InLegalBERT-lora-no-grad / README.md

xshubhamx

Upload README.md with huggingface_hub

9325eaa verified 6 months ago

preview code

raw

history blame contribute delete

No virus

10 kB

	---
	license: mit
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- precision
	- recall
	base_model: law-ai/InLegalBERT
	model-index:
	- name: legal-bert-lora-no-grad
	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. -->

	# legal-bert-lora-no-grad

	This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.5075
	- Accuracy: 0.8280
	- Precision: 0.8290
	- Recall: 0.8280
	- Precision Macro: 0.7852
	- Recall Macro: 0.7756
	- Macro Fpr: 0.0151
	- Weighted Fpr: 0.0145
	- Weighted Specificity: 0.9775
	- Macro Specificity: 0.9871
	- Weighted Sensitivity: 0.8288
	- Macro Sensitivity: 0.7756
	- F1 Micro: 0.8288
	- F1 Macro: 0.7761
	- F1 Weighted: 0.8279

	## 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

	### 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 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:---------------:\|:------------:\|:---------:\|:------------:\|:--------------------:\|:-----------------:\|:--------------------:\|:-----------------:\|:--------:\|:--------:\|:-----------:\|
	\| 1.6412 \| 1.0 \| 643 \| 0.7925 \| 0.7514 \| 0.7190 \| 0.7514 \| 0.4123 \| 0.4707 \| 0.0237 \| 0.0231 \| 0.9699 \| 0.9814 \| 0.7514 \| 0.4707 \| 0.7514 \| 0.4277 \| 0.7283 \|
	\| 0.7481 \| 2.0 \| 1286 \| 0.6772 \| 0.7901 \| 0.7726 \| 0.7901 \| 0.5958 \| 0.6252 \| 0.0192 \| 0.0186 \| 0.9741 \| 0.9843 \| 0.7901 \| 0.6252 \| 0.7901 \| 0.5998 \| 0.7769 \|
	\| 0.6465 \| 3.0 \| 1929 \| 0.6500 \| 0.8048 \| 0.7931 \| 0.8048 \| 0.6216 \| 0.6414 \| 0.0176 \| 0.0170 \| 0.9764 \| 0.9854 \| 0.8048 \| 0.6414 \| 0.8048 \| 0.6110 \| 0.7904 \|
	\| 0.4707 \| 4.0 \| 2572 \| 0.6704 \| 0.8095 \| 0.8008 \| 0.8095 \| 0.6322 \| 0.6689 \| 0.0173 \| 0.0165 \| 0.9745 \| 0.9856 \| 0.8095 \| 0.6689 \| 0.8095 \| 0.6425 \| 0.8018 \|
	\| 0.4021 \| 5.0 \| 3215 \| 0.7320 \| 0.8280 \| 0.8269 \| 0.8280 \| 0.7782 \| 0.7573 \| 0.0154 \| 0.0146 \| 0.9765 \| 0.9870 \| 0.8280 \| 0.7573 \| 0.8280 \| 0.7571 \| 0.8219 \|
	\| 0.3627 \| 6.0 \| 3858 \| 0.6892 \| 0.8242 \| 0.8227 \| 0.8242 \| 0.7431 \| 0.7365 \| 0.0156 \| 0.0150 \| 0.9768 \| 0.9867 \| 0.8242 \| 0.7365 \| 0.8242 \| 0.7374 \| 0.8223 \|
	\| 0.2866 \| 7.0 \| 4501 \| 0.8756 \| 0.8180 \| 0.8171 \| 0.8180 \| 0.7748 \| 0.7410 \| 0.0166 \| 0.0156 \| 0.9718 \| 0.9860 \| 0.8180 \| 0.7410 \| 0.8180 \| 0.7444 \| 0.8122 \|
	\| 0.2639 \| 8.0 \| 5144 \| 0.8580 \| 0.8265 \| 0.8259 \| 0.8265 \| 0.7989 \| 0.7428 \| 0.0155 \| 0.0148 \| 0.9756 \| 0.9868 \| 0.8265 \| 0.7428 \| 0.8265 \| 0.7480 \| 0.8217 \|
	\| 0.2295 \| 9.0 \| 5787 \| 0.9366 \| 0.8257 \| 0.8231 \| 0.8257 \| 0.7725 \| 0.7465 \| 0.0155 \| 0.0149 \| 0.9762 \| 0.9868 \| 0.8257 \| 0.7465 \| 0.8257 \| 0.7524 \| 0.8223 \|
	\| 0.195 \| 10.0 \| 6430 \| 0.9685 \| 0.8273 \| 0.8236 \| 0.8273 \| 0.7595 \| 0.7515 \| 0.0153 \| 0.0147 \| 0.9767 \| 0.9869 \| 0.8273 \| 0.7515 \| 0.8273 \| 0.7528 \| 0.8241 \|
	\| 0.1617 \| 11.0 \| 7073 \| 1.0406 \| 0.8311 \| 0.8263 \| 0.8311 \| 0.7615 \| 0.7552 \| 0.0149 \| 0.0143 \| 0.9776 \| 0.9872 \| 0.8311 \| 0.7552 \| 0.8311 \| 0.7543 \| 0.8265 \|
	\| 0.1421 \| 12.0 \| 7716 \| 1.0713 \| 0.8319 \| 0.8276 \| 0.8319 \| 0.7626 \| 0.7533 \| 0.0148 \| 0.0142 \| 0.9773 \| 0.9873 \| 0.8319 \| 0.7533 \| 0.8319 \| 0.7546 \| 0.8287 \|
	\| 0.1184 \| 13.0 \| 8359 \| 1.1125 \| 0.8257 \| 0.8209 \| 0.8257 \| 0.7569 \| 0.7504 \| 0.0155 \| 0.0149 \| 0.9765 \| 0.9868 \| 0.8257 \| 0.7504 \| 0.8257 \| 0.7510 \| 0.8219 \|
	\| 0.1017 \| 14.0 \| 9002 \| 1.1926 \| 0.8211 \| 0.8215 \| 0.8211 \| 0.7675 \| 0.7815 \| 0.0159 \| 0.0153 \| 0.9776 \| 0.9866 \| 0.8211 \| 0.7815 \| 0.8211 \| 0.7727 \| 0.8196 \|
	\| 0.0752 \| 15.0 \| 9645 \| 1.2508 \| 0.8164 \| 0.8121 \| 0.8164 \| 0.7479 \| 0.7377 \| 0.0164 \| 0.0158 \| 0.9753 \| 0.9861 \| 0.8164 \| 0.7377 \| 0.8164 \| 0.7402 \| 0.8133 \|
	\| 0.0787 \| 16.0 \| 10288 \| 1.3247 \| 0.8218 \| 0.8199 \| 0.8218 \| 0.8034 \| 0.7585 \| 0.0160 \| 0.0152 \| 0.9752 \| 0.9865 \| 0.8218 \| 0.7585 \| 0.8218 \| 0.7698 \| 0.8188 \|
	\| 0.0668 \| 17.0 \| 10931 \| 1.3497 \| 0.8211 \| 0.8201 \| 0.8211 \| 0.7500 \| 0.7487 \| 0.0158 \| 0.0153 \| 0.9778 \| 0.9866 \| 0.8211 \| 0.7487 \| 0.8211 \| 0.7468 \| 0.8198 \|
	\| 0.0471 \| 18.0 \| 11574 \| 1.4278 \| 0.8164 \| 0.8174 \| 0.8164 \| 0.7672 \| 0.7670 \| 0.0165 \| 0.0158 \| 0.9759 \| 0.9862 \| 0.8164 \| 0.7670 \| 0.8164 \| 0.7644 \| 0.8159 \|
	\| 0.0492 \| 19.0 \| 12217 \| 1.4784 \| 0.8180 \| 0.8178 \| 0.8180 \| 0.7631 \| 0.7431 \| 0.0162 \| 0.0156 \| 0.9763 \| 0.9863 \| 0.8180 \| 0.7431 \| 0.8180 \| 0.7453 \| 0.8156 \|
	\| 0.0368 \| 20.0 \| 12860 \| 1.4747 \| 0.8195 \| 0.8183 \| 0.8195 \| 0.7729 \| 0.7568 \| 0.0161 \| 0.0155 \| 0.9760 \| 0.9864 \| 0.8195 \| 0.7568 \| 0.8195 \| 0.7622 \| 0.8180 \|
	\| 0.0329 \| 21.0 \| 13503 \| 1.5075 \| 0.8280 \| 0.8290 \| 0.8280 \| 0.7825 \| 0.7845 \| 0.0152 \| 0.0146 \| 0.9782 \| 0.9871 \| 0.8280 \| 0.7845 \| 0.8280 \| 0.7798 \| 0.8268 \|
	\| 0.0266 \| 22.0 \| 14146 \| 1.4783 \| 0.8273 \| 0.8262 \| 0.8273 \| 0.7780 \| 0.7612 \| 0.0153 \| 0.0147 \| 0.9779 \| 0.9870 \| 0.8273 \| 0.7612 \| 0.8273 \| 0.7651 \| 0.8247 \|
	\| 0.0302 \| 23.0 \| 14789 \| 1.5281 \| 0.8234 \| 0.8246 \| 0.8234 \| 0.7745 \| 0.7699 \| 0.0158 \| 0.0151 \| 0.9760 \| 0.9866 \| 0.8234 \| 0.7699 \| 0.8234 \| 0.7679 \| 0.8224 \|
	\| 0.0207 \| 24.0 \| 15432 \| 1.5475 \| 0.8265 \| 0.8262 \| 0.8265 \| 0.7809 \| 0.7727 \| 0.0155 \| 0.0148 \| 0.9768 \| 0.9869 \| 0.8265 \| 0.7727 \| 0.8265 \| 0.7721 \| 0.8248 \|
	\| 0.0168 \| 25.0 \| 16075 \| 1.5237 \| 0.8242 \| 0.8237 \| 0.8242 \| 0.7726 \| 0.7619 \| 0.0155 \| 0.0150 \| 0.9775 \| 0.9868 \| 0.8242 \| 0.7619 \| 0.8242 \| 0.7629 \| 0.8231 \|
	\| 0.0167 \| 26.0 \| 16718 \| 1.5815 \| 0.8234 \| 0.8255 \| 0.8234 \| 0.7766 \| 0.7728 \| 0.0156 \| 0.0151 \| 0.9775 \| 0.9867 \| 0.8234 \| 0.7728 \| 0.8234 \| 0.7707 \| 0.8232 \|
	\| 0.0127 \| 27.0 \| 17361 \| 1.6010 \| 0.8218 \| 0.8228 \| 0.8218 \| 0.7790 \| 0.7716 \| 0.0158 \| 0.0152 \| 0.9769 \| 0.9866 \| 0.8218 \| 0.7716 \| 0.8218 \| 0.7709 \| 0.8211 \|
	\| 0.0094 \| 28.0 \| 18004 \| 1.5774 \| 0.8265 \| 0.8269 \| 0.8265 \| 0.7788 \| 0.7739 \| 0.0153 \| 0.0148 \| 0.9778 \| 0.9870 \| 0.8265 \| 0.7739 \| 0.8265 \| 0.7728 \| 0.8258 \|
	\| 0.0063 \| 29.0 \| 18647 \| 1.5894 \| 0.8304 \| 0.8306 \| 0.8304 \| 0.7825 \| 0.7764 \| 0.0150 \| 0.0144 \| 0.9779 \| 0.9872 \| 0.8304 \| 0.7764 \| 0.8304 \| 0.7759 \| 0.8296 \|
	\| 0.0126 \| 30.0 \| 19290 \| 1.5927 \| 0.8288 \| 0.8291 \| 0.8288 \| 0.7852 \| 0.7756 \| 0.0151 \| 0.0145 \| 0.9775 \| 0.9871 \| 0.8288 \| 0.7756 \| 0.8288 \| 0.7761 \| 0.8279 \|


	### Framework versions

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