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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd-layoutlmv3 |
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model-index: |
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- name: lilt-en-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# lilt-en-funsd |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9565 |
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- Answer: {'precision': 0.8948004836759371, 'recall': 0.9057527539779682, 'f1': 0.9002433090024331, 'number': 817} |
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- Header: {'precision': 0.6868686868686869, 'recall': 0.5714285714285714, 'f1': 0.6238532110091742, 'number': 119} |
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- Question: {'precision': 0.8923212709620476, 'recall': 0.9387186629526463, 'f1': 0.9149321266968325, 'number': 1077} |
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- Overall Precision: 0.8834 |
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- Overall Recall: 0.9036 |
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- Overall F1: 0.8934 |
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- Overall Accuracy: 0.8096 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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