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OCR-LayoutLMv3-Invoice

Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv3
Generated from Trainer
Eval Results
Inference Endpoints
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End of training

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  1. README.md +148 -0
  2. logs/events.out.tfevents.1667783647.132-145-141-187.64914.0 +2 -2
  3. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - wild_receipt
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: OCR-LayoutLMv3-Invoice
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: wild_receipt
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+ type: wild_receipt
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+ config: WildReceipt
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+ split: train
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+ args: WildReceipt
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8765398302764851
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+ - name: Recall
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+ type: recall
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+ value: 0.8812439796339617
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+ - name: F1
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+ type: f1
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+ value: 0.8788856103753516
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.92678512668641
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # OCR-LayoutLMv3-Invoice
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3159
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+ - Precision: 0.8765
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+ - Recall: 0.8812
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+ - F1: 0.8789
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+ - Accuracy: 0.9268
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - training_steps: 6000
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.16 | 100 | 1.5032 | 0.4934 | 0.1444 | 0.2234 | 0.6064 |
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+ | No log | 0.32 | 200 | 1.0282 | 0.5884 | 0.4420 | 0.5048 | 0.7385 |
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+ | No log | 0.47 | 300 | 0.7856 | 0.7448 | 0.6205 | 0.6770 | 0.8133 |
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+ | No log | 0.63 | 400 | 0.6464 | 0.7736 | 0.6689 | 0.7174 | 0.8399 |
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+ | 1.1733 | 0.79 | 500 | 0.5672 | 0.7609 | 0.7303 | 0.7453 | 0.8557 |
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+ | 1.1733 | 0.95 | 600 | 0.5055 | 0.7658 | 0.7652 | 0.7655 | 0.8677 |
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+ | 1.1733 | 1.1 | 700 | 0.4735 | 0.7946 | 0.7848 | 0.7897 | 0.8784 |
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+ | 1.1733 | 1.26 | 800 | 0.4414 | 0.7962 | 0.7946 | 0.7954 | 0.8818 |
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+ | 1.1733 | 1.42 | 900 | 0.4094 | 0.8176 | 0.8064 | 0.8120 | 0.8894 |
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+ | 0.5047 | 1.58 | 1000 | 0.3971 | 0.8219 | 0.8248 | 0.8234 | 0.8961 |
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+ | 0.5047 | 1.74 | 1100 | 0.4082 | 0.7993 | 0.8362 | 0.8174 | 0.8927 |
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+ | 0.5047 | 1.89 | 1200 | 0.3797 | 0.8240 | 0.8317 | 0.8278 | 0.8962 |
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+ | 0.5047 | 2.05 | 1300 | 0.3597 | 0.8326 | 0.8331 | 0.8329 | 0.9020 |
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+ | 0.5047 | 2.21 | 1400 | 0.3544 | 0.8462 | 0.8283 | 0.8371 | 0.9020 |
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+ | 0.368 | 2.37 | 1500 | 0.3374 | 0.8428 | 0.8435 | 0.8432 | 0.9056 |
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+ | 0.368 | 2.52 | 1600 | 0.3364 | 0.8406 | 0.8522 | 0.8464 | 0.9089 |
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+ | 0.368 | 2.68 | 1700 | 0.3404 | 0.8467 | 0.8536 | 0.8501 | 0.9107 |
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+ | 0.368 | 2.84 | 1800 | 0.3319 | 0.8405 | 0.8501 | 0.8453 | 0.9090 |
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+ | 0.368 | 3.0 | 1900 | 0.3324 | 0.8584 | 0.8492 | 0.8538 | 0.9117 |
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+ | 0.2949 | 3.15 | 2000 | 0.3204 | 0.8691 | 0.8404 | 0.8545 | 0.9119 |
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+ | 0.2949 | 3.31 | 2100 | 0.3107 | 0.8599 | 0.8547 | 0.8573 | 0.9162 |
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+ | 0.2949 | 3.47 | 2200 | 0.3169 | 0.8680 | 0.8489 | 0.8584 | 0.9146 |
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+ | 0.2949 | 3.63 | 2300 | 0.3190 | 0.8683 | 0.8519 | 0.8600 | 0.9152 |
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+ | 0.2949 | 3.79 | 2400 | 0.2975 | 0.8631 | 0.8617 | 0.8624 | 0.9182 |
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+ | 0.2438 | 3.94 | 2500 | 0.3040 | 0.8566 | 0.8640 | 0.8603 | 0.9171 |
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+ | 0.2438 | 4.1 | 2600 | 0.3045 | 0.8585 | 0.8642 | 0.8613 | 0.9181 |
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+ | 0.2438 | 4.26 | 2700 | 0.3139 | 0.8498 | 0.8748 | 0.8621 | 0.9160 |
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+ | 0.2438 | 4.42 | 2800 | 0.2985 | 0.8642 | 0.8672 | 0.8657 | 0.9214 |
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+ | 0.2438 | 4.57 | 2900 | 0.3047 | 0.8688 | 0.8694 | 0.8691 | 0.9214 |
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+ | 0.2028 | 4.73 | 3000 | 0.2986 | 0.8686 | 0.8695 | 0.8691 | 0.9207 |
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+ | 0.2028 | 4.89 | 3100 | 0.3135 | 0.8628 | 0.8755 | 0.8691 | 0.9197 |
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+ | 0.2028 | 5.05 | 3200 | 0.2927 | 0.8656 | 0.8755 | 0.8705 | 0.9217 |
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+ | 0.2028 | 5.21 | 3300 | 0.2992 | 0.8724 | 0.8697 | 0.8711 | 0.9228 |
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+ | 0.2028 | 5.36 | 3400 | 0.2975 | 0.8831 | 0.8639 | 0.8734 | 0.9244 |
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+ | 0.1814 | 5.52 | 3500 | 0.2897 | 0.8736 | 0.8788 | 0.8762 | 0.9250 |
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+ | 0.1814 | 5.68 | 3600 | 0.3118 | 0.8674 | 0.8751 | 0.8712 | 0.9216 |
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+ | 0.1814 | 5.84 | 3700 | 0.2974 | 0.8735 | 0.8779 | 0.8757 | 0.9237 |
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+ | 0.1814 | 5.99 | 3800 | 0.2957 | 0.8696 | 0.8815 | 0.8755 | 0.9240 |
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+ | 0.1814 | 6.15 | 3900 | 0.3120 | 0.8698 | 0.8817 | 0.8757 | 0.9250 |
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+ | 0.1602 | 6.31 | 4000 | 0.3080 | 0.8715 | 0.8800 | 0.8757 | 0.9238 |
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+ | 0.1602 | 6.47 | 4100 | 0.3031 | 0.8767 | 0.8788 | 0.8777 | 0.9261 |
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+ | 0.1602 | 6.62 | 4200 | 0.3146 | 0.8699 | 0.8784 | 0.8741 | 0.9227 |
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+ | 0.1602 | 6.78 | 4300 | 0.3085 | 0.8717 | 0.8788 | 0.8752 | 0.9248 |
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+ | 0.1602 | 6.94 | 4400 | 0.3023 | 0.8749 | 0.8756 | 0.8752 | 0.9250 |
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+ | 0.1383 | 7.1 | 4500 | 0.3025 | 0.8860 | 0.8735 | 0.8797 | 0.9252 |
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+ | 0.1383 | 7.26 | 4600 | 0.3026 | 0.8775 | 0.8810 | 0.8792 | 0.9272 |
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+ | 0.1383 | 7.41 | 4700 | 0.3146 | 0.8715 | 0.8832 | 0.8773 | 0.9251 |
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+ | 0.1383 | 7.57 | 4800 | 0.3113 | 0.8769 | 0.8803 | 0.8786 | 0.9275 |
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+ | 0.1383 | 7.73 | 4900 | 0.3073 | 0.8797 | 0.8786 | 0.8792 | 0.9261 |
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+ | 0.1306 | 7.89 | 5000 | 0.3163 | 0.8714 | 0.8828 | 0.8770 | 0.9248 |
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+ | 0.1306 | 8.04 | 5100 | 0.3163 | 0.8753 | 0.8810 | 0.8781 | 0.9250 |
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+ | 0.1306 | 8.2 | 5200 | 0.3132 | 0.8743 | 0.8804 | 0.8773 | 0.9257 |
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+ | 0.1306 | 8.36 | 5300 | 0.3119 | 0.8735 | 0.8837 | 0.8786 | 0.9264 |
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+ | 0.1306 | 8.52 | 5400 | 0.3145 | 0.8826 | 0.8779 | 0.8802 | 0.9272 |
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+ | 0.1174 | 8.68 | 5500 | 0.3166 | 0.8776 | 0.8811 | 0.8794 | 0.9261 |
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+ | 0.1174 | 8.83 | 5600 | 0.3146 | 0.8776 | 0.8814 | 0.8795 | 0.9260 |
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+ | 0.1174 | 8.99 | 5700 | 0.3135 | 0.8763 | 0.8826 | 0.8795 | 0.9271 |
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+ | 0.1174 | 9.15 | 5800 | 0.3154 | 0.8794 | 0.8818 | 0.8806 | 0.9275 |
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+ | 0.1174 | 9.31 | 5900 | 0.3152 | 0.8788 | 0.8817 | 0.8802 | 0.9274 |
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+ | 0.11 | 9.46 | 6000 | 0.3159 | 0.8765 | 0.8812 | 0.8789 | 0.9268 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.25.0.dev0
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+ - Pytorch 1.12.1
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1
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