End of training

README.md

@@ -0,0 +1,77 @@
+---
+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: 1.7487
+- Answer: {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}
+- Header: {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}
+- Question: {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077}
+- Overall Precision: 0.8731
+- Overall Recall: 0.8952
+- Overall F1: 0.8840
+- Overall Accuracy: 0.7977
+
+## 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: 2500
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                   | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4352        | 10.53  | 200  | 0.9574          | {'precision': 0.8385167464114832, 'recall': 0.8580171358629131, 'f1': 0.8481548699334543, 'number': 817} | {'precision': 0.5673076923076923, 'recall': 0.4957983193277311, 'f1': 0.5291479820627802, 'number': 119} | {'precision': 0.8394534585824082, 'recall': 0.9127205199628597, 'f1': 0.8745551601423487, 'number': 1077} | 0.8257            | 0.8659         | 0.8453     | 0.7896           |
+| 0.0467        | 21.05  | 400  | 1.3446          | {'precision': 0.8343057176196033, 'recall': 0.8751529987760098, 'f1': 0.8542413381123058, 'number': 817} | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119} | {'precision': 0.8543859649122807, 'recall': 0.904363974001857, 'f1': 0.8786648624267027, 'number': 1077}  | 0.8337            | 0.8664         | 0.8497     | 0.7969           |
+| 0.0125        | 31.58  | 600  | 1.4274          | {'precision': 0.8556461001164144, 'recall': 0.8996328029375765, 'f1': 0.8770883054892601, 'number': 817} | {'precision': 0.568, 'recall': 0.5966386554621849, 'f1': 0.5819672131147541, 'number': 119}              | {'precision': 0.8916211293260473, 'recall': 0.9090064995357474, 'f1': 0.9002298850574713, 'number': 1077} | 0.8573            | 0.8867         | 0.8718     | 0.8010           |
+| 0.0071        | 42.11  | 800  | 1.4147          | {'precision': 0.865265760197775, 'recall': 0.8567931456548348, 'f1': 0.8610086100861009, 'number': 817}  | {'precision': 0.6888888888888889, 'recall': 0.5210084033613446, 'f1': 0.5933014354066986, 'number': 119} | {'precision': 0.8798206278026905, 'recall': 0.9108635097493036, 'f1': 0.8950729927007299, 'number': 1077} | 0.8654            | 0.8659         | 0.8657     | 0.8055           |
+| 0.0067        | 52.63  | 1000 | 1.5877          | {'precision': 0.8747016706443914, 'recall': 0.8971848225214198, 'f1': 0.8858006042296073, 'number': 817} | {'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}  | {'precision': 0.8936363636363637, 'recall': 0.9127205199628597, 'f1': 0.9030776297657327, 'number': 1077} | 0.8709            | 0.8847         | 0.8778     | 0.8080           |
+| 0.003         | 63.16  | 1200 | 1.5406          | {'precision': 0.875, 'recall': 0.8996328029375765, 'f1': 0.8871454435727218, 'number': 817}              | {'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119}  | {'precision': 0.8858695652173914, 'recall': 0.9080779944289693, 'f1': 0.8968363136176066, 'number': 1077} | 0.8649            | 0.8838         | 0.8742     | 0.8183           |
+| 0.0011        | 73.68  | 1400 | 1.6131          | {'precision': 0.8686987104337632, 'recall': 0.9069767441860465, 'f1': 0.8874251497005988, 'number': 817} | {'precision': 0.7011494252873564, 'recall': 0.5126050420168067, 'f1': 0.5922330097087377, 'number': 119} | {'precision': 0.8821966341895483, 'recall': 0.924791086350975, 'f1': 0.9029918404351769, 'number': 1077}  | 0.8690            | 0.8932         | 0.8809     | 0.8111           |
+| 0.0008        | 84.21  | 1600 | 1.7487          | {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}             | {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077} | 0.8731            | 0.8952         | 0.8840     | 0.7977           |
+| 0.0007        | 94.74  | 1800 | 1.8317          | {'precision': 0.8605990783410138, 'recall': 0.9143206854345165, 'f1': 0.886646884272997, 'number': 817}  | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.9026876737720111, 'recall': 0.904363974001857, 'f1': 0.9035250463821892, 'number': 1077}  | 0.8699            | 0.8867         | 0.8782     | 0.7934           |
+| 0.0005        | 105.26 | 2000 | 1.8600          | {'precision': 0.8669778296382731, 'recall': 0.9094247246022031, 'f1': 0.8876941457586618, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8701            | 0.8887         | 0.8793     | 0.7897           |
+| 0.0005        | 115.79 | 2200 | 1.7672          | {'precision': 0.8781362007168458, 'recall': 0.8996328029375765, 'f1': 0.8887545344619106, 'number': 817} | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119} | {'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077} | 0.8756            | 0.8882         | 0.8819     | 0.8074           |
+| 0.0002        | 126.32 | 2400 | 1.8044          | {'precision': 0.8604382929642446, 'recall': 0.9130966952264382, 'f1': 0.8859857482185273, 'number': 817} | {'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119} | {'precision': 0.9022140221402214, 'recall': 0.9080779944289693, 'f1': 0.9051365108745951, 'number': 1077} | 0.8721            | 0.8872         | 0.8796     | 0.8000           |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3

preprocessor_config.json

@@ -0,0 +1,26 @@
+{
+  "apply_ocr": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "LayoutLMv3FeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "ocr_lang": null,
+  "processor_class": "LayoutLMv3Processor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  },
+  "tesseract_config": ""
+}

pytorch_model.bin

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 size 520816717

special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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": {
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+    "lstrip": false,
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+  "errors": "replace",
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+  "model_max_length": 512,
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv3Processor",
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+}