End of training

739402b over 2 years ago

16.1 kB

	---
	license: mit
	base_model: prajjwal1/bert-small
	tags:
	- generated_from_trainer
	datasets:
	- sembr2023
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: sembr2023-bert-small
	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: 0.7477250957854407
	- name: Recall
	type: recall
	value: 0.8248580108308018
	- name: F1
	type: f1
	value: 0.7843999246373171
	- name: Accuracy
	type: accuracy
	value: 0.9598803304935198
	---

	<!-- 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 the sembr2023 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2330
	- Precision: 0.7477
	- Recall: 0.8249
	- F1: 0.7844
	- Iou: 0.6453
	- Accuracy: 0.9599
	- Balanced Accuracy: 0.8989
	- Overall Accuracy: 0.9446

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:------:\|:--------:\|:-----------------:\|:----------------:\|
	\| 0.3749 \| 0.07 \| 10 \| 0.3610 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0.9115 \| 0.5 \| 0.9115 \|
	\| 0.255 \| 0.14 \| 20 \| 0.2741 \| 0.7816 \| 0.4170 \| 0.5438 \| 0.3735 \| 0.9381 \| 0.7028 \| 0.9275 \|
	\| 0.2228 \| 0.21 \| 30 \| 0.2281 \| 0.7475 \| 0.6793 \| 0.7118 \| 0.5525 \| 0.9513 \| 0.8285 \| 0.9341 \|
	\| 0.1788 \| 0.28 \| 40 \| 0.2079 \| 0.8020 \| 0.6789 \| 0.7353 \| 0.5814 \| 0.9568 \| 0.8313 \| 0.9398 \|
	\| 0.1754 \| 0.35 \| 50 \| 0.2001 \| 0.7468 \| 0.7424 \| 0.7446 \| 0.5931 \| 0.9549 \| 0.8590 \| 0.9373 \|
	\| 0.1609 \| 0.42 \| 60 \| 0.2016 \| 0.7290 \| 0.7657 \| 0.7469 \| 0.5960 \| 0.9541 \| 0.8690 \| 0.9351 \|
	\| 0.1576 \| 0.49 \| 70 \| 0.1945 \| 0.7318 \| 0.7823 \| 0.7562 \| 0.6080 \| 0.9554 \| 0.8772 \| 0.9370 \|
	\| 0.1377 \| 0.56 \| 80 \| 0.1861 \| 0.7254 \| 0.8006 \| 0.7611 \| 0.6144 \| 0.9555 \| 0.8856 \| 0.9389 \|
	\| 0.128 \| 0.62 \| 90 \| 0.1873 \| 0.7170 \| 0.8183 \| 0.7643 \| 0.6185 \| 0.9553 \| 0.8935 \| 0.9377 \|
	\| 0.1182 \| 0.69 \| 100 \| 0.1868 \| 0.7272 \| 0.8138 \| 0.7681 \| 0.6235 \| 0.9565 \| 0.8921 \| 0.9380 \|
	\| 0.1137 \| 0.76 \| 110 \| 0.1759 \| 0.7526 \| 0.7975 \| 0.7744 \| 0.6319 \| 0.9589 \| 0.8860 \| 0.9415 \|
	\| 0.1232 \| 0.83 \| 120 \| 0.1817 \| 0.7489 \| 0.8054 \| 0.7761 \| 0.6342 \| 0.9589 \| 0.8896 \| 0.9411 \|
	\| 0.0948 \| 0.9 \| 130 \| 0.1809 \| 0.7269 \| 0.8161 \| 0.7690 \| 0.6246 \| 0.9566 \| 0.8932 \| 0.9392 \|
	\| 0.0877 \| 0.97 \| 140 \| 0.1870 \| 0.7260 \| 0.8142 \| 0.7676 \| 0.6228 \| 0.9564 \| 0.8922 \| 0.9388 \|
	\| 0.0976 \| 1.04 \| 150 \| 0.1868 \| 0.7098 \| 0.8365 \| 0.7680 \| 0.6233 \| 0.9553 \| 0.9016 \| 0.9370 \|
	\| 0.0855 \| 1.11 \| 160 \| 0.1659 \| 0.7728 \| 0.7872 \| 0.7800 \| 0.6393 \| 0.9607 \| 0.8824 \| 0.9442 \|
	\| 0.0763 \| 1.18 \| 170 \| 0.1939 \| 0.7187 \| 0.8305 \| 0.7706 \| 0.6268 \| 0.9562 \| 0.8995 \| 0.9380 \|
	\| 0.0821 \| 1.25 \| 180 \| 0.1830 \| 0.7560 \| 0.7996 \| 0.7772 \| 0.6356 \| 0.9594 \| 0.8873 \| 0.9410 \|
	\| 0.0869 \| 1.32 \| 190 \| 0.1814 \| 0.7314 \| 0.8220 \| 0.7741 \| 0.6314 \| 0.9575 \| 0.8963 \| 0.9413 \|
	\| 0.0896 \| 1.39 \| 200 \| 0.1778 \| 0.7480 \| 0.7957 \| 0.7711 \| 0.6275 \| 0.9582 \| 0.8848 \| 0.9415 \|
	\| 0.0806 \| 1.46 \| 210 \| 0.1749 \| 0.7320 \| 0.8270 \| 0.7766 \| 0.6348 \| 0.9579 \| 0.8988 \| 0.9412 \|
	\| 0.0678 \| 1.53 \| 220 \| 0.1884 \| 0.7537 \| 0.8165 \| 0.7839 \| 0.6446 \| 0.9602 \| 0.8953 \| 0.9417 \|
	\| 0.0712 \| 1.6 \| 230 \| 0.1663 \| 0.7731 \| 0.7930 \| 0.7829 \| 0.6433 \| 0.9611 \| 0.8852 \| 0.9443 \|
	\| 0.0672 \| 1.67 \| 240 \| 0.1694 \| 0.7670 \| 0.8098 \| 0.7878 \| 0.6499 \| 0.9614 \| 0.8930 \| 0.9447 \|
	\| 0.0758 \| 1.74 \| 250 \| 0.1855 \| 0.7412 \| 0.8155 \| 0.7766 \| 0.6347 \| 0.9585 \| 0.8939 \| 0.9406 \|
	\| 0.0642 \| 1.81 \| 260 \| 0.1693 \| 0.7590 \| 0.8213 \| 0.7889 \| 0.6514 \| 0.9611 \| 0.8980 \| 0.9437 \|
	\| 0.0704 \| 1.88 \| 270 \| 0.1721 \| 0.7691 \| 0.8028 \| 0.7856 \| 0.6469 \| 0.9612 \| 0.8897 \| 0.9439 \|
	\| 0.0747 \| 1.94 \| 280 \| 0.1815 \| 0.7363 \| 0.8297 \| 0.7802 \| 0.6396 \| 0.9586 \| 0.9004 \| 0.9422 \|
	\| 0.0594 \| 2.01 \| 290 \| 0.1812 \| 0.7499 \| 0.8206 \| 0.7837 \| 0.6443 \| 0.9599 \| 0.8970 \| 0.9433 \|
	\| 0.0545 \| 2.08 \| 300 \| 0.1924 \| 0.7392 \| 0.8229 \| 0.7788 \| 0.6377 \| 0.9586 \| 0.8973 \| 0.9412 \|
	\| 0.0545 \| 2.15 \| 310 \| 0.1965 \| 0.7350 \| 0.8221 \| 0.7761 \| 0.6341 \| 0.9580 \| 0.8967 \| 0.9408 \|
	\| 0.0519 \| 2.22 \| 320 \| 0.1953 \| 0.7420 \| 0.8254 \| 0.7815 \| 0.6413 \| 0.9592 \| 0.8988 \| 0.9428 \|
	\| 0.0495 \| 2.29 \| 330 \| 0.2124 \| 0.7189 \| 0.8387 \| 0.7742 \| 0.6316 \| 0.9567 \| 0.9034 \| 0.9398 \|
	\| 0.0551 \| 2.36 \| 340 \| 0.1996 \| 0.7388 \| 0.8290 \| 0.7813 \| 0.6411 \| 0.9589 \| 0.9003 \| 0.9431 \|
	\| 0.0491 \| 2.43 \| 350 \| 0.2030 \| 0.7414 \| 0.8275 \| 0.7821 \| 0.6422 \| 0.9592 \| 0.8997 \| 0.9423 \|
	\| 0.0393 \| 2.5 \| 360 \| 0.1865 \| 0.7538 \| 0.8147 \| 0.7830 \| 0.6434 \| 0.9601 \| 0.8944 \| 0.9437 \|
	\| 0.0419 \| 2.57 \| 370 \| 0.1983 \| 0.7433 \| 0.8259 \| 0.7824 \| 0.6426 \| 0.9594 \| 0.8991 \| 0.9433 \|
	\| 0.0487 \| 2.64 \| 380 \| 0.1923 \| 0.7321 \| 0.8319 \| 0.7788 \| 0.6377 \| 0.9582 \| 0.9012 \| 0.9427 \|
	\| 0.0542 \| 2.71 \| 390 \| 0.2059 \| 0.7419 \| 0.8243 \| 0.7810 \| 0.6406 \| 0.9591 \| 0.8982 \| 0.9427 \|
	\| 0.0423 \| 2.78 \| 400 \| 0.1897 \| 0.7546 \| 0.8160 \| 0.7841 \| 0.6449 \| 0.9602 \| 0.8951 \| 0.9446 \|
	\| 0.0391 \| 2.85 \| 410 \| 0.2074 \| 0.7248 \| 0.8334 \| 0.7753 \| 0.6331 \| 0.9573 \| 0.9014 \| 0.9411 \|
	\| 0.0473 \| 2.92 \| 420 \| 0.1993 \| 0.7317 \| 0.8377 \| 0.7811 \| 0.6408 \| 0.9585 \| 0.9039 \| 0.9425 \|
	\| 0.0368 \| 2.99 \| 430 \| 0.2029 \| 0.7416 \| 0.8307 \| 0.7836 \| 0.6442 \| 0.9594 \| 0.9013 \| 0.9434 \|
	\| 0.0516 \| 3.06 \| 440 \| 0.2091 \| 0.7466 \| 0.8216 \| 0.7823 \| 0.6424 \| 0.9595 \| 0.8972 \| 0.9441 \|
	\| 0.0414 \| 3.12 \| 450 \| 0.2095 \| 0.7376 \| 0.8309 \| 0.7815 \| 0.6413 \| 0.9589 \| 0.9011 \| 0.9431 \|
	\| 0.0463 \| 3.19 \| 460 \| 0.2051 \| 0.7419 \| 0.8282 \| 0.7827 \| 0.6429 \| 0.9593 \| 0.9001 \| 0.9439 \|
	\| 0.0423 \| 3.26 \| 470 \| 0.2066 \| 0.7669 \| 0.8102 \| 0.7880 \| 0.6501 \| 0.9614 \| 0.8931 \| 0.9461 \|
	\| 0.0384 \| 3.33 \| 480 \| 0.2120 \| 0.7427 \| 0.8217 \| 0.7802 \| 0.6396 \| 0.9590 \| 0.8970 \| 0.9436 \|
	\| 0.0317 \| 3.4 \| 490 \| 0.2084 \| 0.7506 \| 0.8192 \| 0.7834 \| 0.6439 \| 0.9599 \| 0.8964 \| 0.9449 \|
	\| 0.0309 \| 3.47 \| 500 \| 0.2182 \| 0.7489 \| 0.8208 \| 0.7832 \| 0.6437 \| 0.9598 \| 0.8970 \| 0.9441 \|
	\| 0.0341 \| 3.54 \| 510 \| 0.2110 \| 0.7462 \| 0.8239 \| 0.7831 \| 0.6436 \| 0.9596 \| 0.8984 \| 0.9444 \|
	\| 0.0322 \| 3.61 \| 520 \| 0.2152 \| 0.7409 \| 0.8267 \| 0.7814 \| 0.6413 \| 0.9591 \| 0.8993 \| 0.9444 \|
	\| 0.0346 \| 3.68 \| 530 \| 0.2195 \| 0.7342 \| 0.8341 \| 0.7810 \| 0.6407 \| 0.9586 \| 0.9024 \| 0.9436 \|
	\| 0.0364 \| 3.75 \| 540 \| 0.2174 \| 0.7434 \| 0.8268 \| 0.7829 \| 0.6432 \| 0.9594 \| 0.8996 \| 0.9438 \|
	\| 0.0338 \| 3.82 \| 550 \| 0.2081 \| 0.7648 \| 0.8105 \| 0.7870 \| 0.6488 \| 0.9612 \| 0.8931 \| 0.9462 \|
	\| 0.0364 \| 3.89 \| 560 \| 0.2184 \| 0.7493 \| 0.8260 \| 0.7858 \| 0.6471 \| 0.9601 \| 0.8996 \| 0.9446 \|
	\| 0.0324 \| 3.96 \| 570 \| 0.2094 \| 0.7498 \| 0.8192 \| 0.7830 \| 0.6434 \| 0.9598 \| 0.8963 \| 0.9445 \|
	\| 0.027 \| 4.03 \| 580 \| 0.2070 \| 0.7506 \| 0.8225 \| 0.7849 \| 0.6460 \| 0.9601 \| 0.8980 \| 0.9452 \|
	\| 0.0319 \| 4.1 \| 590 \| 0.2253 \| 0.7484 \| 0.8189 \| 0.7821 \| 0.6422 \| 0.9596 \| 0.8961 \| 0.9446 \|
	\| 0.0319 \| 4.17 \| 600 \| 0.2308 \| 0.7506 \| 0.8167 \| 0.7823 \| 0.6424 \| 0.9598 \| 0.8952 \| 0.9445 \|
	\| 0.026 \| 4.24 \| 610 \| 0.2249 \| 0.7515 \| 0.8247 \| 0.7864 \| 0.6480 \| 0.9604 \| 0.8991 \| 0.9447 \|
	\| 0.0314 \| 4.31 \| 620 \| 0.2181 \| 0.7570 \| 0.8196 \| 0.7870 \| 0.6489 \| 0.9608 \| 0.8970 \| 0.9457 \|
	\| 0.0332 \| 4.38 \| 630 \| 0.2272 \| 0.7418 \| 0.8287 \| 0.7828 \| 0.6432 \| 0.9593 \| 0.9003 \| 0.9442 \|
	\| 0.0327 \| 4.44 \| 640 \| 0.2236 \| 0.7519 \| 0.8237 \| 0.7861 \| 0.6476 \| 0.9603 \| 0.8986 \| 0.9445 \|
	\| 0.029 \| 4.51 \| 650 \| 0.2210 \| 0.7517 \| 0.8242 \| 0.7863 \| 0.6478 \| 0.9604 \| 0.8989 \| 0.9449 \|
	\| 0.0268 \| 4.58 \| 660 \| 0.2347 \| 0.7368 \| 0.8356 \| 0.7831 \| 0.6435 \| 0.9590 \| 0.9033 \| 0.9432 \|
	\| 0.0312 \| 4.65 \| 670 \| 0.2247 \| 0.7460 \| 0.8271 \| 0.7845 \| 0.6454 \| 0.9598 \| 0.8999 \| 0.9442 \|
	\| 0.0262 \| 4.72 \| 680 \| 0.2246 \| 0.7581 \| 0.8176 \| 0.7867 \| 0.6484 \| 0.9608 \| 0.8961 \| 0.9452 \|
	\| 0.0277 \| 4.79 \| 690 \| 0.2300 \| 0.7528 \| 0.8222 \| 0.7860 \| 0.6474 \| 0.9604 \| 0.8980 \| 0.9447 \|
	\| 0.0298 \| 4.86 \| 700 \| 0.2239 \| 0.7508 \| 0.8250 \| 0.7862 \| 0.6477 \| 0.9603 \| 0.8992 \| 0.9452 \|
	\| 0.0345 \| 4.93 \| 710 \| 0.2257 \| 0.7499 \| 0.8237 \| 0.7850 \| 0.6462 \| 0.9601 \| 0.8985 \| 0.9447 \|
	\| 0.0255 \| 5.0 \| 720 \| 0.2223 \| 0.7534 \| 0.8213 \| 0.7859 \| 0.6473 \| 0.9604 \| 0.8976 \| 0.9453 \|
	\| 0.0296 \| 5.07 \| 730 \| 0.2253 \| 0.7496 \| 0.8239 \| 0.7850 \| 0.6461 \| 0.9601 \| 0.8986 \| 0.9449 \|
	\| 0.0254 \| 5.14 \| 740 \| 0.2290 \| 0.7452 \| 0.8270 \| 0.7839 \| 0.6447 \| 0.9597 \| 0.8998 \| 0.9445 \|
	\| 0.0264 \| 5.21 \| 750 \| 0.2193 \| 0.7627 \| 0.8128 \| 0.7870 \| 0.6487 \| 0.9611 \| 0.8941 \| 0.9460 \|
	\| 0.0227 \| 5.28 \| 760 \| 0.2332 \| 0.7436 \| 0.8246 \| 0.7820 \| 0.6420 \| 0.9593 \| 0.8985 \| 0.9436 \|
	\| 0.0299 \| 5.35 \| 770 \| 0.2290 \| 0.7536 \| 0.8184 \| 0.7847 \| 0.6456 \| 0.9603 \| 0.8962 \| 0.9448 \|
	\| 0.0215 \| 5.42 \| 780 \| 0.2302 \| 0.7460 \| 0.8274 \| 0.7846 \| 0.6455 \| 0.9598 \| 0.9000 \| 0.9445 \|
	\| 0.0221 \| 5.49 \| 790 \| 0.2316 \| 0.7496 \| 0.8259 \| 0.7859 \| 0.6473 \| 0.9602 \| 0.8996 \| 0.9444 \|
	\| 0.0203 \| 5.56 \| 800 \| 0.2322 \| 0.7497 \| 0.8223 \| 0.7843 \| 0.6452 \| 0.9600 \| 0.8978 \| 0.9444 \|
	\| 0.0273 \| 5.62 \| 810 \| 0.2293 \| 0.7513 \| 0.8209 \| 0.7846 \| 0.6455 \| 0.9601 \| 0.8973 \| 0.9448 \|
	\| 0.022 \| 5.69 \| 820 \| 0.2327 \| 0.7442 \| 0.8271 \| 0.7835 \| 0.6440 \| 0.9596 \| 0.8998 \| 0.9440 \|
	\| 0.0233 \| 5.76 \| 830 \| 0.2333 \| 0.7484 \| 0.8263 \| 0.7854 \| 0.6467 \| 0.9601 \| 0.8997 \| 0.9446 \|
	\| 0.0297 \| 5.83 \| 840 \| 0.2288 \| 0.7463 \| 0.8258 \| 0.7840 \| 0.6448 \| 0.9598 \| 0.8993 \| 0.9445 \|
	\| 0.0239 \| 5.9 \| 850 \| 0.2296 \| 0.7490 \| 0.8233 \| 0.7844 \| 0.6452 \| 0.9600 \| 0.8982 \| 0.9449 \|
	\| 0.0256 \| 5.97 \| 860 \| 0.2335 \| 0.7485 \| 0.8242 \| 0.7845 \| 0.6454 \| 0.9599 \| 0.8987 \| 0.9446 \|
	\| 0.0244 \| 6.04 \| 870 \| 0.2325 \| 0.7505 \| 0.8227 \| 0.7850 \| 0.6460 \| 0.9601 \| 0.8981 \| 0.9448 \|
	\| 0.0191 \| 6.11 \| 880 \| 0.2336 \| 0.7511 \| 0.8230 \| 0.7854 \| 0.6466 \| 0.9602 \| 0.8983 \| 0.9448 \|
	\| 0.0237 \| 6.18 \| 890 \| 0.2330 \| 0.7481 \| 0.8251 \| 0.7847 \| 0.6458 \| 0.9600 \| 0.8991 \| 0.9447 \|
	\| 0.0216 \| 6.25 \| 900 \| 0.2321 \| 0.7476 \| 0.8260 \| 0.7849 \| 0.6459 \| 0.9599 \| 0.8995 \| 0.9447 \|
	\| 0.0289 \| 6.32 \| 910 \| 0.2307 \| 0.7513 \| 0.8220 \| 0.7850 \| 0.6461 \| 0.9602 \| 0.8978 \| 0.9451 \|
	\| 0.0305 \| 6.39 \| 920 \| 0.2315 \| 0.7508 \| 0.8222 \| 0.7849 \| 0.6459 \| 0.9601 \| 0.8979 \| 0.9449 \|
	\| 0.0228 \| 6.46 \| 930 \| 0.2320 \| 0.7490 \| 0.8231 \| 0.7843 \| 0.6452 \| 0.9600 \| 0.8982 \| 0.9447 \|
	\| 0.0249 \| 6.53 \| 940 \| 0.2318 \| 0.7482 \| 0.8237 \| 0.7841 \| 0.6449 \| 0.9599 \| 0.8984 \| 0.9447 \|
	\| 0.022 \| 6.6 \| 950 \| 0.2327 \| 0.7476 \| 0.8251 \| 0.7845 \| 0.6454 \| 0.9599 \| 0.8990 \| 0.9446 \|
	\| 0.0278 \| 6.67 \| 960 \| 0.2333 \| 0.7472 \| 0.8253 \| 0.7843 \| 0.6451 \| 0.9598 \| 0.8991 \| 0.9446 \|
	\| 0.0228 \| 6.74 \| 970 \| 0.2332 \| 0.7473 \| 0.8253 \| 0.7843 \| 0.6452 \| 0.9598 \| 0.8991 \| 0.9446 \|
	\| 0.0289 \| 6.81 \| 980 \| 0.2331 \| 0.7476 \| 0.8249 \| 0.7844 \| 0.6452 \| 0.9599 \| 0.8989 \| 0.9446 \|
	\| 0.0228 \| 6.88 \| 990 \| 0.2330 \| 0.7477 \| 0.8249 \| 0.7844 \| 0.6453 \| 0.9599 \| 0.8989 \| 0.9446 \|
	\| 0.0179 \| 6.94 \| 1000 \| 0.2330 \| 0.7477 \| 0.8249 \| 0.7844 \| 0.6453 \| 0.9599 \| 0.8989 \| 0.9446 \|


	### Framework versions

	- Transformers 4.34.1
	- Pytorch 2.0.1
	- Datasets 2.14.6
	- Tokenizers 0.14.1

	---
	license: mit
	base_model: prajjwal1/bert-small
	tags:
	- generated_from_trainer
	datasets:
	- sembr2023
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: sembr2023-bert-small
	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: 0.7477250957854407
	- name: Recall
	type: recall
	value: 0.8248580108308018
	- name: F1
	type: f1
	value: 0.7843999246373171
	- name: Accuracy
	type: accuracy
	value: 0.9598803304935198
	---

	<!-- 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 the sembr2023 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2330
	- Precision: 0.7477
	- Recall: 0.8249
	- F1: 0.7844
	- Iou: 0.6453
	- Accuracy: 0.9599
	- Balanced Accuracy: 0.8989
	- Overall Accuracy: 0.9446

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:------:\|:--------:\|:-----------------:\|:----------------:\|
	\| 0.3749 \| 0.07 \| 10 \| 0.3610 \| 0 \| 0.0 \| 0.0 \| 0.0 \| 0.9115 \| 0.5 \| 0.9115 \|
	\| 0.255 \| 0.14 \| 20 \| 0.2741 \| 0.7816 \| 0.4170 \| 0.5438 \| 0.3735 \| 0.9381 \| 0.7028 \| 0.9275 \|
	\| 0.2228 \| 0.21 \| 30 \| 0.2281 \| 0.7475 \| 0.6793 \| 0.7118 \| 0.5525 \| 0.9513 \| 0.8285 \| 0.9341 \|
	\| 0.1788 \| 0.28 \| 40 \| 0.2079 \| 0.8020 \| 0.6789 \| 0.7353 \| 0.5814 \| 0.9568 \| 0.8313 \| 0.9398 \|
	\| 0.1754 \| 0.35 \| 50 \| 0.2001 \| 0.7468 \| 0.7424 \| 0.7446 \| 0.5931 \| 0.9549 \| 0.8590 \| 0.9373 \|
	\| 0.1609 \| 0.42 \| 60 \| 0.2016 \| 0.7290 \| 0.7657 \| 0.7469 \| 0.5960 \| 0.9541 \| 0.8690 \| 0.9351 \|
	\| 0.1576 \| 0.49 \| 70 \| 0.1945 \| 0.7318 \| 0.7823 \| 0.7562 \| 0.6080 \| 0.9554 \| 0.8772 \| 0.9370 \|
	\| 0.1377 \| 0.56 \| 80 \| 0.1861 \| 0.7254 \| 0.8006 \| 0.7611 \| 0.6144 \| 0.9555 \| 0.8856 \| 0.9389 \|
	\| 0.128 \| 0.62 \| 90 \| 0.1873 \| 0.7170 \| 0.8183 \| 0.7643 \| 0.6185 \| 0.9553 \| 0.8935 \| 0.9377 \|
	\| 0.1182 \| 0.69 \| 100 \| 0.1868 \| 0.7272 \| 0.8138 \| 0.7681 \| 0.6235 \| 0.9565 \| 0.8921 \| 0.9380 \|
	\| 0.1137 \| 0.76 \| 110 \| 0.1759 \| 0.7526 \| 0.7975 \| 0.7744 \| 0.6319 \| 0.9589 \| 0.8860 \| 0.9415 \|
	\| 0.1232 \| 0.83 \| 120 \| 0.1817 \| 0.7489 \| 0.8054 \| 0.7761 \| 0.6342 \| 0.9589 \| 0.8896 \| 0.9411 \|
	\| 0.0948 \| 0.9 \| 130 \| 0.1809 \| 0.7269 \| 0.8161 \| 0.7690 \| 0.6246 \| 0.9566 \| 0.8932 \| 0.9392 \|
	\| 0.0877 \| 0.97 \| 140 \| 0.1870 \| 0.7260 \| 0.8142 \| 0.7676 \| 0.6228 \| 0.9564 \| 0.8922 \| 0.9388 \|
	\| 0.0976 \| 1.04 \| 150 \| 0.1868 \| 0.7098 \| 0.8365 \| 0.7680 \| 0.6233 \| 0.9553 \| 0.9016 \| 0.9370 \|
	\| 0.0855 \| 1.11 \| 160 \| 0.1659 \| 0.7728 \| 0.7872 \| 0.7800 \| 0.6393 \| 0.9607 \| 0.8824 \| 0.9442 \|
	\| 0.0763 \| 1.18 \| 170 \| 0.1939 \| 0.7187 \| 0.8305 \| 0.7706 \| 0.6268 \| 0.9562 \| 0.8995 \| 0.9380 \|
	\| 0.0821 \| 1.25 \| 180 \| 0.1830 \| 0.7560 \| 0.7996 \| 0.7772 \| 0.6356 \| 0.9594 \| 0.8873 \| 0.9410 \|
	\| 0.0869 \| 1.32 \| 190 \| 0.1814 \| 0.7314 \| 0.8220 \| 0.7741 \| 0.6314 \| 0.9575 \| 0.8963 \| 0.9413 \|
	\| 0.0896 \| 1.39 \| 200 \| 0.1778 \| 0.7480 \| 0.7957 \| 0.7711 \| 0.6275 \| 0.9582 \| 0.8848 \| 0.9415 \|
	\| 0.0806 \| 1.46 \| 210 \| 0.1749 \| 0.7320 \| 0.8270 \| 0.7766 \| 0.6348 \| 0.9579 \| 0.8988 \| 0.9412 \|
	\| 0.0678 \| 1.53 \| 220 \| 0.1884 \| 0.7537 \| 0.8165 \| 0.7839 \| 0.6446 \| 0.9602 \| 0.8953 \| 0.9417 \|
	\| 0.0712 \| 1.6 \| 230 \| 0.1663 \| 0.7731 \| 0.7930 \| 0.7829 \| 0.6433 \| 0.9611 \| 0.8852 \| 0.9443 \|
	\| 0.0672 \| 1.67 \| 240 \| 0.1694 \| 0.7670 \| 0.8098 \| 0.7878 \| 0.6499 \| 0.9614 \| 0.8930 \| 0.9447 \|
	\| 0.0758 \| 1.74 \| 250 \| 0.1855 \| 0.7412 \| 0.8155 \| 0.7766 \| 0.6347 \| 0.9585 \| 0.8939 \| 0.9406 \|
	\| 0.0642 \| 1.81 \| 260 \| 0.1693 \| 0.7590 \| 0.8213 \| 0.7889 \| 0.6514 \| 0.9611 \| 0.8980 \| 0.9437 \|
	\| 0.0704 \| 1.88 \| 270 \| 0.1721 \| 0.7691 \| 0.8028 \| 0.7856 \| 0.6469 \| 0.9612 \| 0.8897 \| 0.9439 \|
	\| 0.0747 \| 1.94 \| 280 \| 0.1815 \| 0.7363 \| 0.8297 \| 0.7802 \| 0.6396 \| 0.9586 \| 0.9004 \| 0.9422 \|
	\| 0.0594 \| 2.01 \| 290 \| 0.1812 \| 0.7499 \| 0.8206 \| 0.7837 \| 0.6443 \| 0.9599 \| 0.8970 \| 0.9433 \|
	\| 0.0545 \| 2.08 \| 300 \| 0.1924 \| 0.7392 \| 0.8229 \| 0.7788 \| 0.6377 \| 0.9586 \| 0.8973 \| 0.9412 \|
	\| 0.0545 \| 2.15 \| 310 \| 0.1965 \| 0.7350 \| 0.8221 \| 0.7761 \| 0.6341 \| 0.9580 \| 0.8967 \| 0.9408 \|
	\| 0.0519 \| 2.22 \| 320 \| 0.1953 \| 0.7420 \| 0.8254 \| 0.7815 \| 0.6413 \| 0.9592 \| 0.8988 \| 0.9428 \|
	\| 0.0495 \| 2.29 \| 330 \| 0.2124 \| 0.7189 \| 0.8387 \| 0.7742 \| 0.6316 \| 0.9567 \| 0.9034 \| 0.9398 \|
	\| 0.0551 \| 2.36 \| 340 \| 0.1996 \| 0.7388 \| 0.8290 \| 0.7813 \| 0.6411 \| 0.9589 \| 0.9003 \| 0.9431 \|
	\| 0.0491 \| 2.43 \| 350 \| 0.2030 \| 0.7414 \| 0.8275 \| 0.7821 \| 0.6422 \| 0.9592 \| 0.8997 \| 0.9423 \|
	\| 0.0393 \| 2.5 \| 360 \| 0.1865 \| 0.7538 \| 0.8147 \| 0.7830 \| 0.6434 \| 0.9601 \| 0.8944 \| 0.9437 \|
	\| 0.0419 \| 2.57 \| 370 \| 0.1983 \| 0.7433 \| 0.8259 \| 0.7824 \| 0.6426 \| 0.9594 \| 0.8991 \| 0.9433 \|
	\| 0.0487 \| 2.64 \| 380 \| 0.1923 \| 0.7321 \| 0.8319 \| 0.7788 \| 0.6377 \| 0.9582 \| 0.9012 \| 0.9427 \|
	\| 0.0542 \| 2.71 \| 390 \| 0.2059 \| 0.7419 \| 0.8243 \| 0.7810 \| 0.6406 \| 0.9591 \| 0.8982 \| 0.9427 \|
	\| 0.0423 \| 2.78 \| 400 \| 0.1897 \| 0.7546 \| 0.8160 \| 0.7841 \| 0.6449 \| 0.9602 \| 0.8951 \| 0.9446 \|
	\| 0.0391 \| 2.85 \| 410 \| 0.2074 \| 0.7248 \| 0.8334 \| 0.7753 \| 0.6331 \| 0.9573 \| 0.9014 \| 0.9411 \|
	\| 0.0473 \| 2.92 \| 420 \| 0.1993 \| 0.7317 \| 0.8377 \| 0.7811 \| 0.6408 \| 0.9585 \| 0.9039 \| 0.9425 \|
	\| 0.0368 \| 2.99 \| 430 \| 0.2029 \| 0.7416 \| 0.8307 \| 0.7836 \| 0.6442 \| 0.9594 \| 0.9013 \| 0.9434 \|
	\| 0.0516 \| 3.06 \| 440 \| 0.2091 \| 0.7466 \| 0.8216 \| 0.7823 \| 0.6424 \| 0.9595 \| 0.8972 \| 0.9441 \|
	\| 0.0414 \| 3.12 \| 450 \| 0.2095 \| 0.7376 \| 0.8309 \| 0.7815 \| 0.6413 \| 0.9589 \| 0.9011 \| 0.9431 \|
	\| 0.0463 \| 3.19 \| 460 \| 0.2051 \| 0.7419 \| 0.8282 \| 0.7827 \| 0.6429 \| 0.9593 \| 0.9001 \| 0.9439 \|
	\| 0.0423 \| 3.26 \| 470 \| 0.2066 \| 0.7669 \| 0.8102 \| 0.7880 \| 0.6501 \| 0.9614 \| 0.8931 \| 0.9461 \|
	\| 0.0384 \| 3.33 \| 480 \| 0.2120 \| 0.7427 \| 0.8217 \| 0.7802 \| 0.6396 \| 0.9590 \| 0.8970 \| 0.9436 \|
	\| 0.0317 \| 3.4 \| 490 \| 0.2084 \| 0.7506 \| 0.8192 \| 0.7834 \| 0.6439 \| 0.9599 \| 0.8964 \| 0.9449 \|
	\| 0.0309 \| 3.47 \| 500 \| 0.2182 \| 0.7489 \| 0.8208 \| 0.7832 \| 0.6437 \| 0.9598 \| 0.8970 \| 0.9441 \|
	\| 0.0341 \| 3.54 \| 510 \| 0.2110 \| 0.7462 \| 0.8239 \| 0.7831 \| 0.6436 \| 0.9596 \| 0.8984 \| 0.9444 \|
	\| 0.0322 \| 3.61 \| 520 \| 0.2152 \| 0.7409 \| 0.8267 \| 0.7814 \| 0.6413 \| 0.9591 \| 0.8993 \| 0.9444 \|
	\| 0.0346 \| 3.68 \| 530 \| 0.2195 \| 0.7342 \| 0.8341 \| 0.7810 \| 0.6407 \| 0.9586 \| 0.9024 \| 0.9436 \|
	\| 0.0364 \| 3.75 \| 540 \| 0.2174 \| 0.7434 \| 0.8268 \| 0.7829 \| 0.6432 \| 0.9594 \| 0.8996 \| 0.9438 \|
	\| 0.0338 \| 3.82 \| 550 \| 0.2081 \| 0.7648 \| 0.8105 \| 0.7870 \| 0.6488 \| 0.9612 \| 0.8931 \| 0.9462 \|
	\| 0.0364 \| 3.89 \| 560 \| 0.2184 \| 0.7493 \| 0.8260 \| 0.7858 \| 0.6471 \| 0.9601 \| 0.8996 \| 0.9446 \|
	\| 0.0324 \| 3.96 \| 570 \| 0.2094 \| 0.7498 \| 0.8192 \| 0.7830 \| 0.6434 \| 0.9598 \| 0.8963 \| 0.9445 \|
	\| 0.027 \| 4.03 \| 580 \| 0.2070 \| 0.7506 \| 0.8225 \| 0.7849 \| 0.6460 \| 0.9601 \| 0.8980 \| 0.9452 \|
	\| 0.0319 \| 4.1 \| 590 \| 0.2253 \| 0.7484 \| 0.8189 \| 0.7821 \| 0.6422 \| 0.9596 \| 0.8961 \| 0.9446 \|
	\| 0.0319 \| 4.17 \| 600 \| 0.2308 \| 0.7506 \| 0.8167 \| 0.7823 \| 0.6424 \| 0.9598 \| 0.8952 \| 0.9445 \|
	\| 0.026 \| 4.24 \| 610 \| 0.2249 \| 0.7515 \| 0.8247 \| 0.7864 \| 0.6480 \| 0.9604 \| 0.8991 \| 0.9447 \|
	\| 0.0314 \| 4.31 \| 620 \| 0.2181 \| 0.7570 \| 0.8196 \| 0.7870 \| 0.6489 \| 0.9608 \| 0.8970 \| 0.9457 \|
	\| 0.0332 \| 4.38 \| 630 \| 0.2272 \| 0.7418 \| 0.8287 \| 0.7828 \| 0.6432 \| 0.9593 \| 0.9003 \| 0.9442 \|
	\| 0.0327 \| 4.44 \| 640 \| 0.2236 \| 0.7519 \| 0.8237 \| 0.7861 \| 0.6476 \| 0.9603 \| 0.8986 \| 0.9445 \|
	\| 0.029 \| 4.51 \| 650 \| 0.2210 \| 0.7517 \| 0.8242 \| 0.7863 \| 0.6478 \| 0.9604 \| 0.8989 \| 0.9449 \|
	\| 0.0268 \| 4.58 \| 660 \| 0.2347 \| 0.7368 \| 0.8356 \| 0.7831 \| 0.6435 \| 0.9590 \| 0.9033 \| 0.9432 \|
	\| 0.0312 \| 4.65 \| 670 \| 0.2247 \| 0.7460 \| 0.8271 \| 0.7845 \| 0.6454 \| 0.9598 \| 0.8999 \| 0.9442 \|
	\| 0.0262 \| 4.72 \| 680 \| 0.2246 \| 0.7581 \| 0.8176 \| 0.7867 \| 0.6484 \| 0.9608 \| 0.8961 \| 0.9452 \|
	\| 0.0277 \| 4.79 \| 690 \| 0.2300 \| 0.7528 \| 0.8222 \| 0.7860 \| 0.6474 \| 0.9604 \| 0.8980 \| 0.9447 \|
	\| 0.0298 \| 4.86 \| 700 \| 0.2239 \| 0.7508 \| 0.8250 \| 0.7862 \| 0.6477 \| 0.9603 \| 0.8992 \| 0.9452 \|
	\| 0.0345 \| 4.93 \| 710 \| 0.2257 \| 0.7499 \| 0.8237 \| 0.7850 \| 0.6462 \| 0.9601 \| 0.8985 \| 0.9447 \|
	\| 0.0255 \| 5.0 \| 720 \| 0.2223 \| 0.7534 \| 0.8213 \| 0.7859 \| 0.6473 \| 0.9604 \| 0.8976 \| 0.9453 \|
	\| 0.0296 \| 5.07 \| 730 \| 0.2253 \| 0.7496 \| 0.8239 \| 0.7850 \| 0.6461 \| 0.9601 \| 0.8986 \| 0.9449 \|
	\| 0.0254 \| 5.14 \| 740 \| 0.2290 \| 0.7452 \| 0.8270 \| 0.7839 \| 0.6447 \| 0.9597 \| 0.8998 \| 0.9445 \|
	\| 0.0264 \| 5.21 \| 750 \| 0.2193 \| 0.7627 \| 0.8128 \| 0.7870 \| 0.6487 \| 0.9611 \| 0.8941 \| 0.9460 \|
	\| 0.0227 \| 5.28 \| 760 \| 0.2332 \| 0.7436 \| 0.8246 \| 0.7820 \| 0.6420 \| 0.9593 \| 0.8985 \| 0.9436 \|
	\| 0.0299 \| 5.35 \| 770 \| 0.2290 \| 0.7536 \| 0.8184 \| 0.7847 \| 0.6456 \| 0.9603 \| 0.8962 \| 0.9448 \|
	\| 0.0215 \| 5.42 \| 780 \| 0.2302 \| 0.7460 \| 0.8274 \| 0.7846 \| 0.6455 \| 0.9598 \| 0.9000 \| 0.9445 \|
	\| 0.0221 \| 5.49 \| 790 \| 0.2316 \| 0.7496 \| 0.8259 \| 0.7859 \| 0.6473 \| 0.9602 \| 0.8996 \| 0.9444 \|
	\| 0.0203 \| 5.56 \| 800 \| 0.2322 \| 0.7497 \| 0.8223 \| 0.7843 \| 0.6452 \| 0.9600 \| 0.8978 \| 0.9444 \|
	\| 0.0273 \| 5.62 \| 810 \| 0.2293 \| 0.7513 \| 0.8209 \| 0.7846 \| 0.6455 \| 0.9601 \| 0.8973 \| 0.9448 \|
	\| 0.022 \| 5.69 \| 820 \| 0.2327 \| 0.7442 \| 0.8271 \| 0.7835 \| 0.6440 \| 0.9596 \| 0.8998 \| 0.9440 \|
	\| 0.0233 \| 5.76 \| 830 \| 0.2333 \| 0.7484 \| 0.8263 \| 0.7854 \| 0.6467 \| 0.9601 \| 0.8997 \| 0.9446 \|
	\| 0.0297 \| 5.83 \| 840 \| 0.2288 \| 0.7463 \| 0.8258 \| 0.7840 \| 0.6448 \| 0.9598 \| 0.8993 \| 0.9445 \|
	\| 0.0239 \| 5.9 \| 850 \| 0.2296 \| 0.7490 \| 0.8233 \| 0.7844 \| 0.6452 \| 0.9600 \| 0.8982 \| 0.9449 \|
	\| 0.0256 \| 5.97 \| 860 \| 0.2335 \| 0.7485 \| 0.8242 \| 0.7845 \| 0.6454 \| 0.9599 \| 0.8987 \| 0.9446 \|
	\| 0.0244 \| 6.04 \| 870 \| 0.2325 \| 0.7505 \| 0.8227 \| 0.7850 \| 0.6460 \| 0.9601 \| 0.8981 \| 0.9448 \|
	\| 0.0191 \| 6.11 \| 880 \| 0.2336 \| 0.7511 \| 0.8230 \| 0.7854 \| 0.6466 \| 0.9602 \| 0.8983 \| 0.9448 \|
	\| 0.0237 \| 6.18 \| 890 \| 0.2330 \| 0.7481 \| 0.8251 \| 0.7847 \| 0.6458 \| 0.9600 \| 0.8991 \| 0.9447 \|
	\| 0.0216 \| 6.25 \| 900 \| 0.2321 \| 0.7476 \| 0.8260 \| 0.7849 \| 0.6459 \| 0.9599 \| 0.8995 \| 0.9447 \|
	\| 0.0289 \| 6.32 \| 910 \| 0.2307 \| 0.7513 \| 0.8220 \| 0.7850 \| 0.6461 \| 0.9602 \| 0.8978 \| 0.9451 \|
	\| 0.0305 \| 6.39 \| 920 \| 0.2315 \| 0.7508 \| 0.8222 \| 0.7849 \| 0.6459 \| 0.9601 \| 0.8979 \| 0.9449 \|
	\| 0.0228 \| 6.46 \| 930 \| 0.2320 \| 0.7490 \| 0.8231 \| 0.7843 \| 0.6452 \| 0.9600 \| 0.8982 \| 0.9447 \|
	\| 0.0249 \| 6.53 \| 940 \| 0.2318 \| 0.7482 \| 0.8237 \| 0.7841 \| 0.6449 \| 0.9599 \| 0.8984 \| 0.9447 \|
	\| 0.022 \| 6.6 \| 950 \| 0.2327 \| 0.7476 \| 0.8251 \| 0.7845 \| 0.6454 \| 0.9599 \| 0.8990 \| 0.9446 \|
	\| 0.0278 \| 6.67 \| 960 \| 0.2333 \| 0.7472 \| 0.8253 \| 0.7843 \| 0.6451 \| 0.9598 \| 0.8991 \| 0.9446 \|
	\| 0.0228 \| 6.74 \| 970 \| 0.2332 \| 0.7473 \| 0.8253 \| 0.7843 \| 0.6452 \| 0.9598 \| 0.8991 \| 0.9446 \|
	\| 0.0289 \| 6.81 \| 980 \| 0.2331 \| 0.7476 \| 0.8249 \| 0.7844 \| 0.6452 \| 0.9599 \| 0.8989 \| 0.9446 \|
	\| 0.0228 \| 6.88 \| 990 \| 0.2330 \| 0.7477 \| 0.8249 \| 0.7844 \| 0.6453 \| 0.9599 \| 0.8989 \| 0.9446 \|
	\| 0.0179 \| 6.94 \| 1000 \| 0.2330 \| 0.7477 \| 0.8249 \| 0.7844 \| 0.6453 \| 0.9599 \| 0.8989 \| 0.9446 \|


	### Framework versions

	- Transformers 4.34.1
	- Pytorch 2.0.1
	- Datasets 2.14.6
	- Tokenizers 0.14.1