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	---
	license: mit
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: multiCorp_2e-05_0404
	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. -->

	# multiCorp_2e-05_0404

	This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0717
	- Precision: 0.5887
	- Recall: 0.5283
	- F1: 0.5569
	- Accuracy: 0.9834

	## 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: 2e-05
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 2000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 1.4744 \| 0.08 \| 25 \| 0.2177 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1749 \| 0.15 \| 50 \| 0.2022 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1853 \| 0.23 \| 75 \| 0.2006 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.188 \| 0.31 \| 100 \| 0.1984 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1662 \| 0.39 \| 125 \| 0.1869 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1824 \| 0.46 \| 150 \| 0.1656 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1672 \| 0.54 \| 175 \| 0.1443 \| 0.8214 \| 0.0107 \| 0.0211 \| 0.9737 \|
	\| 0.1269 \| 0.62 \| 200 \| 0.1296 \| 0.2189 \| 0.1110 \| 0.1473 \| 0.9740 \|
	\| 0.116 \| 0.7 \| 225 \| 0.1221 \| 0.3206 \| 0.1982 \| 0.2450 \| 0.9753 \|
	\| 0.111 \| 0.77 \| 250 \| 0.1208 \| 0.4109 \| 0.1968 \| 0.2662 \| 0.9762 \|
	\| 0.1382 \| 0.85 \| 275 \| 0.1149 \| 0.4680 \| 0.1495 \| 0.2266 \| 0.9763 \|
	\| 0.1136 \| 0.93 \| 300 \| 0.1051 \| 0.3749 \| 0.2303 \| 0.2853 \| 0.9767 \|
	\| 0.1043 \| 1.01 \| 325 \| 0.1066 \| 0.3451 \| 0.3315 \| 0.3381 \| 0.9762 \|
	\| 0.1062 \| 1.08 \| 350 \| 0.1012 \| 0.4072 \| 0.3319 \| 0.3657 \| 0.9769 \|
	\| 0.0834 \| 1.16 \| 375 \| 0.0972 \| 0.4079 \| 0.3301 \| 0.3649 \| 0.9781 \|
	\| 0.0923 \| 1.24 \| 400 \| 0.0973 \| 0.4598 \| 0.3500 \| 0.3975 \| 0.9781 \|
	\| 0.0932 \| 1.32 \| 425 \| 0.0932 \| 0.4649 \| 0.3384 \| 0.3917 \| 0.9789 \|
	\| 0.1044 \| 1.39 \| 450 \| 0.0934 \| 0.5039 \| 0.3319 \| 0.4002 \| 0.9792 \|
	\| 0.0962 \| 1.47 \| 475 \| 0.0926 \| 0.4636 \| 0.3045 \| 0.3676 \| 0.9788 \|
	\| 0.079 \| 1.55 \| 500 \| 0.0883 \| 0.4772 \| 0.3890 \| 0.4286 \| 0.9799 \|
	\| 0.0792 \| 1.63 \| 525 \| 0.0856 \| 0.4520 \| 0.3890 \| 0.4182 \| 0.9799 \|
	\| 0.0823 \| 1.7 \| 550 \| 0.0847 \| 0.4618 \| 0.4517 \| 0.4567 \| 0.9799 \|
	\| 0.079 \| 1.78 \| 575 \| 0.0830 \| 0.5208 \| 0.3890 \| 0.4454 \| 0.9805 \|
	\| 0.0832 \| 1.86 \| 600 \| 0.0830 \| 0.5201 \| 0.3538 \| 0.4211 \| 0.9803 \|
	\| 0.0688 \| 1.93 \| 625 \| 0.0824 \| 0.4816 \| 0.4550 \| 0.4679 \| 0.9806 \|
	\| 0.0752 \| 2.01 \| 650 \| 0.0786 \| 0.4956 \| 0.4401 \| 0.4662 \| 0.9810 \|
	\| 0.0699 \| 2.09 \| 675 \| 0.0795 \| 0.5304 \| 0.4698 \| 0.4983 \| 0.9817 \|
	\| 0.0705 \| 2.17 \| 700 \| 0.0777 \| 0.4963 \| 0.4954 \| 0.4958 \| 0.9813 \|
	\| 0.0591 \| 2.24 \| 725 \| 0.0807 \| 0.5545 \| 0.4438 \| 0.4930 \| 0.9818 \|
	\| 0.0641 \| 2.32 \| 750 \| 0.0793 \| 0.5270 \| 0.4257 \| 0.4710 \| 0.9814 \|
	\| 0.0761 \| 2.4 \| 775 \| 0.0745 \| 0.5150 \| 0.4796 \| 0.4966 \| 0.9818 \|
	\| 0.068 \| 2.48 \| 800 \| 0.0765 \| 0.5741 \| 0.4262 \| 0.4892 \| 0.9819 \|
	\| 0.0596 \| 2.55 \| 825 \| 0.0757 \| 0.5346 \| 0.4341 \| 0.4791 \| 0.9817 \|
	\| 0.0648 \| 2.63 \| 850 \| 0.0724 \| 0.5526 \| 0.5023 \| 0.5263 \| 0.9827 \|
	\| 0.0619 \| 2.71 \| 875 \| 0.0739 \| 0.5471 \| 0.5288 \| 0.5378 \| 0.9824 \|
	\| 0.06 \| 2.79 \| 900 \| 0.0738 \| 0.5627 \| 0.5227 \| 0.5420 \| 0.9829 \|
	\| 0.058 \| 2.86 \| 925 \| 0.0740 \| 0.5456 \| 0.5107 \| 0.5276 \| 0.9825 \|
	\| 0.0624 \| 2.94 \| 950 \| 0.0712 \| 0.5665 \| 0.5237 \| 0.5443 \| 0.9832 \|
	\| 0.0602 \| 3.02 \| 975 \| 0.0700 \| 0.5368 \| 0.5181 \| 0.5273 \| 0.9828 \|
	\| 0.049 \| 3.1 \| 1000 \| 0.0720 \| 0.5710 \| 0.5339 \| 0.5518 \| 0.9832 \|
	\| 0.0562 \| 3.17 \| 1025 \| 0.0715 \| 0.5847 \| 0.5176 \| 0.5491 \| 0.9831 \|
	\| 0.0559 \| 3.25 \| 1050 \| 0.0711 \| 0.5921 \| 0.5460 \| 0.5681 \| 0.9834 \|
	\| 0.054 \| 3.33 \| 1075 \| 0.0707 \| 0.6062 \| 0.5395 \| 0.5709 \| 0.9837 \|
	\| 0.0522 \| 3.41 \| 1100 \| 0.0716 \| 0.5530 \| 0.5209 \| 0.5365 \| 0.9828 \|
	\| 0.0456 \| 3.48 \| 1125 \| 0.0717 \| 0.5887 \| 0.5283 \| 0.5569 \| 0.9834 \|


	### Framework versions

	- Transformers 4.27.4
	- Pytorch 2.0.0+cu118
	- Datasets 2.11.0
	- Tokenizers 0.13.2

	---
	license: mit
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: multiCorp_2e-05_0404
	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. -->

	# multiCorp_2e-05_0404

	This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0717
	- Precision: 0.5887
	- Recall: 0.5283
	- F1: 0.5569
	- Accuracy: 0.9834

	## 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: 2e-05
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 2000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 1.4744 \| 0.08 \| 25 \| 0.2177 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1749 \| 0.15 \| 50 \| 0.2022 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1853 \| 0.23 \| 75 \| 0.2006 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.188 \| 0.31 \| 100 \| 0.1984 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1662 \| 0.39 \| 125 \| 0.1869 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1824 \| 0.46 \| 150 \| 0.1656 \| 0.0 \| 0.0 \| 0.0 \| 0.9735 \|
	\| 0.1672 \| 0.54 \| 175 \| 0.1443 \| 0.8214 \| 0.0107 \| 0.0211 \| 0.9737 \|
	\| 0.1269 \| 0.62 \| 200 \| 0.1296 \| 0.2189 \| 0.1110 \| 0.1473 \| 0.9740 \|
	\| 0.116 \| 0.7 \| 225 \| 0.1221 \| 0.3206 \| 0.1982 \| 0.2450 \| 0.9753 \|
	\| 0.111 \| 0.77 \| 250 \| 0.1208 \| 0.4109 \| 0.1968 \| 0.2662 \| 0.9762 \|
	\| 0.1382 \| 0.85 \| 275 \| 0.1149 \| 0.4680 \| 0.1495 \| 0.2266 \| 0.9763 \|
	\| 0.1136 \| 0.93 \| 300 \| 0.1051 \| 0.3749 \| 0.2303 \| 0.2853 \| 0.9767 \|
	\| 0.1043 \| 1.01 \| 325 \| 0.1066 \| 0.3451 \| 0.3315 \| 0.3381 \| 0.9762 \|
	\| 0.1062 \| 1.08 \| 350 \| 0.1012 \| 0.4072 \| 0.3319 \| 0.3657 \| 0.9769 \|
	\| 0.0834 \| 1.16 \| 375 \| 0.0972 \| 0.4079 \| 0.3301 \| 0.3649 \| 0.9781 \|
	\| 0.0923 \| 1.24 \| 400 \| 0.0973 \| 0.4598 \| 0.3500 \| 0.3975 \| 0.9781 \|
	\| 0.0932 \| 1.32 \| 425 \| 0.0932 \| 0.4649 \| 0.3384 \| 0.3917 \| 0.9789 \|
	\| 0.1044 \| 1.39 \| 450 \| 0.0934 \| 0.5039 \| 0.3319 \| 0.4002 \| 0.9792 \|
	\| 0.0962 \| 1.47 \| 475 \| 0.0926 \| 0.4636 \| 0.3045 \| 0.3676 \| 0.9788 \|
	\| 0.079 \| 1.55 \| 500 \| 0.0883 \| 0.4772 \| 0.3890 \| 0.4286 \| 0.9799 \|
	\| 0.0792 \| 1.63 \| 525 \| 0.0856 \| 0.4520 \| 0.3890 \| 0.4182 \| 0.9799 \|
	\| 0.0823 \| 1.7 \| 550 \| 0.0847 \| 0.4618 \| 0.4517 \| 0.4567 \| 0.9799 \|
	\| 0.079 \| 1.78 \| 575 \| 0.0830 \| 0.5208 \| 0.3890 \| 0.4454 \| 0.9805 \|
	\| 0.0832 \| 1.86 \| 600 \| 0.0830 \| 0.5201 \| 0.3538 \| 0.4211 \| 0.9803 \|
	\| 0.0688 \| 1.93 \| 625 \| 0.0824 \| 0.4816 \| 0.4550 \| 0.4679 \| 0.9806 \|
	\| 0.0752 \| 2.01 \| 650 \| 0.0786 \| 0.4956 \| 0.4401 \| 0.4662 \| 0.9810 \|
	\| 0.0699 \| 2.09 \| 675 \| 0.0795 \| 0.5304 \| 0.4698 \| 0.4983 \| 0.9817 \|
	\| 0.0705 \| 2.17 \| 700 \| 0.0777 \| 0.4963 \| 0.4954 \| 0.4958 \| 0.9813 \|
	\| 0.0591 \| 2.24 \| 725 \| 0.0807 \| 0.5545 \| 0.4438 \| 0.4930 \| 0.9818 \|
	\| 0.0641 \| 2.32 \| 750 \| 0.0793 \| 0.5270 \| 0.4257 \| 0.4710 \| 0.9814 \|
	\| 0.0761 \| 2.4 \| 775 \| 0.0745 \| 0.5150 \| 0.4796 \| 0.4966 \| 0.9818 \|
	\| 0.068 \| 2.48 \| 800 \| 0.0765 \| 0.5741 \| 0.4262 \| 0.4892 \| 0.9819 \|
	\| 0.0596 \| 2.55 \| 825 \| 0.0757 \| 0.5346 \| 0.4341 \| 0.4791 \| 0.9817 \|
	\| 0.0648 \| 2.63 \| 850 \| 0.0724 \| 0.5526 \| 0.5023 \| 0.5263 \| 0.9827 \|
	\| 0.0619 \| 2.71 \| 875 \| 0.0739 \| 0.5471 \| 0.5288 \| 0.5378 \| 0.9824 \|
	\| 0.06 \| 2.79 \| 900 \| 0.0738 \| 0.5627 \| 0.5227 \| 0.5420 \| 0.9829 \|
	\| 0.058 \| 2.86 \| 925 \| 0.0740 \| 0.5456 \| 0.5107 \| 0.5276 \| 0.9825 \|
	\| 0.0624 \| 2.94 \| 950 \| 0.0712 \| 0.5665 \| 0.5237 \| 0.5443 \| 0.9832 \|
	\| 0.0602 \| 3.02 \| 975 \| 0.0700 \| 0.5368 \| 0.5181 \| 0.5273 \| 0.9828 \|
	\| 0.049 \| 3.1 \| 1000 \| 0.0720 \| 0.5710 \| 0.5339 \| 0.5518 \| 0.9832 \|
	\| 0.0562 \| 3.17 \| 1025 \| 0.0715 \| 0.5847 \| 0.5176 \| 0.5491 \| 0.9831 \|
	\| 0.0559 \| 3.25 \| 1050 \| 0.0711 \| 0.5921 \| 0.5460 \| 0.5681 \| 0.9834 \|
	\| 0.054 \| 3.33 \| 1075 \| 0.0707 \| 0.6062 \| 0.5395 \| 0.5709 \| 0.9837 \|
	\| 0.0522 \| 3.41 \| 1100 \| 0.0716 \| 0.5530 \| 0.5209 \| 0.5365 \| 0.9828 \|
	\| 0.0456 \| 3.48 \| 1125 \| 0.0717 \| 0.5887 \| 0.5283 \| 0.5569 \| 0.9834 \|


	### Framework versions

	- Transformers 4.27.4
	- Pytorch 2.0.0+cu118
	- Datasets 2.11.0
	- Tokenizers 0.13.2