bart-base / README.md

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

	This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.7146
	- Accuracy: 0.8180
	- Precision: 0.8189
	- Recall: 0.8180
	- Precision Macro: 0.7608
	- Recall Macro: 0.7799
	- Macro Fpr: 0.0157
	- Weighted Fpr: 0.0151
	- Weighted Specificity: 0.9781
	- Macro Specificity: 0.9868
	- Weighted Sensitivity: 0.8234
	- Macro Sensitivity: 0.7799
	- F1 Micro: 0.8234
	- F1 Macro: 0.7642
	- F1 Weighted: 0.8237

	## 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: 8
	- eval_batch_size: 8
	- 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 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:---------------:\|:------------:\|:---------:\|:------------:\|:--------------------:\|:-----------------:\|:--------------------:\|:-----------------:\|:--------:\|:--------:\|:-----------:\|
	\| 1.2028 \| 1.0 \| 643 \| 0.8430 \| 0.7599 \| 0.7601 \| 0.7599 \| 0.6004 \| 0.6367 \| 0.0232 \| 0.0221 \| 0.9655 \| 0.9817 \| 0.7599 \| 0.6367 \| 0.7599 \| 0.6064 \| 0.7489 \|
	\| 0.715 \| 2.0 \| 1286 \| 0.7332 \| 0.7932 \| 0.8020 \| 0.7932 \| 0.7386 \| 0.7321 \| 0.0190 \| 0.0183 \| 0.9745 \| 0.9845 \| 0.7932 \| 0.7321 \| 0.7932 \| 0.7214 \| 0.7853 \|
	\| 0.578 \| 3.0 \| 1929 \| 0.8045 \| 0.7940 \| 0.8075 \| 0.7940 \| 0.7231 \| 0.7069 \| 0.0185 \| 0.0182 \| 0.9775 \| 0.9848 \| 0.7940 \| 0.7069 \| 0.7940 \| 0.6998 \| 0.7901 \|
	\| 0.3938 \| 4.0 \| 2572 \| 0.8291 \| 0.8156 \| 0.8171 \| 0.8156 \| 0.7937 \| 0.7218 \| 0.0169 \| 0.0159 \| 0.9711 \| 0.9858 \| 0.8156 \| 0.7218 \| 0.8156 \| 0.7369 \| 0.8105 \|
	\| 0.3238 \| 5.0 \| 3215 \| 0.8889 \| 0.7940 \| 0.8146 \| 0.7940 \| 0.7464 \| 0.7515 \| 0.0188 \| 0.0182 \| 0.9762 \| 0.9847 \| 0.7940 \| 0.7515 \| 0.7940 \| 0.7361 \| 0.7995 \|
	\| 0.246 \| 6.0 \| 3858 \| 1.1629 \| 0.7955 \| 0.8067 \| 0.7955 \| 0.7483 \| 0.7600 \| 0.0186 \| 0.0180 \| 0.9749 \| 0.9847 \| 0.7955 \| 0.7600 \| 0.7955 \| 0.7362 \| 0.7946 \|
	\| 0.1791 \| 7.0 \| 4501 \| 1.1354 \| 0.8180 \| 0.8151 \| 0.8180 \| 0.7832 \| 0.7697 \| 0.0165 \| 0.0156 \| 0.9747 \| 0.9862 \| 0.8180 \| 0.7697 \| 0.8180 \| 0.7736 \| 0.8147 \|
	\| 0.1305 \| 8.0 \| 5144 \| 1.2825 \| 0.8110 \| 0.8148 \| 0.8110 \| 0.7422 \| 0.7489 \| 0.0169 \| 0.0164 \| 0.9765 \| 0.9858 \| 0.8110 \| 0.7489 \| 0.8110 \| 0.7369 \| 0.8088 \|
	\| 0.0924 \| 9.0 \| 5787 \| 1.4217 \| 0.8040 \| 0.8114 \| 0.8040 \| 0.7465 \| 0.7809 \| 0.0178 \| 0.0171 \| 0.9762 \| 0.9853 \| 0.8040 \| 0.7809 \| 0.8040 \| 0.7560 \| 0.8015 \|
	\| 0.0953 \| 10.0 \| 6430 \| 1.5552 \| 0.8025 \| 0.8056 \| 0.8025 \| 0.7702 \| 0.7822 \| 0.0183 \| 0.0173 \| 0.9712 \| 0.9849 \| 0.8025 \| 0.7822 \| 0.8025 \| 0.7661 \| 0.8001 \|
	\| 0.0617 \| 11.0 \| 7073 \| 1.5224 \| 0.8040 \| 0.8144 \| 0.8040 \| 0.7457 \| 0.7512 \| 0.0176 \| 0.0171 \| 0.9762 \| 0.9853 \| 0.8040 \| 0.7512 \| 0.8040 \| 0.7422 \| 0.8070 \|
	\| 0.0582 \| 12.0 \| 7716 \| 1.6428 \| 0.7971 \| 0.8148 \| 0.7971 \| 0.7470 \| 0.7655 \| 0.0183 \| 0.0179 \| 0.9771 \| 0.9849 \| 0.7971 \| 0.7655 \| 0.7971 \| 0.7465 \| 0.8022 \|
	\| 0.0511 \| 13.0 \| 8359 \| 1.4952 \| 0.8195 \| 0.8208 \| 0.8195 \| 0.7645 \| 0.7580 \| 0.0162 \| 0.0155 \| 0.9759 \| 0.9864 \| 0.8195 \| 0.7580 \| 0.8195 \| 0.7586 \| 0.8187 \|
	\| 0.0476 \| 14.0 \| 9002 \| 1.7132 \| 0.7971 \| 0.7958 \| 0.7971 \| 0.7637 \| 0.7328 \| 0.0189 \| 0.0179 \| 0.9708 \| 0.9845 \| 0.7971 \| 0.7328 \| 0.7971 \| 0.7417 \| 0.7913 \|
	\| 0.0375 \| 15.0 \| 9645 \| 1.7058 \| 0.8002 \| 0.8110 \| 0.8002 \| 0.7369 \| 0.7696 \| 0.0182 \| 0.0175 \| 0.9757 \| 0.9851 \| 0.8002 \| 0.7696 \| 0.8002 \| 0.7437 \| 0.8017 \|
	\| 0.0241 \| 16.0 \| 10288 \| 1.7146 \| 0.8180 \| 0.8189 \| 0.8180 \| 0.7852 \| 0.7787 \| 0.0162 \| 0.0156 \| 0.9761 \| 0.9863 \| 0.8180 \| 0.7787 \| 0.8180 \| 0.7780 \| 0.8174 \|
	\| 0.0226 \| 17.0 \| 10931 \| 1.7035 \| 0.8203 \| 0.8238 \| 0.8203 \| 0.7732 \| 0.7781 \| 0.0160 \| 0.0154 \| 0.9774 \| 0.9865 \| 0.8203 \| 0.7781 \| 0.8203 \| 0.7714 \| 0.8206 \|
	\| 0.0189 \| 18.0 \| 11574 \| 1.8079 \| 0.8164 \| 0.8160 \| 0.8164 \| 0.7583 \| 0.7677 \| 0.0166 \| 0.0158 \| 0.9749 \| 0.9861 \| 0.8164 \| 0.7677 \| 0.8164 \| 0.7578 \| 0.8149 \|
	\| 0.026 \| 19.0 \| 12217 \| 1.8187 \| 0.8125 \| 0.8170 \| 0.8125 \| 0.7675 \| 0.7833 \| 0.0169 \| 0.0162 \| 0.9748 \| 0.9858 \| 0.8125 \| 0.7833 \| 0.8125 \| 0.7719 \| 0.8138 \|
	\| 0.0101 \| 20.0 \| 12860 \| 1.8354 \| 0.8187 \| 0.8220 \| 0.8187 \| 0.7748 \| 0.7818 \| 0.0161 \| 0.0156 \| 0.9772 \| 0.9864 \| 0.8187 \| 0.7818 \| 0.8187 \| 0.7710 \| 0.8180 \|
	\| 0.0216 \| 21.0 \| 13503 \| 1.8372 \| 0.8156 \| 0.8219 \| 0.8156 \| 0.7502 \| 0.7858 \| 0.0163 \| 0.0159 \| 0.9789 \| 0.9863 \| 0.8156 \| 0.7858 \| 0.8156 \| 0.7618 \| 0.8164 \|
	\| 0.0138 \| 22.0 \| 14146 \| 1.8472 \| 0.8203 \| 0.8263 \| 0.8203 \| 0.7613 \| 0.7796 \| 0.0159 \| 0.0154 \| 0.9786 \| 0.9866 \| 0.8203 \| 0.7796 \| 0.8203 \| 0.7662 \| 0.8222 \|
	\| 0.0169 \| 23.0 \| 14789 \| 1.8104 \| 0.8218 \| 0.8252 \| 0.8218 \| 0.7719 \| 0.7595 \| 0.0160 \| 0.0152 \| 0.9749 \| 0.9865 \| 0.8218 \| 0.7595 \| 0.8218 \| 0.7607 \| 0.8209 \|
	\| 0.0079 \| 24.0 \| 15432 \| 1.9253 \| 0.8110 \| 0.8202 \| 0.8110 \| 0.7622 \| 0.7576 \| 0.0171 \| 0.0164 \| 0.9759 \| 0.9858 \| 0.8110 \| 0.7576 \| 0.8110 \| 0.7524 \| 0.8123 \|
	\| 0.0017 \| 25.0 \| 16075 \| 1.9111 \| 0.8156 \| 0.8193 \| 0.8156 \| 0.7554 \| 0.7742 \| 0.0164 \| 0.0159 \| 0.9775 \| 0.9862 \| 0.8156 \| 0.7742 \| 0.8156 \| 0.7594 \| 0.8155 \|
	\| 0.0071 \| 26.0 \| 16718 \| 1.8809 \| 0.8187 \| 0.8244 \| 0.8187 \| 0.7595 \| 0.7749 \| 0.0161 \| 0.0156 \| 0.9783 \| 0.9865 \| 0.8187 \| 0.7749 \| 0.8187 \| 0.7601 \| 0.8199 \|
	\| 0.0032 \| 27.0 \| 17361 \| 1.8246 \| 0.8273 \| 0.8333 \| 0.8273 \| 0.7727 \| 0.7807 \| 0.0152 \| 0.0147 \| 0.9786 \| 0.9871 \| 0.8273 \| 0.7807 \| 0.8273 \| 0.7718 \| 0.8289 \|
	\| 0.0014 \| 28.0 \| 18004 \| 1.8354 \| 0.8265 \| 0.8337 \| 0.8265 \| 0.7624 \| 0.7806 \| 0.0154 \| 0.0148 \| 0.9784 \| 0.9870 \| 0.8265 \| 0.7806 \| 0.8265 \| 0.7648 \| 0.8282 \|
	\| 0.0004 \| 29.0 \| 18647 \| 1.8558 \| 0.8234 \| 0.8277 \| 0.8234 \| 0.7616 \| 0.7801 \| 0.0157 \| 0.0151 \| 0.9778 \| 0.9867 \| 0.8234 \| 0.7801 \| 0.8234 \| 0.7646 \| 0.8234 \|
	\| 0.0012 \| 30.0 \| 19290 \| 1.8392 \| 0.8234 \| 0.8281 \| 0.8234 \| 0.7608 \| 0.7799 \| 0.0157 \| 0.0151 \| 0.9781 \| 0.9868 \| 0.8234 \| 0.7799 \| 0.8234 \| 0.7642 \| 0.8237 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.1.0+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.1