bart-large / README.md

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	base_model: facebook/bart-large
	metrics:
	- accuracy
	- precision
	- recall
	model-index:
	- name: bart-large
	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. -->

	# bart-large

	This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.0027
	- Accuracy: 0.7916
	- Precision: 0.7858
	- Recall: 0.7916
	- Precision Macro: 0.7201
	- Recall Macro: 0.7056
	- Macro Fpr: 0.0201
	- Weighted Fpr: 0.0195
	- Weighted Specificity: 0.9714
	- Macro Specificity: 0.9836
	- Weighted Sensitivity: 0.7823
	- Macro Sensitivity: 0.7056
	- F1 Micro: 0.7823
	- F1 Macro: 0.7080
	- F1 Weighted: 0.7801

	## 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: 5e-05
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| Precision Macro \| Recall Macro \| Macro Fpr \| Weighted Fpr \| Weighted Specificity \| Macro Specificity \| Weighted Sensitivity \| Macro Sensitivity \| F1 Micro \| F1 Macro \| F1 Weighted \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:---------------:\|:------------:\|:---------:\|:------------:\|:--------------------:\|:-----------------:\|:--------------------:\|:-----------------:\|:--------:\|:--------:\|:-----------:\|
	\| 1.1685 \| 1.0 \| 2569 \| 1.2587 \| 0.6847 \| 0.6360 \| 0.6847 \| 0.4176 \| 0.4720 \| 0.0331 \| 0.0318 \| 0.9550 \| 0.9760 \| 0.6847 \| 0.4720 \| 0.6847 \| 0.4296 \| 0.6471 \|
	\| 1.1965 \| 2.0 \| 5138 \| 1.1623 \| 0.6638 \| 0.6943 \| 0.6638 \| 0.4564 \| 0.4261 \| 0.0342 \| 0.0349 \| 0.9654 \| 0.9753 \| 0.6638 \| 0.4261 \| 0.6638 \| 0.3955 \| 0.6468 \|
	\| 1.189 \| 3.0 \| 7707 \| 1.3574 \| 0.7235 \| 0.7220 \| 0.7235 \| 0.5413 \| 0.5528 \| 0.0271 \| 0.0266 \| 0.9628 \| 0.9791 \| 0.7235 \| 0.5528 \| 0.7235 \| 0.5196 \| 0.7031 \|
	\| 1.0127 \| 4.0 \| 10276 \| 1.4685 \| 0.7668 \| 0.7584 \| 0.7668 \| 0.6671 \| 0.6202 \| 0.0224 \| 0.0213 \| 0.9653 \| 0.9821 \| 0.7668 \| 0.6202 \| 0.7668 \| 0.6233 \| 0.7569 \|
	\| 1.0205 \| 5.0 \| 12845 \| 1.4232 \| 0.7668 \| 0.7711 \| 0.7668 \| 0.6765 \| 0.6872 \| 0.0215 \| 0.0213 \| 0.9737 \| 0.9827 \| 0.7668 \| 0.6872 \| 0.7668 \| 0.6732 \| 0.7643 \|
	\| 0.7927 \| 6.0 \| 15414 \| 1.5678 \| 0.7428 \| 0.7451 \| 0.7428 \| 0.6489 \| 0.6333 \| 0.0248 \| 0.0241 \| 0.9690 \| 0.9808 \| 0.7428 \| 0.6333 \| 0.7428 \| 0.6108 \| 0.7292 \|
	\| 0.7701 \| 7.0 \| 17983 \| 1.7337 \| 0.7467 \| 0.7600 \| 0.7467 \| 0.6863 \| 0.6536 \| 0.0240 \| 0.0237 \| 0.9680 \| 0.9810 \| 0.7467 \| 0.6536 \| 0.7467 \| 0.6584 \| 0.7399 \|
	\| 0.584 \| 8.0 \| 20552 \| 1.6188 \| 0.7692 \| 0.7766 \| 0.7692 \| 0.6979 \| 0.7065 \| 0.0214 \| 0.0210 \| 0.9706 \| 0.9827 \| 0.7692 \| 0.7065 \| 0.7692 \| 0.6980 \| 0.7683 \|
	\| 0.5659 \| 9.0 \| 23121 \| 1.6983 \| 0.7599 \| 0.7665 \| 0.7599 \| 0.7000 \| 0.6804 \| 0.0227 \| 0.0221 \| 0.9695 \| 0.9820 \| 0.7599 \| 0.6804 \| 0.7599 \| 0.6728 \| 0.7542 \|
	\| 0.7021 \| 10.0 \| 25690 \| 1.6445 \| 0.7699 \| 0.7656 \| 0.7699 \| 0.7144 \| 0.6857 \| 0.0223 \| 0.0209 \| 0.9608 \| 0.9821 \| 0.7699 \| 0.6857 \| 0.7699 \| 0.6954 \| 0.7634 \|
	\| 0.6216 \| 11.0 \| 28259 \| 1.6562 \| 0.7676 \| 0.7634 \| 0.7676 \| 0.6856 \| 0.6776 \| 0.0223 \| 0.0212 \| 0.9640 \| 0.9821 \| 0.7676 \| 0.6776 \| 0.7676 \| 0.6786 \| 0.7624 \|
	\| 0.6408 \| 12.0 \| 30828 \| 1.6682 \| 0.7668 \| 0.7629 \| 0.7668 \| 0.6706 \| 0.6719 \| 0.0223 \| 0.0213 \| 0.9666 \| 0.9822 \| 0.7668 \| 0.6719 \| 0.7668 \| 0.6666 \| 0.7608 \|
	\| 0.523 \| 13.0 \| 33397 \| 1.7727 \| 0.7653 \| 0.7674 \| 0.7653 \| 0.8238 \| 0.6934 \| 0.0226 \| 0.0214 \| 0.9659 \| 0.9821 \| 0.7653 \| 0.6934 \| 0.7653 \| 0.7066 \| 0.7534 \|
	\| 0.3688 \| 14.0 \| 35966 \| 1.8404 \| 0.7792 \| 0.7788 \| 0.7792 \| 0.7229 \| 0.6921 \| 0.0209 \| 0.0198 \| 0.9675 \| 0.9831 \| 0.7792 \| 0.6921 \| 0.7792 \| 0.6960 \| 0.7731 \|
	\| 0.2394 \| 15.0 \| 38535 \| 1.7885 \| 0.7816 \| 0.7809 \| 0.7816 \| 0.7441 \| 0.7115 \| 0.0210 \| 0.0196 \| 0.9628 \| 0.9830 \| 0.7816 \| 0.7115 \| 0.7816 \| 0.7230 \| 0.7765 \|
	\| 0.2734 \| 16.0 \| 41104 \| 1.8944 \| 0.7777 \| 0.7870 \| 0.7777 \| 0.7539 \| 0.7265 \| 0.0203 \| 0.0200 \| 0.9724 \| 0.9833 \| 0.7777 \| 0.7265 \| 0.7777 \| 0.7295 \| 0.7777 \|
	\| 0.4319 \| 17.0 \| 43673 \| 1.7744 \| 0.7885 \| 0.7847 \| 0.7885 \| 0.7247 \| 0.7320 \| 0.0195 \| 0.0188 \| 0.9718 \| 0.9840 \| 0.7885 \| 0.7320 \| 0.7885 \| 0.7269 \| 0.7855 \|
	\| 0.2347 \| 18.0 \| 46242 \| 2.0036 \| 0.7413 \| 0.7352 \| 0.7413 \| 0.6934 \| 0.6799 \| 0.0255 \| 0.0243 \| 0.9597 \| 0.9801 \| 0.7413 \| 0.6799 \| 0.7413 \| 0.6825 \| 0.7354 \|
	\| 0.1882 \| 19.0 \| 48811 \| 1.9298 \| 0.7816 \| 0.7804 \| 0.7816 \| 0.7243 \| 0.7262 \| 0.0202 \| 0.0196 \| 0.9708 \| 0.9835 \| 0.7816 \| 0.7262 \| 0.7816 \| 0.7225 \| 0.7792 \|
	\| 0.1799 \| 20.0 \| 51380 \| 1.9688 \| 0.7792 \| 0.7892 \| 0.7792 \| 0.7312 \| 0.7343 \| 0.0205 \| 0.0198 \| 0.9714 \| 0.9834 \| 0.7792 \| 0.7343 \| 0.7792 \| 0.7242 \| 0.7779 \|
	\| 0.1366 \| 21.0 \| 53949 \| 1.9910 \| 0.7847 \| 0.7846 \| 0.7847 \| 0.7148 \| 0.7455 \| 0.0198 \| 0.0192 \| 0.9730 \| 0.9838 \| 0.7847 \| 0.7455 \| 0.7847 \| 0.7265 \| 0.7833 \|
	\| 0.1793 \| 22.0 \| 56518 \| 2.2548 \| 0.7630 \| 0.7648 \| 0.7630 \| 0.7150 \| 0.7273 \| 0.0230 \| 0.0217 \| 0.9633 \| 0.9818 \| 0.7630 \| 0.7273 \| 0.7630 \| 0.7150 \| 0.7582 \|
	\| 0.1749 \| 23.0 \| 59087 \| 2.1109 \| 0.7816 \| 0.7768 \| 0.7816 \| 0.7466 \| 0.7230 \| 0.0205 \| 0.0196 \| 0.9690 \| 0.9834 \| 0.7816 \| 0.7230 \| 0.7816 \| 0.7289 \| 0.7774 \|
	\| 0.1154 \| 24.0 \| 61656 \| 2.0637 \| 0.7878 \| 0.7837 \| 0.7878 \| 0.7590 \| 0.7269 \| 0.0196 \| 0.0189 \| 0.9718 \| 0.9840 \| 0.7878 \| 0.7269 \| 0.7878 \| 0.7331 \| 0.7828 \|
	\| 0.1447 \| 25.0 \| 64225 \| 2.0027 \| 0.7916 \| 0.7858 \| 0.7916 \| 0.7750 \| 0.7299 \| 0.0194 \| 0.0185 \| 0.9697 \| 0.9841 \| 0.7916 \| 0.7299 \| 0.7916 \| 0.7408 \| 0.7861 \|
	\| 0.0806 \| 26.0 \| 66794 \| 2.0777 \| 0.7885 \| 0.7831 \| 0.7885 \| 0.7162 \| 0.7134 \| 0.0196 \| 0.0188 \| 0.9715 \| 0.9840 \| 0.7885 \| 0.7134 \| 0.7885 \| 0.7118 \| 0.7840 \|
	\| 0.0407 \| 27.0 \| 69363 \| 2.1754 \| 0.7885 \| 0.7863 \| 0.7885 \| 0.7192 \| 0.7080 \| 0.0194 \| 0.0188 \| 0.9725 \| 0.9841 \| 0.7885 \| 0.7080 \| 0.7885 \| 0.7105 \| 0.7866 \|
	\| 0.0701 \| 28.0 \| 71932 \| 2.1578 \| 0.7823 \| 0.7817 \| 0.7823 \| 0.7130 \| 0.7097 \| 0.0201 \| 0.0195 \| 0.9714 \| 0.9836 \| 0.7823 \| 0.7097 \| 0.7823 \| 0.7066 \| 0.7810 \|
	\| 0.1034 \| 29.0 \| 74501 \| 2.2132 \| 0.7800 \| 0.7789 \| 0.7800 \| 0.7163 \| 0.7044 \| 0.0203 \| 0.0197 \| 0.9713 \| 0.9834 \| 0.7800 \| 0.7044 \| 0.7800 \| 0.7064 \| 0.7785 \|
	\| 0.0388 \| 30.0 \| 77070 \| 2.1833 \| 0.7823 \| 0.7806 \| 0.7823 \| 0.7201 \| 0.7056 \| 0.0201 \| 0.0195 \| 0.9714 \| 0.9836 \| 0.7823 \| 0.7056 \| 0.7823 \| 0.7080 \| 0.7801 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.1.0+cu121
	- Datasets 2.19.0
	- Tokenizers 0.15.1