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: 1.4801
+- Answer: {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817}
+- Header: {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119}
+- Question: {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077}
+- Overall Precision: 0.8720
+- Overall Recall: 0.8932
+- Overall F1: 0.8825
+- Overall Accuracy: 0.8040
+
+## 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 | Answer                                                                                                   | Header                                                                                                  | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0015        | 2.67  | 200  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0011        | 5.33  | 400  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0011        | 8.0   | 600  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0008        | 10.67 | 800  | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0011        | 13.33 | 1000 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0011        | 16.0  | 1200 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0017        | 18.67 | 1400 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0008        | 21.33 | 1600 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0008        | 24.0  | 1800 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0009        | 26.67 | 2000 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0012        | 29.33 | 2200 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+| 0.0009        | 32.0  | 2400 | 1.4801          | {'precision': 0.8607888631090487, 'recall': 0.9082007343941249, 'f1': 0.8838594401429422, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119} | {'precision': 0.8991899189918992, 'recall': 0.9275766016713092, 'f1': 0.9131627056672761, 'number': 1077} | 0.8720            | 0.8932         | 0.8825     | 0.8040           |
+
+
+### Framework versions
+
+- Transformers 4.30.0.dev0
+- Pytorch 1.8.0+cu101
+- Datasets 2.12.0
+- Tokenizers 0.13.3

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preprocessor_config.json

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+{
+  "apply_ocr": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+  "image_processor_type": "LayoutLMv3FeatureExtractor",
+  "image_std": [
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+  "ocr_lang": null,
+  "processor_class": "LayoutLMv3Processor",
+  "resample": 2,
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+  "size": {
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+    "width": 224
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+  "tesseract_config": ""
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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "mask_token": {
+    "content": "<mask>",
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+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
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+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
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+  "cls_token_box": [
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+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "errors": "replace",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "model_max_length": 512,
+  "only_label_first_subword": true,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv3Processor",
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+    "content": "</s>",
+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}