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layoutlmv3-finetuned-wildreceipt

Token Classification
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layoutlmv3
Generated from Trainer
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ base_model: microsoft/layoutlmv3-base
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - wildreceipt
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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: layoutlmv3-finetuned-wildreceipt
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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: wildreceipt
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+ type: wildreceipt
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+ config: WildReceipt
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+ split: test
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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: 0.8791872597473915
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+ - name: Recall
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+ type: recall
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+ value: 0.8814865794907089
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+ - name: F1
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+ type: f1
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+ value: 0.8803354182418035
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9270261366132221
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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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+ # layoutlmv3-finetuned-wildreceipt
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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 wildreceipt dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3081
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+ - Precision: 0.8792
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+ - Recall: 0.8815
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+ - F1: 0.8803
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+ - Accuracy: 0.9270
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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: 4
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+ - eval_batch_size: 4
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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: 4000
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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 | 0.32 | 100 | 1.3430 | 0.5041 | 0.1959 | 0.2821 | 0.6414 |
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+ | No log | 0.63 | 200 | 0.8931 | 0.6739 | 0.5367 | 0.5975 | 0.7786 |
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+ | No log | 0.95 | 300 | 0.6793 | 0.7332 | 0.6410 | 0.6840 | 0.8273 |
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+ | No log | 1.26 | 400 | 0.5804 | 0.7659 | 0.7090 | 0.7364 | 0.8507 |
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+ | 1.0357 | 1.58 | 500 | 0.4876 | 0.7919 | 0.7551 | 0.7731 | 0.8723 |
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+ | 1.0357 | 1.89 | 600 | 0.4417 | 0.8009 | 0.7997 | 0.8003 | 0.8857 |
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+ | 1.0357 | 2.21 | 700 | 0.3937 | 0.8256 | 0.8200 | 0.8228 | 0.8973 |
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+ | 1.0357 | 2.52 | 800 | 0.3904 | 0.8143 | 0.8321 | 0.8231 | 0.8958 |
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+ | 1.0357 | 2.84 | 900 | 0.3638 | 0.8462 | 0.8211 | 0.8334 | 0.9010 |
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+ | 0.3989 | 3.15 | 1000 | 0.3586 | 0.8386 | 0.8447 | 0.8417 | 0.9055 |
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+ | 0.3989 | 3.47 | 1100 | 0.3227 | 0.8382 | 0.8564 | 0.8472 | 0.9104 |
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+ | 0.3989 | 3.79 | 1200 | 0.3120 | 0.8538 | 0.8522 | 0.8530 | 0.9119 |
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+ | 0.3989 | 4.1 | 1300 | 0.3283 | 0.8498 | 0.8559 | 0.8528 | 0.9117 |
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+ | 0.3989 | 4.42 | 1400 | 0.3084 | 0.8595 | 0.8606 | 0.8600 | 0.9165 |
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+ | 0.2727 | 4.73 | 1500 | 0.3026 | 0.8552 | 0.8666 | 0.8609 | 0.9159 |
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+ | 0.2727 | 5.05 | 1600 | 0.3052 | 0.8633 | 0.8537 | 0.8585 | 0.9165 |
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+ | 0.2727 | 5.36 | 1700 | 0.3052 | 0.8505 | 0.8747 | 0.8625 | 0.9165 |
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+ | 0.2727 | 5.68 | 1800 | 0.3040 | 0.8579 | 0.8690 | 0.8634 | 0.9164 |
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+ | 0.2727 | 5.99 | 1900 | 0.2926 | 0.8717 | 0.8696 | 0.8707 | 0.9205 |
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+ | 0.2059 | 6.31 | 2000 | 0.3004 | 0.8646 | 0.8753 | 0.8699 | 0.9207 |
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+ | 0.2059 | 6.62 | 2100 | 0.2973 | 0.8711 | 0.8742 | 0.8726 | 0.9215 |
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+ | 0.2059 | 6.94 | 2200 | 0.3010 | 0.8650 | 0.8761 | 0.8705 | 0.9214 |
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+ | 0.2059 | 7.26 | 2300 | 0.3028 | 0.8654 | 0.8760 | 0.8706 | 0.9214 |
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+ | 0.2059 | 7.57 | 2400 | 0.2956 | 0.8769 | 0.8769 | 0.8769 | 0.9260 |
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+ | 0.1617 | 7.89 | 2500 | 0.2871 | 0.8746 | 0.8778 | 0.8762 | 0.9266 |
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+ | 0.1617 | 8.2 | 2600 | 0.3092 | 0.8632 | 0.8797 | 0.8714 | 0.9226 |
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+ | 0.1617 | 8.52 | 2700 | 0.3042 | 0.8834 | 0.8738 | 0.8786 | 0.9265 |
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+ | 0.1617 | 8.83 | 2800 | 0.3092 | 0.8672 | 0.8793 | 0.8732 | 0.9224 |
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+ | 0.1617 | 9.15 | 2900 | 0.3014 | 0.8738 | 0.8841 | 0.8789 | 0.9256 |
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+ | 0.1359 | 9.46 | 3000 | 0.3038 | 0.8763 | 0.8760 | 0.8762 | 0.9249 |
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+ | 0.1359 | 9.78 | 3100 | 0.3087 | 0.8730 | 0.8797 | 0.8763 | 0.9241 |
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+ | 0.1359 | 10.09 | 3200 | 0.3021 | 0.8740 | 0.8812 | 0.8776 | 0.9251 |
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+ | 0.1359 | 10.41 | 3300 | 0.2975 | 0.8790 | 0.8836 | 0.8812 | 0.9268 |
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+ | 0.1359 | 10.73 | 3400 | 0.3121 | 0.8734 | 0.8809 | 0.8771 | 0.9254 |
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+ | 0.1192 | 11.04 | 3500 | 0.3111 | 0.8812 | 0.8794 | 0.8803 | 0.9260 |
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+ | 0.1192 | 11.36 | 3600 | 0.3101 | 0.8785 | 0.8790 | 0.8788 | 0.9261 |
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+ | 0.1192 | 11.67 | 3700 | 0.3082 | 0.8790 | 0.8829 | 0.8809 | 0.9275 |
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+ | 0.1192 | 11.99 | 3800 | 0.3081 | 0.8822 | 0.8830 | 0.8826 | 0.9276 |
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+ | 0.1192 | 12.3 | 3900 | 0.3100 | 0.8800 | 0.8809 | 0.8805 | 0.9269 |
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+ | 0.1065 | 12.62 | 4000 | 0.3081 | 0.8792 | 0.8815 | 0.8803 | 0.9270 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.0.dev0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.3
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+ - Tokenizers 0.13.3