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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	datasets:
	- crows_pairs
	metrics:
	- accuracy
	model-index:
	- name: t5-small_crows_pairs_finetuned
	results:
	- task:
	name: Sequence-to-sequence Language Modeling
	type: text2text-generation
	dataset:
	name: crows_pairs
	type: crows_pairs
	config: crows_pairs
	split: test
	args: crows_pairs
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.7218543046357616
	---

	<!-- 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. -->

	# t5-small_crows_pairs_finetuned

	This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the crows_pairs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.0876
	- Accuracy: 0.7219
	- Tp: 0.4901
	- Tn: 0.2318
	- Fp: 0.2649
	- Fn: 0.0132

	## 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: 0.0005
	- train_batch_size: 32
	- eval_batch_size: 32
	- 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 \| Tp \| Tn \| Fp \| Fn \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|
	\| 0.4776 \| 0.53 \| 20 \| 0.3559 \| 0.5033 \| 0.5033 \| 0.0 \| 0.4967 \| 0.0 \|
	\| 0.3708 \| 1.05 \| 40 \| 0.3431 \| 0.5265 \| 0.5033 \| 0.0232 \| 0.4735 \| 0.0 \|
	\| 0.3905 \| 1.58 \| 60 \| 0.3414 \| 0.6325 \| 0.5033 \| 0.1291 \| 0.3675 \| 0.0 \|
	\| 0.3526 \| 2.11 \| 80 \| 0.3260 \| 0.5464 \| 0.5033 \| 0.0430 \| 0.4536 \| 0.0 \|
	\| 0.2734 \| 2.63 \| 100 \| 0.3047 \| 0.8179 \| 0.4934 \| 0.3245 \| 0.1722 \| 0.0099 \|
	\| 0.2429 \| 3.16 \| 120 \| 0.2631 \| 0.6623 \| 0.5033 \| 0.1589 \| 0.3377 \| 0.0 \|
	\| 0.2161 \| 3.68 \| 140 \| 0.2829 \| 0.7152 \| 0.4901 \| 0.2252 \| 0.2715 \| 0.0132 \|
	\| 0.1617 \| 4.21 \| 160 \| 0.2947 \| 0.7748 \| 0.4901 \| 0.2848 \| 0.2119 \| 0.0132 \|
	\| 0.1776 \| 4.74 \| 180 \| 0.3258 \| 0.7384 \| 0.4901 \| 0.2483 \| 0.2483 \| 0.0132 \|
	\| 0.1113 \| 5.26 \| 200 \| 0.3362 \| 0.7815 \| 0.4901 \| 0.2914 \| 0.2053 \| 0.0132 \|
	\| 0.1008 \| 5.79 \| 220 \| 0.4016 \| 0.7748 \| 0.4868 \| 0.2881 \| 0.2086 \| 0.0166 \|
	\| 0.0572 \| 6.32 \| 240 \| 0.4486 \| 0.7219 \| 0.4901 \| 0.2318 \| 0.2649 \| 0.0132 \|
	\| 0.0701 \| 6.84 \| 260 \| 0.4561 \| 0.7384 \| 0.4901 \| 0.2483 \| 0.2483 \| 0.0132 \|
	\| 0.051 \| 7.37 \| 280 \| 0.4813 \| 0.7815 \| 0.4868 \| 0.2947 \| 0.2020 \| 0.0166 \|
	\| 0.0408 \| 7.89 \| 300 \| 0.5279 \| 0.7815 \| 0.4868 \| 0.2947 \| 0.2020 \| 0.0166 \|
	\| 0.0326 \| 8.42 \| 320 \| 0.6454 \| 0.7550 \| 0.4934 \| 0.2616 \| 0.2351 \| 0.0099 \|
	\| 0.0361 \| 8.95 \| 340 \| 0.7559 \| 0.7417 \| 0.4901 \| 0.2517 \| 0.2450 \| 0.0132 \|
	\| 0.0304 \| 9.47 \| 360 \| 0.7422 \| 0.7318 \| 0.4868 \| 0.2450 \| 0.2517 \| 0.0166 \|
	\| 0.0299 \| 10.0 \| 380 \| 0.7770 \| 0.7450 \| 0.4868 \| 0.2583 \| 0.2384 \| 0.0166 \|
	\| 0.0227 \| 10.53 \| 400 \| 0.7033 \| 0.7947 \| 0.4801 \| 0.3146 \| 0.1821 \| 0.0232 \|
	\| 0.017 \| 11.05 \| 420 \| 0.7220 \| 0.7649 \| 0.4801 \| 0.2848 \| 0.2119 \| 0.0232 \|
	\| 0.0166 \| 11.58 \| 440 \| 0.7674 \| 0.7649 \| 0.4636 \| 0.3013 \| 0.1954 \| 0.0397 \|
	\| 0.0123 \| 12.11 \| 460 \| 0.8153 \| 0.7616 \| 0.4834 \| 0.2781 \| 0.2185 \| 0.0199 \|
	\| 0.0084 \| 12.63 \| 480 \| 0.8422 \| 0.7483 \| 0.4834 \| 0.2649 \| 0.2318 \| 0.0199 \|
	\| 0.0178 \| 13.16 \| 500 \| 0.7960 \| 0.7649 \| 0.4735 \| 0.2914 \| 0.2053 \| 0.0298 \|
	\| 0.016 \| 13.68 \| 520 \| 0.8152 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.007 \| 14.21 \| 540 \| 0.8518 \| 0.6589 \| 0.4901 \| 0.1689 \| 0.3278 \| 0.0132 \|
	\| 0.0019 \| 14.74 \| 560 \| 0.8647 \| 0.7020 \| 0.4834 \| 0.2185 \| 0.2781 \| 0.0199 \|
	\| 0.0159 \| 15.26 \| 580 \| 0.8817 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.0016 \| 15.79 \| 600 \| 0.9105 \| 0.6689 \| 0.4834 \| 0.1854 \| 0.3113 \| 0.0199 \|
	\| 0.0079 \| 16.32 \| 620 \| 0.9311 \| 0.7053 \| 0.4834 \| 0.2219 \| 0.2748 \| 0.0199 \|
	\| 0.0045 \| 16.84 \| 640 \| 0.9586 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.0033 \| 17.37 \| 660 \| 0.9765 \| 0.7252 \| 0.4834 \| 0.2417 \| 0.2550 \| 0.0199 \|
	\| 0.0078 \| 17.89 \| 680 \| 1.0263 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.0047 \| 18.42 \| 700 \| 0.9929 \| 0.7351 \| 0.4768 \| 0.2583 \| 0.2384 \| 0.0265 \|
	\| 0.0082 \| 18.95 \| 720 \| 1.0001 \| 0.7185 \| 0.4801 \| 0.2384 \| 0.2583 \| 0.0232 \|
	\| 0.0022 \| 19.47 \| 740 \| 1.0150 \| 0.7086 \| 0.4801 \| 0.2285 \| 0.2682 \| 0.0232 \|
	\| 0.0027 \| 20.0 \| 760 \| 1.0638 \| 0.6887 \| 0.4901 \| 0.1987 \| 0.2980 \| 0.0132 \|
	\| 0.0025 \| 20.53 \| 780 \| 1.0124 \| 0.7020 \| 0.4834 \| 0.2185 \| 0.2781 \| 0.0199 \|
	\| 0.007 \| 21.05 \| 800 \| 1.0082 \| 0.6987 \| 0.4834 \| 0.2152 \| 0.2815 \| 0.0199 \|
	\| 0.0119 \| 21.58 \| 820 \| 1.0225 \| 0.7119 \| 0.4801 \| 0.2318 \| 0.2649 \| 0.0232 \|
	\| 0.0016 \| 22.11 \| 840 \| 1.0494 \| 0.7053 \| 0.4901 \| 0.2152 \| 0.2815 \| 0.0132 \|
	\| 0.0007 \| 22.63 \| 860 \| 1.0515 \| 0.7152 \| 0.4868 \| 0.2285 \| 0.2682 \| 0.0166 \|
	\| 0.0014 \| 23.16 \| 880 \| 1.0492 \| 0.7119 \| 0.4868 \| 0.2252 \| 0.2715 \| 0.0166 \|
	\| 0.002 \| 23.68 \| 900 \| 1.0970 \| 0.7020 \| 0.4934 \| 0.2086 \| 0.2881 \| 0.0099 \|
	\| 0.0003 \| 24.21 \| 920 \| 1.0429 \| 0.7185 \| 0.4834 \| 0.2351 \| 0.2616 \| 0.0199 \|
	\| 0.0002 \| 24.74 \| 940 \| 1.0772 \| 0.7053 \| 0.4868 \| 0.2185 \| 0.2781 \| 0.0166 \|
	\| 0.0008 \| 25.26 \| 960 \| 1.0766 \| 0.7119 \| 0.4934 \| 0.2185 \| 0.2781 \| 0.0099 \|
	\| 0.001 \| 25.79 \| 980 \| 1.0720 \| 0.7185 \| 0.4934 \| 0.2252 \| 0.2715 \| 0.0099 \|
	\| 0.0002 \| 26.32 \| 1000 \| 1.0763 \| 0.7152 \| 0.4901 \| 0.2252 \| 0.2715 \| 0.0132 \|
	\| 0.0002 \| 26.84 \| 1020 \| 1.0675 \| 0.7185 \| 0.4901 \| 0.2285 \| 0.2682 \| 0.0132 \|
	\| 0.0011 \| 27.37 \| 1040 \| 1.0745 \| 0.7185 \| 0.4834 \| 0.2351 \| 0.2616 \| 0.0199 \|
	\| 0.0007 \| 27.89 \| 1060 \| 1.0792 \| 0.7185 \| 0.4901 \| 0.2285 \| 0.2682 \| 0.0132 \|
	\| 0.0007 \| 28.42 \| 1080 \| 1.0880 \| 0.7152 \| 0.4934 \| 0.2219 \| 0.2748 \| 0.0099 \|
	\| 0.0005 \| 28.95 \| 1100 \| 1.0903 \| 0.7185 \| 0.4934 \| 0.2252 \| 0.2715 \| 0.0099 \|
	\| 0.0025 \| 29.47 \| 1120 \| 1.0877 \| 0.7185 \| 0.4901 \| 0.2285 \| 0.2682 \| 0.0132 \|
	\| 0.0004 \| 30.0 \| 1140 \| 1.0876 \| 0.7219 \| 0.4901 \| 0.2318 \| 0.2649 \| 0.0132 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.13.1
	- Datasets 2.10.1
	- Tokenizers 0.13.2

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	datasets:
	- crows_pairs
	metrics:
	- accuracy
	model-index:
	- name: t5-small_crows_pairs_finetuned
	results:
	- task:
	name: Sequence-to-sequence Language Modeling
	type: text2text-generation
	dataset:
	name: crows_pairs
	type: crows_pairs
	config: crows_pairs
	split: test
	args: crows_pairs
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.7218543046357616
	---

	<!-- 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. -->

	# t5-small_crows_pairs_finetuned

	This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the crows_pairs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.0876
	- Accuracy: 0.7219
	- Tp: 0.4901
	- Tn: 0.2318
	- Fp: 0.2649
	- Fn: 0.0132

	## 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: 0.0005
	- train_batch_size: 32
	- eval_batch_size: 32
	- 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 \| Tp \| Tn \| Fp \| Fn \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|
	\| 0.4776 \| 0.53 \| 20 \| 0.3559 \| 0.5033 \| 0.5033 \| 0.0 \| 0.4967 \| 0.0 \|
	\| 0.3708 \| 1.05 \| 40 \| 0.3431 \| 0.5265 \| 0.5033 \| 0.0232 \| 0.4735 \| 0.0 \|
	\| 0.3905 \| 1.58 \| 60 \| 0.3414 \| 0.6325 \| 0.5033 \| 0.1291 \| 0.3675 \| 0.0 \|
	\| 0.3526 \| 2.11 \| 80 \| 0.3260 \| 0.5464 \| 0.5033 \| 0.0430 \| 0.4536 \| 0.0 \|
	\| 0.2734 \| 2.63 \| 100 \| 0.3047 \| 0.8179 \| 0.4934 \| 0.3245 \| 0.1722 \| 0.0099 \|
	\| 0.2429 \| 3.16 \| 120 \| 0.2631 \| 0.6623 \| 0.5033 \| 0.1589 \| 0.3377 \| 0.0 \|
	\| 0.2161 \| 3.68 \| 140 \| 0.2829 \| 0.7152 \| 0.4901 \| 0.2252 \| 0.2715 \| 0.0132 \|
	\| 0.1617 \| 4.21 \| 160 \| 0.2947 \| 0.7748 \| 0.4901 \| 0.2848 \| 0.2119 \| 0.0132 \|
	\| 0.1776 \| 4.74 \| 180 \| 0.3258 \| 0.7384 \| 0.4901 \| 0.2483 \| 0.2483 \| 0.0132 \|
	\| 0.1113 \| 5.26 \| 200 \| 0.3362 \| 0.7815 \| 0.4901 \| 0.2914 \| 0.2053 \| 0.0132 \|
	\| 0.1008 \| 5.79 \| 220 \| 0.4016 \| 0.7748 \| 0.4868 \| 0.2881 \| 0.2086 \| 0.0166 \|
	\| 0.0572 \| 6.32 \| 240 \| 0.4486 \| 0.7219 \| 0.4901 \| 0.2318 \| 0.2649 \| 0.0132 \|
	\| 0.0701 \| 6.84 \| 260 \| 0.4561 \| 0.7384 \| 0.4901 \| 0.2483 \| 0.2483 \| 0.0132 \|
	\| 0.051 \| 7.37 \| 280 \| 0.4813 \| 0.7815 \| 0.4868 \| 0.2947 \| 0.2020 \| 0.0166 \|
	\| 0.0408 \| 7.89 \| 300 \| 0.5279 \| 0.7815 \| 0.4868 \| 0.2947 \| 0.2020 \| 0.0166 \|
	\| 0.0326 \| 8.42 \| 320 \| 0.6454 \| 0.7550 \| 0.4934 \| 0.2616 \| 0.2351 \| 0.0099 \|
	\| 0.0361 \| 8.95 \| 340 \| 0.7559 \| 0.7417 \| 0.4901 \| 0.2517 \| 0.2450 \| 0.0132 \|
	\| 0.0304 \| 9.47 \| 360 \| 0.7422 \| 0.7318 \| 0.4868 \| 0.2450 \| 0.2517 \| 0.0166 \|
	\| 0.0299 \| 10.0 \| 380 \| 0.7770 \| 0.7450 \| 0.4868 \| 0.2583 \| 0.2384 \| 0.0166 \|
	\| 0.0227 \| 10.53 \| 400 \| 0.7033 \| 0.7947 \| 0.4801 \| 0.3146 \| 0.1821 \| 0.0232 \|
	\| 0.017 \| 11.05 \| 420 \| 0.7220 \| 0.7649 \| 0.4801 \| 0.2848 \| 0.2119 \| 0.0232 \|
	\| 0.0166 \| 11.58 \| 440 \| 0.7674 \| 0.7649 \| 0.4636 \| 0.3013 \| 0.1954 \| 0.0397 \|
	\| 0.0123 \| 12.11 \| 460 \| 0.8153 \| 0.7616 \| 0.4834 \| 0.2781 \| 0.2185 \| 0.0199 \|
	\| 0.0084 \| 12.63 \| 480 \| 0.8422 \| 0.7483 \| 0.4834 \| 0.2649 \| 0.2318 \| 0.0199 \|
	\| 0.0178 \| 13.16 \| 500 \| 0.7960 \| 0.7649 \| 0.4735 \| 0.2914 \| 0.2053 \| 0.0298 \|
	\| 0.016 \| 13.68 \| 520 \| 0.8152 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.007 \| 14.21 \| 540 \| 0.8518 \| 0.6589 \| 0.4901 \| 0.1689 \| 0.3278 \| 0.0132 \|
	\| 0.0019 \| 14.74 \| 560 \| 0.8647 \| 0.7020 \| 0.4834 \| 0.2185 \| 0.2781 \| 0.0199 \|
	\| 0.0159 \| 15.26 \| 580 \| 0.8817 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.0016 \| 15.79 \| 600 \| 0.9105 \| 0.6689 \| 0.4834 \| 0.1854 \| 0.3113 \| 0.0199 \|
	\| 0.0079 \| 16.32 \| 620 \| 0.9311 \| 0.7053 \| 0.4834 \| 0.2219 \| 0.2748 \| 0.0199 \|
	\| 0.0045 \| 16.84 \| 640 \| 0.9586 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.0033 \| 17.37 \| 660 \| 0.9765 \| 0.7252 \| 0.4834 \| 0.2417 \| 0.2550 \| 0.0199 \|
	\| 0.0078 \| 17.89 \| 680 \| 1.0263 \| 0.7086 \| 0.4834 \| 0.2252 \| 0.2715 \| 0.0199 \|
	\| 0.0047 \| 18.42 \| 700 \| 0.9929 \| 0.7351 \| 0.4768 \| 0.2583 \| 0.2384 \| 0.0265 \|
	\| 0.0082 \| 18.95 \| 720 \| 1.0001 \| 0.7185 \| 0.4801 \| 0.2384 \| 0.2583 \| 0.0232 \|
	\| 0.0022 \| 19.47 \| 740 \| 1.0150 \| 0.7086 \| 0.4801 \| 0.2285 \| 0.2682 \| 0.0232 \|
	\| 0.0027 \| 20.0 \| 760 \| 1.0638 \| 0.6887 \| 0.4901 \| 0.1987 \| 0.2980 \| 0.0132 \|
	\| 0.0025 \| 20.53 \| 780 \| 1.0124 \| 0.7020 \| 0.4834 \| 0.2185 \| 0.2781 \| 0.0199 \|
	\| 0.007 \| 21.05 \| 800 \| 1.0082 \| 0.6987 \| 0.4834 \| 0.2152 \| 0.2815 \| 0.0199 \|
	\| 0.0119 \| 21.58 \| 820 \| 1.0225 \| 0.7119 \| 0.4801 \| 0.2318 \| 0.2649 \| 0.0232 \|
	\| 0.0016 \| 22.11 \| 840 \| 1.0494 \| 0.7053 \| 0.4901 \| 0.2152 \| 0.2815 \| 0.0132 \|
	\| 0.0007 \| 22.63 \| 860 \| 1.0515 \| 0.7152 \| 0.4868 \| 0.2285 \| 0.2682 \| 0.0166 \|
	\| 0.0014 \| 23.16 \| 880 \| 1.0492 \| 0.7119 \| 0.4868 \| 0.2252 \| 0.2715 \| 0.0166 \|
	\| 0.002 \| 23.68 \| 900 \| 1.0970 \| 0.7020 \| 0.4934 \| 0.2086 \| 0.2881 \| 0.0099 \|
	\| 0.0003 \| 24.21 \| 920 \| 1.0429 \| 0.7185 \| 0.4834 \| 0.2351 \| 0.2616 \| 0.0199 \|
	\| 0.0002 \| 24.74 \| 940 \| 1.0772 \| 0.7053 \| 0.4868 \| 0.2185 \| 0.2781 \| 0.0166 \|
	\| 0.0008 \| 25.26 \| 960 \| 1.0766 \| 0.7119 \| 0.4934 \| 0.2185 \| 0.2781 \| 0.0099 \|
	\| 0.001 \| 25.79 \| 980 \| 1.0720 \| 0.7185 \| 0.4934 \| 0.2252 \| 0.2715 \| 0.0099 \|
	\| 0.0002 \| 26.32 \| 1000 \| 1.0763 \| 0.7152 \| 0.4901 \| 0.2252 \| 0.2715 \| 0.0132 \|
	\| 0.0002 \| 26.84 \| 1020 \| 1.0675 \| 0.7185 \| 0.4901 \| 0.2285 \| 0.2682 \| 0.0132 \|
	\| 0.0011 \| 27.37 \| 1040 \| 1.0745 \| 0.7185 \| 0.4834 \| 0.2351 \| 0.2616 \| 0.0199 \|
	\| 0.0007 \| 27.89 \| 1060 \| 1.0792 \| 0.7185 \| 0.4901 \| 0.2285 \| 0.2682 \| 0.0132 \|
	\| 0.0007 \| 28.42 \| 1080 \| 1.0880 \| 0.7152 \| 0.4934 \| 0.2219 \| 0.2748 \| 0.0099 \|
	\| 0.0005 \| 28.95 \| 1100 \| 1.0903 \| 0.7185 \| 0.4934 \| 0.2252 \| 0.2715 \| 0.0099 \|
	\| 0.0025 \| 29.47 \| 1120 \| 1.0877 \| 0.7185 \| 0.4901 \| 0.2285 \| 0.2682 \| 0.0132 \|
	\| 0.0004 \| 30.0 \| 1140 \| 1.0876 \| 0.7219 \| 0.4901 \| 0.2318 \| 0.2649 \| 0.0132 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.13.1
	- Datasets 2.10.1
	- Tokenizers 0.13.2