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Theivaprakasham
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layoutlmv3-finetuned-invoice

Token Classification
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layoutlmv3
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - sroie
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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-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: sroie
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+ type: sroie
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+ args: sroie
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 1.0
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+ - name: Recall
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+ type: recall
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+ value: 1.0
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+ - name: F1
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+ type: f1
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+ value: 1.0
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+ - name: Accuracy
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+ type: accuracy
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+ value: 1.0
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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-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 sroie dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0012
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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+ - Accuracy: 1.0
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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: 2000
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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 | 2.0 | 100 | 0.0878 | 0.968 | 0.9817 | 0.9748 | 0.9966 |
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+ | No log | 4.0 | 200 | 0.0241 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | No log | 6.0 | 300 | 0.0186 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | No log | 8.0 | 400 | 0.0184 | 0.9854 | 0.9574 | 0.9712 | 0.9956 |
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+ | 0.1308 | 10.0 | 500 | 0.0121 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | 0.1308 | 12.0 | 600 | 0.0076 | 0.9939 | 0.9878 | 0.9908 | 0.9987 |
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+ | 0.1308 | 14.0 | 700 | 0.0047 | 1.0 | 0.9959 | 0.9980 | 0.9996 |
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+ | 0.1308 | 16.0 | 800 | 0.0036 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
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+ | 0.1308 | 18.0 | 900 | 0.0045 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
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+ | 0.0069 | 20.0 | 1000 | 0.0043 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
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+ | 0.0069 | 22.0 | 1100 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0069 | 24.0 | 1200 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0069 | 26.0 | 1300 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0069 | 28.0 | 1400 | 0.0013 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0026 | 30.0 | 1500 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0026 | 32.0 | 1600 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0026 | 34.0 | 1700 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0026 | 36.0 | 1800 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0026 | 38.0 | 1900 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.002 | 40.0 | 2000 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.20.0.dev0
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.2.2
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+ - Tokenizers 0.12.1