End of training

README.md

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+---
+license: mit
+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: 1.4637
+- Answer: {'precision': 0.8445714285714285, 'recall': 0.9045287637698899, 'f1': 0.8735224586288417, 'number': 817}
+- Header: {'precision': 0.6271186440677966, 'recall': 0.6218487394957983, 'f1': 0.6244725738396625, 'number': 119}
+- Question: {'precision': 0.9180790960451978, 'recall': 0.9052924791086351, 'f1': 0.9116409537166901, 'number': 1077}
+- Overall Precision: 0.8701
+- Overall Recall: 0.8882
+- Overall F1: 0.8791
+- Overall Accuracy: 0.7969
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 500
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                   | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.017         | 10.53 | 200  | 1.4637          | {'precision': 0.8445714285714285, 'recall': 0.9045287637698899, 'f1': 0.8735224586288417, 'number': 817} | {'precision': 0.6271186440677966, 'recall': 0.6218487394957983, 'f1': 0.6244725738396625, 'number': 119} | {'precision': 0.9180790960451978, 'recall': 0.9052924791086351, 'f1': 0.9116409537166901, 'number': 1077} | 0.8701            | 0.8882         | 0.8791     | 0.7969           |
+| 0.0025        | 21.05 | 400  | 1.5607          | {'precision': 0.8456140350877193, 'recall': 0.8849449204406364, 'f1': 0.8648325358851674, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} | {'precision': 0.8882931188561215, 'recall': 0.9229340761374187, 'f1': 0.9052823315118397, 'number': 1077} | 0.8573            | 0.8867         | 0.8718     | 0.8017           |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2

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preprocessor_config.json

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special_tokens_map.json

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