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layoutlmv3-finetuned-invoice

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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+ - generated
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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: generated
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+ type: generated
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+ config: sroie
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+ split: test
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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 generated dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0014
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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: 5000
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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 | 0.0766 | 0.97 | 0.9838 | 0.9768 | 0.9968 |
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+ | No log | 4.0 | 200 | 0.0214 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | No log | 6.0 | 300 | 0.0157 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | No log | 8.0 | 400 | 0.0142 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | 0.1264 | 10.0 | 500 | 0.0129 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | 0.1264 | 12.0 | 600 | 0.0118 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
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+ | 0.1264 | 14.0 | 700 | 0.0038 | 0.9980 | 0.9959 | 0.9970 | 0.9996 |
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+ | 0.1264 | 16.0 | 800 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1264 | 18.0 | 900 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0064 | 20.0 | 1000 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0064 | 22.0 | 1100 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0064 | 24.0 | 1200 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0064 | 26.0 | 1300 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0064 | 28.0 | 1400 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0018 | 30.0 | 1500 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0018 | 32.0 | 1600 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0018 | 34.0 | 1700 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0018 | 36.0 | 1800 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0018 | 38.0 | 1900 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 40.0 | 2000 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 42.0 | 2100 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 44.0 | 2200 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 46.0 | 2300 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0011 | 48.0 | 2400 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0008 | 50.0 | 2500 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0008 | 52.0 | 2600 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0008 | 54.0 | 2700 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0008 | 56.0 | 2800 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0008 | 58.0 | 2900 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0006 | 60.0 | 3000 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0006 | 62.0 | 3100 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0006 | 64.0 | 3200 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0006 | 66.0 | 3300 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0006 | 68.0 | 3400 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 70.0 | 3500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 72.0 | 3600 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 74.0 | 3700 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 76.0 | 3800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0005 | 78.0 | 3900 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0004 | 80.0 | 4000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0004 | 82.0 | 4100 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0004 | 84.0 | 4200 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0004 | 86.0 | 4300 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0004 | 88.0 | 4400 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0003 | 90.0 | 4500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0003 | 92.0 | 4600 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0003 | 94.0 | 4700 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0003 | 96.0 | 4800 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0003 | 98.0 | 4900 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0003 | 100.0 | 5000 | 0.0002 | 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 4.32.0.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