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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv2-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cord |
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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: layoutlmv2-finetuned-cord |
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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 |
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type: cord |
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config: cord |
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split: validation |
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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: 0.9652945924132365 |
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- name: Recall |
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type: recall |
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value: 0.9676375404530745 |
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- name: F1 |
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type: f1 |
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value: 0.9664646464646465 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9702653247941445 |
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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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# overfitting issue |
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I use this colab: |
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https://colab.research.google.com/drive/1AXh3G3-VmbMWlwbSvesVIurzNlcezTce?usp=sharing |
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to Fine tuning LayoutLMv2ForTokenClassification on CORD dataset |
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here is the result: |
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https://huggingface.co/doc2txt/layoutlmv2-finetuned-cord |
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* F1: 0.9665 |
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and indeed the result are pretty amazing when running on the test set, |
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however when running on any other receipt (printed or pdf) the result are completely off |
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So from some reason the model is overfitting to the cord dataset, even though I use similar images for testing. |
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I don't think that there is a **Data leakage** unless the cord DS is not clean (which I assume it is clean) |
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What could be the reason for this? |
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Is it some inherent property of LayoutLM? |
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The LayoutLM models are somewhat old, and it seems deserted... |
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I don't have much experience so I would appreciate any info |
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Thanks |
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here is an example code of how to run this model on a specific img folder: |
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https://huggingface.co/doc2txt/layoutlmv2-finetuned-cord/blob/main/LayoutLMv2Main_cord2_gOcr_folder.py |
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# layoutlmv2-finetuned-cord |
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This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the cord dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2819 |
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- Precision: 0.9653 |
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- Recall: 0.9676 |
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- F1: 0.9665 |
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- Accuracy: 0.9703 |
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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: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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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 | 1.0 | 400 | 1.2752 | 0.8527 | 0.8382 | 0.8454 | 0.8481 | |
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| 1.9583 | 2.0 | 800 | 0.6372 | 0.8799 | 0.8948 | 0.8873 | 0.9021 | |
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| 0.7097 | 3.0 | 1200 | 0.4255 | 0.9241 | 0.9264 | 0.9253 | 0.9414 | |
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| 0.3845 | 4.0 | 1600 | 0.3021 | 0.9414 | 0.9482 | 0.9448 | 0.9611 | |
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| 0.2699 | 5.0 | 2000 | 0.2819 | 0.9653 | 0.9676 | 0.9665 | 0.9703 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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