End of training

README.md

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+---
+license: mit
+base_model: SCUT-DLVCLab/lilt-roberta-en-base
+tags:
+- generated_from_trainer
+datasets:
+- funsd-layoutlmv3
+model-index:
+- name: lilt-en-funsd
+  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. -->
+
+# lilt-en-funsd
+
+This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0001
+- Account Name.key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
+- Account Name.value: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
+- Account No.key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
+- Account No.value: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
+- Overall Precision: 1.0
+- Overall Recall: 1.0
+- Overall F1: 1.0
+- Overall Accuracy: 1.0
+
+## 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: 5e-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: 2500
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Account Name.key                                          | Account Name.value                                        | Account No.key                                            | Account No.value                                          | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0635        | 100.0  | 200  | 0.0001          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0002        | 200.0  | 400  | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0001        | 300.0  | 600  | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0001        | 400.0  | 800  | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0001        | 500.0  | 1000 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 600.0  | 1200 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 700.0  | 1400 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 800.0  | 1600 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 900.0  | 1800 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 1000.0 | 2000 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 1100.0 | 2200 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+| 0.0           | 1200.0 | 2400 | 0.0000          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1} | 1.0               | 1.0            | 1.0        | 1.0              |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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preprocessor_config.json

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+{
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+  "do_normalize": true,
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+  "do_resize": true,
+  "image_mean": [
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special_tokens_map.json

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tokenizer_config.json

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+}