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Output_LayoutLMv3_v3

Token Classification
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layoutlmv3
Generated from Trainer
Inference Endpoints
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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-large
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+ tags:
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+ - generated_from_trainer
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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: Output_LayoutLMv3_v3
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+ results: []
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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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+ # Output_LayoutLMv3_v3
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1344
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+ - Precision: 0.7699
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+ - Recall: 0.8142
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+ - F1: 0.7914
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+ - Accuracy: 0.9695
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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: 3e-07
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+ - train_batch_size: 4
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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: 3000
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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 | 4.55 | 100 | 0.5786 | 0.0 | 0.0 | 0.0 | 0.8867 |
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+ | No log | 9.09 | 200 | 0.4032 | 0.0 | 0.0 | 0.0 | 0.8867 |
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+ | No log | 13.64 | 300 | 0.2908 | 0.4091 | 0.1593 | 0.2293 | 0.9067 |
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+ | No log | 18.18 | 400 | 0.2300 | 0.5858 | 0.4381 | 0.5013 | 0.9267 |
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+ | 0.5251 | 22.73 | 500 | 0.1981 | 0.685 | 0.6062 | 0.6432 | 0.9438 |
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+ | 0.5251 | 27.27 | 600 | 0.1790 | 0.7130 | 0.6814 | 0.6968 | 0.9505 |
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+ | 0.5251 | 31.82 | 700 | 0.1689 | 0.7249 | 0.7345 | 0.7297 | 0.9581 |
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+ | 0.5251 | 36.36 | 800 | 0.1593 | 0.7478 | 0.7478 | 0.7478 | 0.9619 |
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+ | 0.5251 | 40.91 | 900 | 0.1582 | 0.75 | 0.7832 | 0.7662 | 0.9638 |
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+ | 0.129 | 45.45 | 1000 | 0.1527 | 0.7306 | 0.7920 | 0.7601 | 0.9619 |
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+ | 0.129 | 50.0 | 1100 | 0.1470 | 0.7429 | 0.8053 | 0.7728 | 0.9638 |
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+ | 0.129 | 54.55 | 1200 | 0.1418 | 0.7552 | 0.8053 | 0.7794 | 0.9657 |
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+ | 0.129 | 59.09 | 1300 | 0.1404 | 0.7657 | 0.8097 | 0.7871 | 0.9667 |
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+ | 0.129 | 63.64 | 1400 | 0.1368 | 0.7741 | 0.8186 | 0.7957 | 0.9695 |
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+ | 0.0799 | 68.18 | 1500 | 0.1316 | 0.7741 | 0.8186 | 0.7957 | 0.9705 |
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+ | 0.0799 | 72.73 | 1600 | 0.1301 | 0.7764 | 0.8142 | 0.7948 | 0.9705 |
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+ | 0.0799 | 77.27 | 1700 | 0.1326 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
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+ | 0.0799 | 81.82 | 1800 | 0.1357 | 0.7552 | 0.8053 | 0.7794 | 0.9676 |
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+ | 0.0799 | 86.36 | 1900 | 0.1304 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
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+ | 0.0561 | 90.91 | 2000 | 0.1326 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
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+ | 0.0561 | 95.45 | 2100 | 0.1340 | 0.7689 | 0.8097 | 0.7888 | 0.9695 |
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+ | 0.0561 | 100.0 | 2200 | 0.1371 | 0.7635 | 0.8142 | 0.7880 | 0.9686 |
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+ | 0.0561 | 104.55 | 2300 | 0.1337 | 0.7764 | 0.8142 | 0.7948 | 0.9705 |
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+ | 0.0561 | 109.09 | 2400 | 0.1310 | 0.7764 | 0.8142 | 0.7948 | 0.9705 |
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+ | 0.0451 | 113.64 | 2500 | 0.1353 | 0.7657 | 0.8097 | 0.7871 | 0.9686 |
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+ | 0.0451 | 118.18 | 2600 | 0.1357 | 0.7657 | 0.8097 | 0.7871 | 0.9686 |
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+ | 0.0451 | 122.73 | 2700 | 0.1361 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
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+ | 0.0451 | 127.27 | 2800 | 0.1358 | 0.7667 | 0.8142 | 0.7897 | 0.9686 |
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+ | 0.0451 | 131.82 | 2900 | 0.1347 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
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+ | 0.0414 | 136.36 | 3000 | 0.1344 | 0.7699 | 0.8142 | 0.7914 | 0.9695 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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