layoutlmv3-cordv2-binary

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-cordv2-binary
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# layoutlmv3-cordv2-binary
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0490
+- Precision: 0.9529
+- Recall: 0.9564
+- F1: 0.9546
+- Accuracy: 0.9941
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1500
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.3333 | 100  | 0.0970          | 0.7517    | 0.8145 | 0.7818 | 0.9788   |
+| No log        | 0.6667 | 200  | 0.0520          | 0.8715    | 0.9127 | 0.8917 | 0.9894   |
+| No log        | 1.0    | 300  | 0.0630          | 0.9143    | 0.9309 | 0.9225 | 0.9919   |
+| No log        | 1.3333 | 400  | 0.0459          | 0.925     | 0.9418 | 0.9333 | 0.9936   |
+| 0.0764        | 1.6667 | 500  | 0.0540          | 0.9457    | 0.9491 | 0.9474 | 0.9936   |
+| 0.0764        | 2.0    | 600  | 0.0395          | 0.9393    | 0.9564 | 0.9477 | 0.9945   |
+| 0.0764        | 2.3333 | 700  | 0.0455          | 0.9457    | 0.9491 | 0.9474 | 0.9945   |
+| 0.0764        | 2.6667 | 800  | 0.0490          | 0.9562    | 0.9527 | 0.9545 | 0.9941   |
+| 0.0764        | 3.0    | 900  | 0.0422          | 0.9395    | 0.96   | 0.9496 | 0.9958   |
+| 0.02          | 3.3333 | 1000 | 0.0524          | 0.9529    | 0.9564 | 0.9546 | 0.9941   |
+| 0.02          | 3.6667 | 1100 | 0.0466          | 0.9529    | 0.9564 | 0.9546 | 0.9941   |
+| 0.02          | 4.0    | 1200 | 0.0482          | 0.9568    | 0.9673 | 0.9620 | 0.9953   |
+| 0.02          | 4.3333 | 1300 | 0.0444          | 0.9529    | 0.9564 | 0.9546 | 0.9941   |
+| 0.02          | 4.6667 | 1400 | 0.0493          | 0.9529    | 0.9564 | 0.9546 | 0.9941   |
+| 0.0103        | 5.0    | 1500 | 0.0490          | 0.9529    | 0.9564 | 0.9546 | 0.9941   |
+
+
+### Framework versions
+
+- Transformers 4.43.0.dev0
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

model.safetensors

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