 0.9489 |
| 0.0413 | 2.24 | 370 | 0.2116 | 0.7989 | 0.8288 | 0.8136 | 0.6857 | 0.9651 | 0.9038 | 0.9463 |
| 0.051 | 2.3 | 380 | 0.2073 | 0.7864 | 0.8454 | 0.8148 | 0.6875 | 0.9647 | 0.9111 | 0.9460 |
| 0.0529 | 2.36 | 390 | 0.1923 | 0.8103 | 0.8278 | 0.8190 | 0.6934 | 0.9663 | 0.9041 | 0.9496 |
| 0.0469 | 2.42 | 400 | 0.1808 | 0.8131 | 0.8217 | 0.8173 | 0.6911 | 0.9662 | 0.9013 | 0.9497 |
| 0.0579 | 2.48 | 410 | 0.2053 | 0.7795 | 0.8493 | 0.8129 | 0.6848 | 0.9640 | 0.9125 | 0.9464 |
| 0.0494 | 2.55 | 420 | 0.1953 | 0.7872 | 0.8457 | 0.8154 | 0.6883 | 0.9648 | 0.9113 | 0.9471 |
| 0.0468 | 2.61 | 430 | 0.1972 | 0.8064 | 0.8182 | 0.8123 | 0.6839 | 0.9652 | 0.8992 | 0.9488 |
| 0.0545 | 2.67 | 440 | 0.2116 | 0.7774 | 0.8455 | 0.8100 | 0.6807 | 0.9635 | 0.9105 | 0.9458 |
| 0.0544 | 2.73 | 450 | 0.1954 | 0.7868 | 0.8455 | 0.8151 | 0.6879 | 0.9647 | 0.9111 | 0.9472 |
| 0.044 | 2.79 | 460 | 0.2046 | 0.8149 | 0.8203 | 0.8175 | 0.6914 | 0.9663 | 0.9007 | 0.9491 |
| 0.0468 | 2.85 | 470 | 0.2036 | 0.8031 | 0.8321 | 0.8174 | 0.6911 | 0.9658 | 0.9057 | 0.9483 |
| 0.0457 | 2.91 | 480 | 0.1998 | 0.7923 | 0.8377 | 0.8144 | 0.6869 | 0.9649 | 0.9077 | 0.9479 |
| 0.0435 | 2.97 | 490 | 0.2077 | 0.7864 | 0.8432 | 0.8138 | 0.6860 | 0.9645 | 0.9100 | 0.9475 |
| 0.0489 | 3.03 | 500 | 0.2067 | 0.7933 | 0.8339 | 0.8131 | 0.6850 | 0.9647 | 0.9059 | 0.9478 |
| 0.0472 | 3.09 | 510 | 0.2204 | 0.7883 | 0.8464 | 0.8163 | 0.6896 | 0.9650 | 0.9117 | 0.9475 |
| 0.0469 | 3.15 | 520 | 0.2209 | 0.7821 | 0.8470 | 0.8132 | 0.6853 | 0.9642 | 0.9115 | 0.9467 |
| 0.0384 | 3.21 | 530 | 0.2147 | 0.7923 | 0.8367 | 0.8139 | 0.6862 | 0.9648 | 0.9072 | 0.9479 |
| 0.0322 | 3.27 | 540 | 0.2215 | 0.7842 | 0.8489 | 0.8153 | 0.6881 | 0.9646 | 0.9126 | 0.9475 |
| 0.0429 | 3.33 | 550 | 0.2184 | 0.7743 | 0.8504 | 0.8106 | 0.6815 | 0.9634 | 0.9127 | 0.9463 |
| 0.0348 | 3.39 | 560 | 0.2293 | 0.7642 | 0.8594 | 0.8090 | 0.6792 | 0.9627 | 0.9163 | 0.9451 |
| 0.0365 | 3.45 | 570 | 0.2221 | 0.7922 | 0.8411 | 0.8159 | 0.6891 | 0.9651 | 0.9094 | 0.9477 |
| 0.0374 | 3.52 | 580 | 0.2175 | 0.7917 | 0.8382 | 0.8143 | 0.6868 | 0.9648 | 0.9079 | 0.9479 |
| 0.0413 | 3.58 | 590 | 0.2111 | 0.8122 | 0.8243 | 0.8182 | 0.6924 | 0.9663 | 0.9025 | 0.9499 |
| 0.0362 | 3.64 | 600 | 0.2183 | 0.7883 | 0.8404 | 0.8135 | 0.6856 | 0.9646 | 0.9088 | 0.9479 |
| 0.0352 | 3.7 | 610 | 0.2124 | 0.8005 | 0.8340 | 0.8169 | 0.6905 | 0.9656 | 0.9065 | 0.9487 |
| 0.0301 | 3.76 | 620 | 0.2145 | 0.7993 | 0.8369 | 0.8177 | 0.6916 | 0.9657 | 0.9078 | 0.9488 |
| 0.0399 | 3.82 | 630 | 0.2188 | 0.8018 | 0.8318 | 0.8166 | 0.6900 | 0.9656 | 0.9055 | 0.9485 |
| 0.0366 | 3.88 | 640 | 0.2211 | 0.7969 | 0.8346 | 0.8153 | 0.6882 | 0.9652 | 0.9066 | 0.9478 |
| 0.0289 | 3.94 | 650 | 0.2201 | 0.7850 | 0.8468 | 0.8147 | 0.6874 | 0.9646 | 0.9116 | 0.9475 |
| 0.0367 | 4.0 | 660 | 0.2280 | 0.7859 | 0.8437 | 0.8138 | 0.6860 | 0.9645 | 0.9102 | 0.9475 |
| 0.0362 | 4.06 | 670 | 0.2226 | 0.7785 | 0.8502 | 0.8128 | 0.6846 | 0.9640 | 0.9128 | 0.9469 |
| 0.0376 | 4.12 | 680 | 0.2213 | 0.8006 | 0.8317 | 0.8159 | 0.6890 | 0.9655 | 0.9054 | 0.9490 |
| 0.0294 | 4.18 | 690 | 0.2212 | 0.8052 | 0.8271 | 0.8160 | 0.6892 | 0.9657 | 0.9034 | 0.9492 |
| 0.0318 | 4.24 | 700 | 0.2254 | 0.7874 | 0.8420 | 0.8138 | 0.6860 | 0.9646 | 0.9095 | 0.9477 |
| 0.0359 | 4.3 | 710 | 0.2250 | 0.7899 | 0.8432 | 0.8157 | 0.6887 | 0.9649 | 0.9102 | 0.9479 |
| 0.034 | 4.36 | 720 | 0.2264 | 0.7985 | 0.8380 | 0.8178 | 0.6917 | 0.9656 | 0.9083 | 0.9489 |
| 0.0334 | 4.42 | 730 | 0.2308 | 0.7871 | 0.8436 | 0.8144 | 0.6869 | 0.9646 | 0.9102 | 0.9475 |
| 0.036 | 4.48 | 740 | 0.2250 | 0.7936 | 0.8404 | 0.8163 | 0.6896 | 0.9652 | 0.9091 | 0.9485 |
| 0.0257 | 4.55 | 750 | 0.2267 | 0.7861 | 0.8456 | 0.8148 | 0.6874 | 0.9646 | 0.9112 | 0.9479 |
| 0.0354 | 4.61 | 760 | 0.2288 | 0.7943 | 0.8401 | 0.8166 | 0.6900 | 0.9653 | 0.9090 | 0.9484 |
| 0.0373 | 4.67 | 770 | 0.2320 | 0.7828 | 0.8471 | 0.8137 | 0.6859 | 0.9643 | 0.9117 | 0.9470 |
| 0.0272 | 4.73 | 780 | 0.2250 | 0.7994 | 0.8354 | 0.8170 | 0.6906 | 0.9656 | 0.9071 | 0.9487 |
| 0.034 | 4.79 | 790 | 0.2339 | 0.7861 | 0.8450 | 0.8145 | 0.6870 | 0.9646 | 0.9108 | 0.9473 |
| 0.0294 | 4.85 | 800 | 0.2262 | 0.7972 | 0.8381 | 0.8171 | 0.6908 | 0.9655 | 0.9082 | 0.9486 |
| 0.0353 | 4.91 | 810 | 0.2337 | 0.7833 | 0.8473 | 0.8140 | 0.6864 | 0.9644 | 0.9118 | 0.9472 |
| 0.0337 | 4.97 | 820 | 0.2273 | 0.7973 | 0.8372 | 0.8168 | 0.6903 | 0.9655 | 0.9078 | 0.9485 |
| 0.0309 | 5.03 | 830 | 0.2318 | 0.7917 | 0.8413 | 0.8157 | 0.6888 | 0.9650 | 0.9094 | 0.9481 |
| 0.026 | 5.09 | 840 | 0.2327 | 0.7932 | 0.8418 | 0.8168 | 0.6903 | 0.9653 | 0.9098 | 0.9483 |
| 0.0271 | 5.15 | 850 | 0.2317 | 0.7887 | 0.8459 | 0.8163 | 0.6896 | 0.9650 | 0.9115 | 0.9479 |
| 0.0352 | 5.21 | 860 | 0.2344 | 0.7914 | 0.8427 | 0.8162 | 0.6895 | 0.9651 | 0.9101 | 0.9481 |
| 0.0268 | 5.27 | 870 | 0.2306 | 0.7931 | 0.8417 | 0.8166 | 0.6901 | 0.9652 | 0.9097 | 0.9484 |
| 0.0248 | 5.33 | 880 | 0.2309 | 0.7889 | 0.8438 | 0.8155 | 0.6884 | 0.9649 | 0.9105 | 0.9480 |
| 0.0331 | 5.39 | 890 | 0.2306 | 0.7895 | 0.8432 | 0.8154 | 0.6884 | 0.9649 | 0.9102 | 0.9480 |
| 0.0265 | 5.45 | 900 | 0.2322 | 0.7944 | 0.8401 | 0.8166 | 0.6901 | 0.9653 | 0.9091 | 0.9484 |
| 0.0352 | 5.52 | 910 | 0.2326 | 0.7922 | 0.8419 | 0.8163 | 0.6896 | 0.9651 | 0.9098 | 0.9482 |
| 0.0368 | 5.58 | 920 | 0.2313 | 0.7911 | 0.8424 | 0.8160 | 0.6891 | 0.9651 | 0.9099 | 0.9481 |
| 0.0315 | 5.64 | 930 | 0.2313 | 0.7917 | 0.8420 | 0.8161 | 0.6893 | 0.9651 | 0.9098 | 0.9482 |
| 0.0251 | 5.7 | 940 | 0.2324 | 0.7919 | 0.8409 | 0.8156 | 0.6887 | 0.9650 | 0.9093 | 0.9481 |
| 0.0331 | 5.76 | 950 | 0.2327 | 0.7913 | 0.8414 | 0.8156 | 0.6886 | 0.9650 | 0.9095 | 0.9481 |
| 0.0361 | 5.82 | 960 | 0.2327 | 0.7904 | 0.8423 | 0.8155 | 0.6885 | 0.9650 | 0.9098 | 0.9480 |
| 0.0362 | 5.88 | 970 | 0.2325 | 0.7909 | 0.8419 | 0.8156 | 0.6887 | 0.9650 | 0.9097 | 0.9481 |
| 0.031 | 5.94 | 980 | 0.2324 | 0.7914 | 0.8418 | 0.8158 | 0.6889 | 0.9650 | 0.9097 | 0.9481 |
| 0.0316 | 6.0 | 990 | 0.2324 | 0.7915 | 0.8418 | 0.8159 | 0.6890 | 0.9651 | 0.9097 | 0.9481 |
| 0.0232 | 6.06 | 1000 | 0.2324 | 0.7915 | 0.8418 | 0.8159 | 0.6890 | 0.9651 | 0.9097 | 0.9481 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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