File size: 16,163 Bytes
23378f7 d6a4d88 23378f7 d6a4d88 23378f7 d6a4d88 23378f7 d6a4d88 23378f7 d6a4d88 23378f7 d6a4d88 23378f7 d6a4d88 23378f7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- sembr2023
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sembr2023-distilbert-base-uncased
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sembr2023
type: sembr2023
config: sembr2023
split: test
args: sembr2023
metrics:
- name: Precision
type: precision
value: 0.7588342440801458
- name: Recall
type: recall
value: 0.8253863426231673
- name: F1
type: f1
value: 0.790712387700873
- name: Accuracy
type: accuracy
value: 0.9613411398987951
---
<!-- 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 the sembr2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2562
- Precision: 0.7588
- Recall: 0.8254
- F1: 0.7907
- Iou: 0.6539
- Accuracy: 0.9613
- Balanced Accuracy: 0.9000
- Overall Accuracy: 0.9468
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:--------:|:-----------------:|:----------------:|
| 0.4048 | 0.07 | 10 | 0.4102 | 0 | 0.0 | 0.0 | 0.0 | 0.9115 | 0.5 | 0.9115 |
| 0.3834 | 0.14 | 20 | 0.3950 | 0 | 0.0 | 0.0 | 0.0 | 0.9115 | 0.5 | 0.9115 |
| 0.3817 | 0.21 | 30 | 0.3616 | 0 | 0.0 | 0.0 | 0.0 | 0.9115 | 0.5 | 0.9115 |
| 0.2982 | 0.28 | 40 | 0.3127 | 0 | 0.0 | 0.0 | 0.0 | 0.9115 | 0.5 | 0.9115 |
| 0.2505 | 0.35 | 50 | 0.2716 | 0.6064 | 0.7118 | 0.6549 | 0.4869 | 0.9336 | 0.8335 | 0.9149 |
| 0.2176 | 0.42 | 60 | 0.2119 | 0.8427 | 0.5653 | 0.6767 | 0.5114 | 0.9522 | 0.7775 | 0.9411 |
| 0.2018 | 0.49 | 70 | 0.2031 | 0.7785 | 0.7056 | 0.7402 | 0.5876 | 0.9562 | 0.8430 | 0.9391 |
| 0.1609 | 0.56 | 80 | 0.1825 | 0.8199 | 0.6992 | 0.7548 | 0.6061 | 0.9598 | 0.8422 | 0.9437 |
| 0.153 | 0.62 | 90 | 0.1820 | 0.7830 | 0.7362 | 0.7589 | 0.6115 | 0.9586 | 0.8582 | 0.9421 |
| 0.1336 | 0.69 | 100 | 0.1760 | 0.7955 | 0.7323 | 0.7626 | 0.6163 | 0.9597 | 0.8570 | 0.9447 |
| 0.1351 | 0.76 | 110 | 0.1691 | 0.8314 | 0.7243 | 0.7742 | 0.6316 | 0.9626 | 0.8550 | 0.9460 |
| 0.1393 | 0.83 | 120 | 0.1742 | 0.8028 | 0.7506 | 0.7758 | 0.6338 | 0.9616 | 0.8664 | 0.9454 |
| 0.1013 | 0.9 | 130 | 0.1657 | 0.7991 | 0.7608 | 0.7795 | 0.6387 | 0.9619 | 0.8711 | 0.9454 |
| 0.0969 | 0.97 | 140 | 0.1681 | 0.7926 | 0.7702 | 0.7812 | 0.6410 | 0.9618 | 0.8753 | 0.9461 |
| 0.1009 | 1.04 | 150 | 0.1624 | 0.8247 | 0.7475 | 0.7842 | 0.6450 | 0.9636 | 0.8660 | 0.9485 |
| 0.086 | 1.11 | 160 | 0.1532 | 0.8062 | 0.7793 | 0.7925 | 0.6564 | 0.9639 | 0.8806 | 0.9496 |
| 0.0732 | 1.18 | 170 | 0.1710 | 0.7633 | 0.8185 | 0.7899 | 0.6528 | 0.9615 | 0.8969 | 0.9465 |
| 0.0804 | 1.25 | 180 | 0.1731 | 0.8098 | 0.7735 | 0.7912 | 0.6546 | 0.9639 | 0.8779 | 0.9472 |
| 0.0865 | 1.32 | 190 | 0.1659 | 0.7905 | 0.7863 | 0.7884 | 0.6507 | 0.9627 | 0.8830 | 0.9486 |
| 0.0867 | 1.39 | 200 | 0.1716 | 0.7704 | 0.8082 | 0.7888 | 0.6513 | 0.9617 | 0.8924 | 0.9452 |
| 0.0769 | 1.46 | 210 | 0.1644 | 0.8122 | 0.7678 | 0.7894 | 0.6520 | 0.9637 | 0.8753 | 0.9486 |
| 0.0639 | 1.53 | 220 | 0.1668 | 0.7927 | 0.7786 | 0.7856 | 0.6469 | 0.9624 | 0.8794 | 0.9474 |
| 0.0642 | 1.6 | 230 | 0.1669 | 0.7960 | 0.7830 | 0.7895 | 0.6521 | 0.9630 | 0.8818 | 0.9482 |
| 0.0598 | 1.67 | 240 | 0.1776 | 0.7959 | 0.7802 | 0.7880 | 0.6501 | 0.9628 | 0.8804 | 0.9464 |
| 0.0679 | 1.74 | 250 | 0.1938 | 0.7436 | 0.8338 | 0.7861 | 0.6476 | 0.9599 | 0.9030 | 0.9423 |
| 0.0578 | 1.81 | 260 | 0.1669 | 0.7965 | 0.7855 | 0.7910 | 0.6542 | 0.9633 | 0.8830 | 0.9494 |
| 0.0587 | 1.88 | 270 | 0.1750 | 0.7982 | 0.7833 | 0.7907 | 0.6538 | 0.9633 | 0.8820 | 0.9468 |
| 0.0624 | 1.94 | 280 | 0.1759 | 0.7684 | 0.8249 | 0.7956 | 0.6606 | 0.9625 | 0.9004 | 0.9482 |
| 0.0494 | 2.01 | 290 | 0.1868 | 0.7606 | 0.8194 | 0.7889 | 0.6514 | 0.9612 | 0.8972 | 0.9447 |
| 0.0418 | 2.08 | 300 | 0.1746 | 0.8089 | 0.7769 | 0.7926 | 0.6564 | 0.9640 | 0.8795 | 0.9501 |
| 0.0406 | 2.15 | 310 | 0.1900 | 0.7778 | 0.8157 | 0.7963 | 0.6616 | 0.9631 | 0.8966 | 0.9477 |
| 0.0417 | 2.22 | 320 | 0.2066 | 0.7754 | 0.8165 | 0.7954 | 0.6603 | 0.9628 | 0.8968 | 0.9467 |
| 0.0438 | 2.29 | 330 | 0.2037 | 0.7623 | 0.8282 | 0.7939 | 0.6582 | 0.9619 | 0.9015 | 0.9461 |
| 0.0454 | 2.36 | 340 | 0.1882 | 0.7671 | 0.8221 | 0.7936 | 0.6579 | 0.9622 | 0.8989 | 0.9487 |
| 0.0372 | 2.43 | 350 | 0.1896 | 0.7937 | 0.8082 | 0.8009 | 0.6679 | 0.9644 | 0.8939 | 0.9489 |
| 0.0301 | 2.5 | 360 | 0.1846 | 0.8058 | 0.7913 | 0.7985 | 0.6646 | 0.9647 | 0.8864 | 0.9503 |
| 0.0334 | 2.57 | 370 | 0.1867 | 0.7723 | 0.8212 | 0.7960 | 0.6611 | 0.9628 | 0.8988 | 0.9498 |
| 0.038 | 2.64 | 380 | 0.2099 | 0.7538 | 0.8276 | 0.7890 | 0.6516 | 0.9608 | 0.9007 | 0.9463 |
| 0.0427 | 2.71 | 390 | 0.2050 | 0.7625 | 0.8272 | 0.7935 | 0.6577 | 0.9619 | 0.9011 | 0.9460 |
| 0.0373 | 2.78 | 400 | 0.2002 | 0.7779 | 0.8086 | 0.7930 | 0.6569 | 0.9626 | 0.8931 | 0.9487 |
| 0.0308 | 2.85 | 410 | 0.2002 | 0.7501 | 0.8260 | 0.7863 | 0.6478 | 0.9603 | 0.8997 | 0.9449 |
| 0.035 | 2.92 | 420 | 0.2034 | 0.7656 | 0.8173 | 0.7906 | 0.6537 | 0.9617 | 0.8965 | 0.9470 |
| 0.0265 | 2.99 | 430 | 0.1877 | 0.7766 | 0.8202 | 0.7978 | 0.6637 | 0.9632 | 0.8987 | 0.9487 |
| 0.0358 | 3.06 | 440 | 0.2140 | 0.7555 | 0.8300 | 0.7910 | 0.6542 | 0.9612 | 0.9020 | 0.9470 |
| 0.0286 | 3.12 | 450 | 0.2084 | 0.7775 | 0.8052 | 0.7911 | 0.6544 | 0.9624 | 0.8914 | 0.9487 |
| 0.0335 | 3.19 | 460 | 0.2084 | 0.7706 | 0.8144 | 0.7919 | 0.6555 | 0.9621 | 0.8954 | 0.9468 |
| 0.0304 | 3.26 | 470 | 0.2231 | 0.7506 | 0.8280 | 0.7874 | 0.6494 | 0.9604 | 0.9007 | 0.9458 |
| 0.0255 | 3.33 | 480 | 0.2194 | 0.7501 | 0.8348 | 0.7902 | 0.6532 | 0.9608 | 0.9039 | 0.9459 |
| 0.0209 | 3.4 | 490 | 0.2186 | 0.7602 | 0.8300 | 0.7936 | 0.6578 | 0.9618 | 0.9023 | 0.9469 |
| 0.0237 | 3.47 | 500 | 0.2138 | 0.7731 | 0.8196 | 0.7957 | 0.6607 | 0.9628 | 0.8981 | 0.9488 |
| 0.0263 | 3.54 | 510 | 0.2268 | 0.7565 | 0.8313 | 0.7921 | 0.6558 | 0.9614 | 0.9027 | 0.9472 |
| 0.0196 | 3.61 | 520 | 0.2157 | 0.7626 | 0.8242 | 0.7922 | 0.6559 | 0.9617 | 0.8996 | 0.9473 |
| 0.0225 | 3.68 | 530 | 0.2265 | 0.7664 | 0.8185 | 0.7916 | 0.6551 | 0.9619 | 0.8971 | 0.9469 |
| 0.0226 | 3.75 | 540 | 0.2316 | 0.7628 | 0.8139 | 0.7875 | 0.6495 | 0.9611 | 0.8947 | 0.9461 |
| 0.0226 | 3.82 | 550 | 0.2100 | 0.7812 | 0.8130 | 0.7968 | 0.6622 | 0.9633 | 0.8954 | 0.9493 |
| 0.024 | 3.89 | 560 | 0.2213 | 0.7670 | 0.8196 | 0.7924 | 0.6562 | 0.9620 | 0.8977 | 0.9487 |
| 0.0194 | 3.96 | 570 | 0.2227 | 0.7673 | 0.8164 | 0.7911 | 0.6544 | 0.9618 | 0.8962 | 0.9483 |
| 0.0153 | 4.03 | 580 | 0.2296 | 0.7621 | 0.8245 | 0.7921 | 0.6557 | 0.9617 | 0.8997 | 0.9462 |
| 0.0168 | 4.1 | 590 | 0.2362 | 0.7522 | 0.8237 | 0.7863 | 0.6479 | 0.9604 | 0.8987 | 0.9463 |
| 0.0215 | 4.17 | 600 | 0.2413 | 0.7629 | 0.8183 | 0.7896 | 0.6524 | 0.9614 | 0.8968 | 0.9470 |
| 0.0173 | 4.24 | 610 | 0.2411 | 0.7550 | 0.8196 | 0.7859 | 0.6474 | 0.9605 | 0.8969 | 0.9461 |
| 0.0196 | 4.31 | 620 | 0.2315 | 0.7625 | 0.8239 | 0.7920 | 0.6557 | 0.9617 | 0.8995 | 0.9471 |
| 0.0186 | 4.38 | 630 | 0.2593 | 0.7370 | 0.8377 | 0.7841 | 0.6449 | 0.9592 | 0.9043 | 0.9432 |
| 0.0183 | 4.44 | 640 | 0.2368 | 0.7660 | 0.8255 | 0.7947 | 0.6593 | 0.9623 | 0.9005 | 0.9477 |
| 0.0178 | 4.51 | 650 | 0.2326 | 0.7705 | 0.8237 | 0.7962 | 0.6614 | 0.9627 | 0.8999 | 0.9486 |
| 0.017 | 4.58 | 660 | 0.2457 | 0.7448 | 0.8352 | 0.7874 | 0.6493 | 0.9601 | 0.9037 | 0.9454 |
| 0.0175 | 4.65 | 670 | 0.2311 | 0.7694 | 0.8209 | 0.7943 | 0.6588 | 0.9624 | 0.8985 | 0.9485 |
| 0.0163 | 4.72 | 680 | 0.2426 | 0.7696 | 0.8208 | 0.7944 | 0.6589 | 0.9624 | 0.8985 | 0.9484 |
| 0.0163 | 4.79 | 690 | 0.2400 | 0.7527 | 0.8305 | 0.7897 | 0.6525 | 0.9609 | 0.9020 | 0.9462 |
| 0.0122 | 4.86 | 700 | 0.2328 | 0.7656 | 0.8208 | 0.7922 | 0.6559 | 0.9619 | 0.8982 | 0.9476 |
| 0.0205 | 4.93 | 710 | 0.2483 | 0.7474 | 0.8305 | 0.7868 | 0.6485 | 0.9602 | 0.9016 | 0.9452 |
| 0.0154 | 5.0 | 720 | 0.2350 | 0.7612 | 0.8206 | 0.7898 | 0.6526 | 0.9614 | 0.8978 | 0.9472 |
| 0.0155 | 5.07 | 730 | 0.2581 | 0.7570 | 0.8263 | 0.7901 | 0.6531 | 0.9612 | 0.9003 | 0.9463 |
| 0.0137 | 5.14 | 740 | 0.2482 | 0.7592 | 0.8242 | 0.7904 | 0.6534 | 0.9613 | 0.8994 | 0.9472 |
| 0.0124 | 5.21 | 750 | 0.2507 | 0.7639 | 0.8185 | 0.7903 | 0.6533 | 0.9616 | 0.8970 | 0.9466 |
| 0.0121 | 5.28 | 760 | 0.2509 | 0.7586 | 0.8229 | 0.7894 | 0.6521 | 0.9612 | 0.8987 | 0.9470 |
| 0.016 | 5.35 | 770 | 0.2552 | 0.7625 | 0.8173 | 0.7890 | 0.6515 | 0.9613 | 0.8963 | 0.9466 |
| 0.0131 | 5.42 | 780 | 0.2500 | 0.7573 | 0.8231 | 0.7889 | 0.6513 | 0.9610 | 0.8988 | 0.9467 |
| 0.0111 | 5.49 | 790 | 0.2647 | 0.7496 | 0.8312 | 0.7883 | 0.6506 | 0.9605 | 0.9021 | 0.9453 |
| 0.0113 | 5.56 | 800 | 0.2529 | 0.7646 | 0.8226 | 0.7926 | 0.6564 | 0.9619 | 0.8990 | 0.9475 |
| 0.0141 | 5.62 | 810 | 0.2521 | 0.7623 | 0.8230 | 0.7915 | 0.6549 | 0.9616 | 0.8990 | 0.9474 |
| 0.0123 | 5.69 | 820 | 0.2558 | 0.7523 | 0.8282 | 0.7884 | 0.6508 | 0.9607 | 0.9008 | 0.9458 |
| 0.0113 | 5.76 | 830 | 0.2560 | 0.7585 | 0.8208 | 0.7884 | 0.6507 | 0.9610 | 0.8977 | 0.9465 |
| 0.0176 | 5.83 | 840 | 0.2560 | 0.7553 | 0.8263 | 0.7892 | 0.6518 | 0.9609 | 0.9002 | 0.9466 |
| 0.0137 | 5.9 | 850 | 0.2539 | 0.7628 | 0.8233 | 0.7919 | 0.6555 | 0.9617 | 0.8992 | 0.9471 |
| 0.0159 | 5.97 | 860 | 0.2574 | 0.7574 | 0.8246 | 0.7896 | 0.6523 | 0.9611 | 0.8995 | 0.9464 |
| 0.0107 | 6.04 | 870 | 0.2573 | 0.7561 | 0.8257 | 0.7894 | 0.6520 | 0.9610 | 0.8999 | 0.9464 |
| 0.01 | 6.11 | 880 | 0.2562 | 0.7633 | 0.8204 | 0.7908 | 0.6540 | 0.9616 | 0.8978 | 0.9471 |
| 0.0112 | 6.18 | 890 | 0.2574 | 0.7618 | 0.8218 | 0.7907 | 0.6538 | 0.9615 | 0.8984 | 0.9469 |
| 0.0111 | 6.25 | 900 | 0.2563 | 0.7598 | 0.8239 | 0.7906 | 0.6537 | 0.9614 | 0.8993 | 0.9469 |
| 0.0135 | 6.32 | 910 | 0.2558 | 0.7578 | 0.8263 | 0.7905 | 0.6536 | 0.9613 | 0.9003 | 0.9468 |
| 0.0171 | 6.39 | 920 | 0.2554 | 0.7601 | 0.8251 | 0.7913 | 0.6546 | 0.9615 | 0.8999 | 0.9470 |
| 0.0115 | 6.46 | 930 | 0.2563 | 0.7602 | 0.8254 | 0.7915 | 0.6549 | 0.9615 | 0.9001 | 0.9469 |
| 0.0127 | 6.53 | 940 | 0.2561 | 0.7591 | 0.8258 | 0.7910 | 0.6543 | 0.9614 | 0.9002 | 0.9468 |
| 0.0107 | 6.6 | 950 | 0.2563 | 0.7584 | 0.8258 | 0.7906 | 0.6538 | 0.9613 | 0.9001 | 0.9467 |
| 0.0129 | 6.67 | 960 | 0.2567 | 0.7579 | 0.8259 | 0.7905 | 0.6535 | 0.9613 | 0.9002 | 0.9467 |
| 0.012 | 6.74 | 970 | 0.2567 | 0.7580 | 0.8257 | 0.7904 | 0.6534 | 0.9612 | 0.9000 | 0.9467 |
| 0.0141 | 6.81 | 980 | 0.2563 | 0.7587 | 0.8254 | 0.7907 | 0.6538 | 0.9613 | 0.9000 | 0.9468 |
| 0.0116 | 6.88 | 990 | 0.2562 | 0.7588 | 0.8254 | 0.7907 | 0.6539 | 0.9613 | 0.9000 | 0.9468 |
| 0.0097 | 6.94 | 1000 | 0.2562 | 0.7588 | 0.8254 | 0.7907 | 0.6539 | 0.9613 | 0.9000 | 0.9468 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|