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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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- format_dataset |
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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: reciept-model-2500 |
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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: format_dataset |
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type: format_dataset |
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config: assesment dataset |
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split: test |
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args: assesment dataset |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9673366834170855 |
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- name: Recall |
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type: recall |
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value: 0.9625 |
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- name: F1 |
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type: f1 |
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value: 0.9649122807017544 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9993105033325672 |
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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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# reciept-model-2500 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the format_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0043 |
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- Precision: 0.9673 |
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- Recall: 0.9625 |
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- F1: 0.9649 |
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- Accuracy: 0.9993 |
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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: 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: 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 | 0.62 | 100 | 0.0150 | 0.8575 | 0.8725 | 0.8649 | 0.9972 | |
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| No log | 1.25 | 200 | 0.0075 | 0.8756 | 0.9325 | 0.9031 | 0.9979 | |
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| No log | 1.88 | 300 | 0.0154 | 0.8744 | 0.8875 | 0.8809 | 0.9973 | |
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| No log | 2.5 | 400 | 0.0118 | 0.8881 | 0.9525 | 0.9192 | 0.9982 | |
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| 0.0029 | 3.12 | 500 | 0.0091 | 0.9158 | 0.925 | 0.9204 | 0.9983 | |
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| 0.0029 | 3.75 | 600 | 0.0167 | 0.8720 | 0.9025 | 0.8870 | 0.9975 | |
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| 0.0029 | 4.38 | 700 | 0.0092 | 0.9183 | 0.9275 | 0.9229 | 0.9983 | |
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| 0.0029 | 5.0 | 800 | 0.0113 | 0.8843 | 0.9175 | 0.9006 | 0.9979 | |
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| 0.0029 | 5.62 | 900 | 0.0106 | 0.9349 | 0.8975 | 0.9158 | 0.9982 | |
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| 0.0017 | 6.25 | 1000 | 0.0043 | 0.9673 | 0.9625 | 0.9649 | 0.9993 | |
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| 0.0017 | 6.88 | 1100 | 0.0044 | 0.9602 | 0.965 | 0.9626 | 0.9993 | |
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| 0.0017 | 7.5 | 1200 | 0.0118 | 0.9246 | 0.92 | 0.9223 | 0.9982 | |
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| 0.0017 | 8.12 | 1300 | 0.0067 | 0.9406 | 0.95 | 0.9453 | 0.9988 | |
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| 0.0017 | 8.75 | 1400 | 0.0083 | 0.9409 | 0.955 | 0.9479 | 0.9989 | |
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| 0.001 | 9.38 | 1500 | 0.0060 | 0.9495 | 0.94 | 0.9447 | 0.9988 | |
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| 0.001 | 10.0 | 1600 | 0.0078 | 0.9369 | 0.9275 | 0.9322 | 0.9985 | |
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| 0.001 | 10.62 | 1700 | 0.0093 | 0.9248 | 0.9525 | 0.9384 | 0.9986 | |
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| 0.001 | 11.25 | 1800 | 0.0097 | 0.9062 | 0.9425 | 0.9240 | 0.9983 | |
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| 0.001 | 11.88 | 1900 | 0.0100 | 0.9098 | 0.9325 | 0.9210 | 0.9982 | |
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| 0.0006 | 12.5 | 2000 | 0.0111 | 0.9113 | 0.925 | 0.9181 | 0.9981 | |
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| 0.0006 | 13.12 | 2100 | 0.0107 | 0.9275 | 0.9275 | 0.9275 | 0.9983 | |
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| 0.0006 | 13.75 | 2200 | 0.0105 | 0.9279 | 0.9325 | 0.9302 | 0.9984 | |
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| 0.0006 | 14.38 | 2300 | 0.0109 | 0.9325 | 0.9325 | 0.9325 | 0.9985 | |
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| 0.0006 | 15.0 | 2400 | 0.0109 | 0.9325 | 0.9325 | 0.9325 | 0.9985 | |
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| 0.0003 | 15.62 | 2500 | 0.0109 | 0.9325 | 0.9325 | 0.9325 | 0.9985 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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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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