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	---
	license: cc-by-nc-sa-4.0
	tags:
	- generated_from_trainer
	datasets:
	- wild_receipt
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: OCR-LayoutLMv3-Invoice
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: wild_receipt
	type: wild_receipt
	config: WildReceipt
	split: train
	args: WildReceipt
	metrics:
	- name: Precision
	type: precision
	value: 0.8765398302764851
	- name: Recall
	type: recall
	value: 0.8812439796339617
	- name: F1
	type: f1
	value: 0.8788856103753516
	- name: Accuracy
	type: accuracy
	value: 0.92678512668641
	---

	<!-- 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. -->

	# OCR-LayoutLMv3-Invoice

	This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3159
	- Precision: 0.8765
	- Recall: 0.8812
	- F1: 0.8789
	- Accuracy: 0.9268

	## Model description

	More information needed


	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 1e-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
	- training_steps: 6000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.16 \| 100 \| 1.5032 \| 0.4934 \| 0.1444 \| 0.2234 \| 0.6064 \|
	\| No log \| 0.32 \| 200 \| 1.0282 \| 0.5884 \| 0.4420 \| 0.5048 \| 0.7385 \|
	\| No log \| 0.47 \| 300 \| 0.7856 \| 0.7448 \| 0.6205 \| 0.6770 \| 0.8133 \|
	\| No log \| 0.63 \| 400 \| 0.6464 \| 0.7736 \| 0.6689 \| 0.7174 \| 0.8399 \|
	\| 1.1733 \| 0.79 \| 500 \| 0.5672 \| 0.7609 \| 0.7303 \| 0.7453 \| 0.8557 \|
	\| 1.1733 \| 0.95 \| 600 \| 0.5055 \| 0.7658 \| 0.7652 \| 0.7655 \| 0.8677 \|
	\| 1.1733 \| 1.1 \| 700 \| 0.4735 \| 0.7946 \| 0.7848 \| 0.7897 \| 0.8784 \|
	\| 1.1733 \| 1.26 \| 800 \| 0.4414 \| 0.7962 \| 0.7946 \| 0.7954 \| 0.8818 \|
	\| 1.1733 \| 1.42 \| 900 \| 0.4094 \| 0.8176 \| 0.8064 \| 0.8120 \| 0.8894 \|
	\| 0.5047 \| 1.58 \| 1000 \| 0.3971 \| 0.8219 \| 0.8248 \| 0.8234 \| 0.8961 \|
	\| 0.5047 \| 1.74 \| 1100 \| 0.4082 \| 0.7993 \| 0.8362 \| 0.8174 \| 0.8927 \|
	\| 0.5047 \| 1.89 \| 1200 \| 0.3797 \| 0.8240 \| 0.8317 \| 0.8278 \| 0.8962 \|
	\| 0.5047 \| 2.05 \| 1300 \| 0.3597 \| 0.8326 \| 0.8331 \| 0.8329 \| 0.9020 \|
	\| 0.5047 \| 2.21 \| 1400 \| 0.3544 \| 0.8462 \| 0.8283 \| 0.8371 \| 0.9020 \|
	\| 0.368 \| 2.37 \| 1500 \| 0.3374 \| 0.8428 \| 0.8435 \| 0.8432 \| 0.9056 \|
	\| 0.368 \| 2.52 \| 1600 \| 0.3364 \| 0.8406 \| 0.8522 \| 0.8464 \| 0.9089 \|
	\| 0.368 \| 2.68 \| 1700 \| 0.3404 \| 0.8467 \| 0.8536 \| 0.8501 \| 0.9107 \|
	\| 0.368 \| 2.84 \| 1800 \| 0.3319 \| 0.8405 \| 0.8501 \| 0.8453 \| 0.9090 \|
	\| 0.368 \| 3.0 \| 1900 \| 0.3324 \| 0.8584 \| 0.8492 \| 0.8538 \| 0.9117 \|
	\| 0.2949 \| 3.15 \| 2000 \| 0.3204 \| 0.8691 \| 0.8404 \| 0.8545 \| 0.9119 \|
	\| 0.2949 \| 3.31 \| 2100 \| 0.3107 \| 0.8599 \| 0.8547 \| 0.8573 \| 0.9162 \|
	\| 0.2949 \| 3.47 \| 2200 \| 0.3169 \| 0.8680 \| 0.8489 \| 0.8584 \| 0.9146 \|
	\| 0.2949 \| 3.63 \| 2300 \| 0.3190 \| 0.8683 \| 0.8519 \| 0.8600 \| 0.9152 \|
	\| 0.2949 \| 3.79 \| 2400 \| 0.2975 \| 0.8631 \| 0.8617 \| 0.8624 \| 0.9182 \|
	\| 0.2438 \| 3.94 \| 2500 \| 0.3040 \| 0.8566 \| 0.8640 \| 0.8603 \| 0.9171 \|
	\| 0.2438 \| 4.1 \| 2600 \| 0.3045 \| 0.8585 \| 0.8642 \| 0.8613 \| 0.9181 \|
	\| 0.2438 \| 4.26 \| 2700 \| 0.3139 \| 0.8498 \| 0.8748 \| 0.8621 \| 0.9160 \|
	\| 0.2438 \| 4.42 \| 2800 \| 0.2985 \| 0.8642 \| 0.8672 \| 0.8657 \| 0.9214 \|
	\| 0.2438 \| 4.57 \| 2900 \| 0.3047 \| 0.8688 \| 0.8694 \| 0.8691 \| 0.9214 \|
	\| 0.2028 \| 4.73 \| 3000 \| 0.2986 \| 0.8686 \| 0.8695 \| 0.8691 \| 0.9207 \|
	\| 0.2028 \| 4.89 \| 3100 \| 0.3135 \| 0.8628 \| 0.8755 \| 0.8691 \| 0.9197 \|
	\| 0.2028 \| 5.05 \| 3200 \| 0.2927 \| 0.8656 \| 0.8755 \| 0.8705 \| 0.9217 \|
	\| 0.2028 \| 5.21 \| 3300 \| 0.2992 \| 0.8724 \| 0.8697 \| 0.8711 \| 0.9228 \|
	\| 0.2028 \| 5.36 \| 3400 \| 0.2975 \| 0.8831 \| 0.8639 \| 0.8734 \| 0.9244 \|
	\| 0.1814 \| 5.52 \| 3500 \| 0.2897 \| 0.8736 \| 0.8788 \| 0.8762 \| 0.9250 \|
	\| 0.1814 \| 5.68 \| 3600 \| 0.3118 \| 0.8674 \| 0.8751 \| 0.8712 \| 0.9216 \|
	\| 0.1814 \| 5.84 \| 3700 \| 0.2974 \| 0.8735 \| 0.8779 \| 0.8757 \| 0.9237 \|
	\| 0.1814 \| 5.99 \| 3800 \| 0.2957 \| 0.8696 \| 0.8815 \| 0.8755 \| 0.9240 \|
	\| 0.1814 \| 6.15 \| 3900 \| 0.3120 \| 0.8698 \| 0.8817 \| 0.8757 \| 0.9250 \|
	\| 0.1602 \| 6.31 \| 4000 \| 0.3080 \| 0.8715 \| 0.8800 \| 0.8757 \| 0.9238 \|
	\| 0.1602 \| 6.47 \| 4100 \| 0.3031 \| 0.8767 \| 0.8788 \| 0.8777 \| 0.9261 \|
	\| 0.1602 \| 6.62 \| 4200 \| 0.3146 \| 0.8699 \| 0.8784 \| 0.8741 \| 0.9227 \|
	\| 0.1602 \| 6.78 \| 4300 \| 0.3085 \| 0.8717 \| 0.8788 \| 0.8752 \| 0.9248 \|
	\| 0.1602 \| 6.94 \| 4400 \| 0.3023 \| 0.8749 \| 0.8756 \| 0.8752 \| 0.9250 \|
	\| 0.1383 \| 7.1 \| 4500 \| 0.3025 \| 0.8860 \| 0.8735 \| 0.8797 \| 0.9252 \|
	\| 0.1383 \| 7.26 \| 4600 \| 0.3026 \| 0.8775 \| 0.8810 \| 0.8792 \| 0.9272 \|
	\| 0.1383 \| 7.41 \| 4700 \| 0.3146 \| 0.8715 \| 0.8832 \| 0.8773 \| 0.9251 \|
	\| 0.1383 \| 7.57 \| 4800 \| 0.3113 \| 0.8769 \| 0.8803 \| 0.8786 \| 0.9275 \|
	\| 0.1383 \| 7.73 \| 4900 \| 0.3073 \| 0.8797 \| 0.8786 \| 0.8792 \| 0.9261 \|
	\| 0.1306 \| 7.89 \| 5000 \| 0.3163 \| 0.8714 \| 0.8828 \| 0.8770 \| 0.9248 \|
	\| 0.1306 \| 8.04 \| 5100 \| 0.3163 \| 0.8753 \| 0.8810 \| 0.8781 \| 0.9250 \|
	\| 0.1306 \| 8.2 \| 5200 \| 0.3132 \| 0.8743 \| 0.8804 \| 0.8773 \| 0.9257 \|
	\| 0.1306 \| 8.36 \| 5300 \| 0.3119 \| 0.8735 \| 0.8837 \| 0.8786 \| 0.9264 \|
	\| 0.1306 \| 8.52 \| 5400 \| 0.3145 \| 0.8826 \| 0.8779 \| 0.8802 \| 0.9272 \|
	\| 0.1174 \| 8.68 \| 5500 \| 0.3166 \| 0.8776 \| 0.8811 \| 0.8794 \| 0.9261 \|
	\| 0.1174 \| 8.83 \| 5600 \| 0.3146 \| 0.8776 \| 0.8814 \| 0.8795 \| 0.9260 \|
	\| 0.1174 \| 8.99 \| 5700 \| 0.3135 \| 0.8763 \| 0.8826 \| 0.8795 \| 0.9271 \|
	\| 0.1174 \| 9.15 \| 5800 \| 0.3154 \| 0.8794 \| 0.8818 \| 0.8806 \| 0.9275 \|
	\| 0.1174 \| 9.31 \| 5900 \| 0.3152 \| 0.8788 \| 0.8817 \| 0.8802 \| 0.9274 \|
	\| 0.11 \| 9.46 \| 6000 \| 0.3159 \| 0.8765 \| 0.8812 \| 0.8789 \| 0.9268 \|


	### Framework versions

	- Transformers 4.25.0.dev0
	- Pytorch 1.12.1
	- Datasets 2.6.1
	- Tokenizers 0.13.1