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update model card README.md
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README.md
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---
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license: cc-by-nc-sa-4.0
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tags:
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- generated_from_trainer
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datasets:
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- cord-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-finetuned-cord_100
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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: cord-layoutlmv3
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type: cord-layoutlmv3
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config: cord
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split: train
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args: cord
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metrics:
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- name: Precision
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type: precision
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value: 0.9472118959107807
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- name: Recall
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type: recall
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value: 0.9535928143712575
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- name: F1
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type: f1
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value: 0.9503916449086163
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- name: Accuracy
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type: accuracy
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value: 0.9562818336162988
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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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# layoutlmv3-finetuned-cord_100
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2152
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- Precision: 0.9472
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- Recall: 0.9536
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- F1: 0.9504
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- Accuracy: 0.9563
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 5
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- eval_batch_size: 5
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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: 2500
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.56 | 250 | 0.9909 | 0.7582 | 0.8099 | 0.7832 | 0.8128 |
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| 1.3653 | 3.12 | 500 | 0.5650 | 0.8392 | 0.8675 | 0.8531 | 0.8756 |
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| 1.3653 | 4.69 | 750 | 0.3851 | 0.8865 | 0.9177 | 0.9018 | 0.9181 |
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| 0.3744 | 6.25 | 1000 | 0.3104 | 0.9280 | 0.9364 | 0.9322 | 0.9380 |
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| 0.3744 | 7.81 | 1250 | 0.2778 | 0.9347 | 0.9424 | 0.9385 | 0.9440 |
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| 0.1955 | 9.38 | 1500 | 0.2316 | 0.9327 | 0.9446 | 0.9386 | 0.9440 |
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| 0.1955 | 10.94 | 1750 | 0.2461 | 0.9414 | 0.9491 | 0.9452 | 0.9533 |
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| 0.1349 | 12.5 | 2000 | 0.2316 | 0.9379 | 0.9491 | 0.9435 | 0.9478 |
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| 0.1349 | 14.06 | 2250 | 0.2227 | 0.9487 | 0.9551 | 0.9519 | 0.9533 |
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| 0.1024 | 15.62 | 2500 | 0.2152 | 0.9472 | 0.9536 | 0.9504 | 0.9563 |
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### Framework versions
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- Transformers 4.23.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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