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: 2.9565
+- Answer: {'precision': 0.8948004836759371, 'recall': 0.9057527539779682, 'f1': 0.9002433090024331, 'number': 817}
+- Header: {'precision': 0.6868686868686869, 'recall': 0.5714285714285714, 'f1': 0.6238532110091742, 'number': 119}
+- Question: {'precision': 0.8923212709620476, 'recall': 0.9387186629526463, 'f1': 0.9149321266968325, 'number': 1077}
+- Overall Precision: 0.8834
+- Overall Recall: 0.9036
+- Overall F1: 0.8934
+- Overall Accuracy: 0.8096
+
+## 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.409         | 10.53  | 200  | 0.8991          | {'precision': 0.8176855895196506, 'recall': 0.9167686658506732, 'f1': 0.8643969994229659, 'number': 817} | {'precision': 0.5094339622641509, 'recall': 0.453781512605042, 'f1': 0.48, 'number': 119}                 | {'precision': 0.891465677179963, 'recall': 0.8922934076137419, 'f1': 0.8918793503480278, 'number': 1077}  | 0.84              | 0.8763         | 0.8578     | 0.7897           |
+| 0.0485        | 21.05  | 400  | 1.1875          | {'precision': 0.8504566210045662, 'recall': 0.9118727050183598, 'f1': 0.8800945067926758, 'number': 817} | {'precision': 0.5691056910569106, 'recall': 0.5882352941176471, 'f1': 0.578512396694215, 'number': 119}   | {'precision': 0.8970315398886828, 'recall': 0.8978644382544104, 'f1': 0.897447795823666, 'number': 1077}  | 0.8580            | 0.8852         | 0.8714     | 0.7935           |
+| 0.0139        | 31.58  | 600  | 1.5032          | {'precision': 0.8455377574370709, 'recall': 0.9045287637698899, 'f1': 0.8740390301596689, 'number': 817} | {'precision': 0.6206896551724138, 'recall': 0.6050420168067226, 'f1': 0.6127659574468085, 'number': 119}  | {'precision': 0.9057142857142857, 'recall': 0.883008356545961, 'f1': 0.8942172073342736, 'number': 1077}  | 0.8637            | 0.8753         | 0.8695     | 0.7913           |
+| 0.0083        | 42.11  | 800  | 1.4968          | {'precision': 0.8316939890710382, 'recall': 0.9314565483476133, 'f1': 0.8787528868360277, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.47058823529411764, 'f1': 0.5410628019323671, 'number': 119} | {'precision': 0.8928909952606635, 'recall': 0.8746518105849582, 'f1': 0.8836772983114447, 'number': 1077} | 0.8547            | 0.8738         | 0.8642     | 0.8017           |
+| 0.0058        | 52.63  | 1000 | 1.7837          | {'precision': 0.8385300668151447, 'recall': 0.9216646266829865, 'f1': 0.8781341107871721, 'number': 817} | {'precision': 0.6138613861386139, 'recall': 0.5210084033613446, 'f1': 0.5636363636363637, 'number': 119}  | {'precision': 0.8972667295004713, 'recall': 0.8839368616527391, 'f1': 0.8905519176800748, 'number': 1077} | 0.8578            | 0.8778         | 0.8677     | 0.7914           |
+| 0.008         | 63.16  | 1200 | 1.8600          | {'precision': 0.8239130434782609, 'recall': 0.9277845777233782, 'f1': 0.8727691421991941, 'number': 817} | {'precision': 0.5865384615384616, 'recall': 0.5126050420168067, 'f1': 0.5470852017937219, 'number': 119}  | {'precision': 0.9037735849056604, 'recall': 0.8895078922934077, 'f1': 0.8965839962564343, 'number': 1077} | 0.8527            | 0.8828         | 0.8675     | 0.8009           |
+| 0.0037        | 73.68  | 1400 | 2.8372          | {'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817} | {'precision': 0.5966386554621849, 'recall': 0.5966386554621849, 'f1': 0.5966386554621849, 'number': 119}  | {'precision': 0.8961748633879781, 'recall': 0.9136490250696379, 'f1': 0.9048275862068965, 'number': 1077} | 0.8731            | 0.8922         | 0.8826     | 0.7928           |
+| 0.004         | 84.21  | 1600 | 2.8378          | {'precision': 0.881578947368421, 'recall': 0.9020807833537332, 'f1': 0.8917120387174834, 'number': 817}  | {'precision': 0.631578947368421, 'recall': 0.6050420168067226, 'f1': 0.6180257510729613, 'number': 119}   | {'precision': 0.891989198919892, 'recall': 0.9201485608170845, 'f1': 0.9058500914076782, 'number': 1077}  | 0.8734            | 0.8942         | 0.8837     | 0.8079           |
+| 0.0018        | 94.74  | 1800 | 3.0272          | {'precision': 0.8742655699177438, 'recall': 0.9106487148102815, 'f1': 0.8920863309352519, 'number': 817} | {'precision': 0.6759259259259259, 'recall': 0.6134453781512605, 'f1': 0.6431718061674008, 'number': 119}  | {'precision': 0.89937106918239, 'recall': 0.9294336118848654, 'f1': 0.9141552511415526, 'number': 1077}   | 0.8774            | 0.9031         | 0.8901     | 0.7992           |
+| 0.0008        | 105.26 | 2000 | 2.9565          | {'precision': 0.8948004836759371, 'recall': 0.9057527539779682, 'f1': 0.9002433090024331, 'number': 817} | {'precision': 0.6868686868686869, 'recall': 0.5714285714285714, 'f1': 0.6238532110091742, 'number': 119}  | {'precision': 0.8923212709620476, 'recall': 0.9387186629526463, 'f1': 0.9149321266968325, 'number': 1077} | 0.8834            | 0.9036         | 0.8934     | 0.8096           |
+| 0.0008        | 115.79 | 2200 | 3.1429          | {'precision': 0.8411111111111111, 'recall': 0.9265605875152999, 'f1': 0.881770529994176, 'number': 817}  | {'precision': 0.6666666666666666, 'recall': 0.5546218487394958, 'f1': 0.6055045871559633, 'number': 119}  | {'precision': 0.9147141518275539, 'recall': 0.9062209842154132, 'f1': 0.9104477611940299, 'number': 1077} | 0.8708            | 0.8937         | 0.8821     | 0.7970           |
+| 0.0005        | 126.32 | 2400 | 3.0269          | {'precision': 0.8617511520737328, 'recall': 0.9155446756425949, 'f1': 0.8878338278931751, 'number': 817} | {'precision': 0.6952380952380952, 'recall': 0.6134453781512605, 'f1': 0.6517857142857143, 'number': 119}  | {'precision': 0.906871609403255, 'recall': 0.9312906220984215, 'f1': 0.9189189189189189, 'number': 1077}  | 0.8773            | 0.9061         | 0.8915     | 0.7994           |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.1
+- Tokenizers 0.13.3

preprocessor_config.json

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+{
+  "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
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+  "ocr_lang": null,
+  "processor_class": "LayoutLMv3Processor",
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
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+  "tesseract_config": ""
+}

pytorch_model.bin

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special_tokens_map.json

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+{
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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,
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv3Processor",
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+}