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layoutlmv3-base-fine_tuned-FUNSD_dataset

Token Classification
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PyTorch
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layoutlmv3
Generated from Trainer
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LayoutLMv3
Eval Results
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README.md ADDED
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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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+ - funsd-layoutlmv3
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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-base-fine_tuned-FUNSD_dataset
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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: funsd-layoutlmv3
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+ type: funsd-layoutlmv3
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+ config: funsd
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+ split: test
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+ args: funsd
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8978890525282278
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+ - name: Recall
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+ type: recall
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+ value: 0.9085941381023348
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+ - name: F1
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+ type: f1
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+ value: 0.9032098765432099
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8461904195887318
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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-base-fine_tuned-FUNSD_dataset
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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 funsd-layoutlmv3 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2956
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+ - Precision: 0.8979
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+ - Recall: 0.9086
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+ - F1: 0.9032
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+ - Accuracy: 0.8462
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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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+ | 0.2149 | 1.33 | 100 | 0.2402 | 0.7469 | 0.8212 | 0.7823 | 0.7758 |
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+ | 0.1466 | 2.67 | 200 | 0.1869 | 0.8161 | 0.8838 | 0.8486 | 0.8273 |
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+ | 0.1122 | 4.0 | 300 | 0.1902 | 0.8538 | 0.8997 | 0.8761 | 0.8316 |
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+ | 0.0757 | 5.33 | 400 | 0.1857 | 0.8354 | 0.8927 | 0.8631 | 0.8349 |
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+ | 0.0427 | 6.67 | 500 | 0.2091 | 0.8792 | 0.8897 | 0.8844 | 0.8446 |
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+ | 0.0495 | 8.0 | 600 | 0.2235 | 0.8825 | 0.9031 | 0.8927 | 0.8370 |
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+ | 0.0369 | 9.33 | 700 | 0.2532 | 0.8826 | 0.9146 | 0.8983 | 0.8349 |
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+ | 0.0329 | 10.67 | 800 | 0.2576 | 0.8829 | 0.8992 | 0.8910 | 0.8474 |
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+ | 0.0229 | 12.0 | 900 | 0.2579 | 0.8827 | 0.8937 | 0.8882 | 0.8443 |
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+ | 0.0219 | 13.33 | 1000 | 0.2710 | 0.8710 | 0.8987 | 0.8846 | 0.8347 |
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+ | 0.0191 | 14.67 | 1100 | 0.2582 | 0.8889 | 0.9061 | 0.8974 | 0.8454 |
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+ | 0.0179 | 16.0 | 1200 | 0.2646 | 0.8870 | 0.9006 | 0.8938 | 0.8356 |
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+ | 0.0135 | 17.33 | 1300 | 0.2798 | 0.8949 | 0.9180 | 0.9063 | 0.8512 |
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+ | 0.007 | 18.67 | 1400 | 0.2944 | 0.8988 | 0.9091 | 0.9039 | 0.8455 |
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+ | 0.0064 | 20.0 | 1500 | 0.2822 | 0.8938 | 0.9071 | 0.9004 | 0.8452 |
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+ | 0.0089 | 21.33 | 1600 | 0.3003 | 0.8941 | 0.9101 | 0.9020 | 0.8484 |
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+ | 0.0099 | 22.67 | 1700 | 0.3008 | 0.8942 | 0.9071 | 0.9006 | 0.8439 |
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+ | 0.0069 | 24.0 | 1800 | 0.2965 | 0.8942 | 0.9071 | 0.9006 | 0.8386 |
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+ | 0.0048 | 25.33 | 1900 | 0.2973 | 0.9027 | 0.9076 | 0.9051 | 0.8501 |
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+ | 0.0069 | 26.67 | 2000 | 0.2956 | 0.8979 | 0.9086 | 0.9032 | 0.8462 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.2
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+ - Pytorch 2.0.1
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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