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--- |
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license: mit |
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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: 1.6117 |
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- Answer: {'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817} |
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- Header: {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119} |
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- Question: {'precision': 0.9045045045045045, 'recall': 0.9322191272051996, 'f1': 0.9181527206218564, 'number': 1077} |
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- Overall Precision: 0.8797 |
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- Overall Recall: 0.9006 |
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- Overall F1: 0.8900 |
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- Overall Accuracy: 0.8204 |
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## Model Usage |
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```python |
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from transformers import LiltForTokenClassification, LayoutLMv3Processor |
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from PIL import Image, ImageDraw, ImageFont |
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import torch |
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# load model and processor from huggingface hub |
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model = LiltForTokenClassification.from_pretrained("philschmid/lilt-en-funsd") |
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processor = LayoutLMv3Processor.from_pretrained("philschmid/lilt-en-funsd") |
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# helper function to unnormalize bboxes for drawing onto the image |
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def unnormalize_box(bbox, width, height): |
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return [ |
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width * (bbox[0] / 1000), |
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height * (bbox[1] / 1000), |
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width * (bbox[2] / 1000), |
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height * (bbox[3] / 1000), |
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] |
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label2color = { |
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"B-HEADER": "blue", |
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"B-QUESTION": "red", |
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"B-ANSWER": "green", |
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"I-HEADER": "blue", |
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"I-QUESTION": "red", |
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"I-ANSWER": "green", |
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} |
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# draw results onto the image |
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def draw_boxes(image, boxes, predictions): |
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width, height = image.size |
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normalizes_boxes = [unnormalize_box(box, width, height) for box in boxes] |
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# draw predictions over the image |
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draw = ImageDraw.Draw(image) |
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font = ImageFont.load_default() |
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for prediction, box in zip(predictions, normalizes_boxes): |
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if prediction == "O": |
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continue |
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draw.rectangle(box, outline="black") |
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draw.rectangle(box, outline=label2color[prediction]) |
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draw.text((box[0] + 10, box[1] - 10), text=prediction, fill=label2color[prediction], font=font) |
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return image |
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# run inference |
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def run_inference(image, model=model, processor=processor, output_image=True): |
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# create model input |
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encoding = processor(image, return_tensors="pt") |
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del encoding["pixel_values"] |
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# run inference |
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outputs = model(**encoding) |
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predictions = outputs.logits.argmax(-1).squeeze().tolist() |
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# get labels |
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labels = [model.config.id2label[prediction] for prediction in predictions] |
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if output_image: |
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return draw_boxes(image, encoding["bbox"][0], labels) |
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else: |
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return labels |
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run_inference(dataset["test"][34]["image"]) |
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``` |
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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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- mixed_precision_training: Native AMP |
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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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| 0.0211 | 10.53 | 200 | 1.5528 | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.5684210526315789, 'recall': 0.453781512605042, 'f1': 0.5046728971962617, 'number': 119} | {'precision': 0.896551724137931, 'recall': 0.89322191272052, 'f1': 0.8948837209302325, 'number': 1077} | 0.8596 | 0.8728 | 0.8662 | 0.8011 | |
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| 0.0132 | 21.05 | 400 | 1.3143 | {'precision': 0.8447058823529412, 'recall': 0.8788249694002448, 'f1': 0.8614277144571085, 'number': 817} | {'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119} | {'precision': 0.8854262144821264, 'recall': 0.8969359331476323, 'f1': 0.8911439114391144, 'number': 1077} | 0.8548 | 0.8659 | 0.8603 | 0.8095 | |
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| 0.0052 | 31.58 | 600 | 1.5747 | {'precision': 0.8482446206115515, 'recall': 0.9167686658506732, 'f1': 0.8811764705882352, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119} | {'precision': 0.8997161778618732, 'recall': 0.883008356545961, 'f1': 0.8912839737582005, 'number': 1077} | 0.8626 | 0.8798 | 0.8711 | 0.8030 | |
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| 0.0073 | 42.11 | 800 | 1.4848 | {'precision': 0.8487972508591065, 'recall': 0.9069767441860465, 'f1': 0.8769230769230769, 'number': 817} | {'precision': 0.5190839694656488, 'recall': 0.5714285714285714, 'f1': 0.5439999999999999, 'number': 119} | {'precision': 0.8941947565543071, 'recall': 0.8867223769730733, 'f1': 0.8904428904428905, 'number': 1077} | 0.8514 | 0.8763 | 0.8636 | 0.7969 | |
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| 0.0057 | 52.63 | 1000 | 1.3993 | {'precision': 0.8852071005917159, 'recall': 0.9155446756425949, 'f1': 0.9001203369434416, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119} | {'precision': 0.899090909090909, 'recall': 0.9182915506035283, 'f1': 0.9085898024804776, 'number': 1077} | 0.8710 | 0.8987 | 0.8846 | 0.8198 | |
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| 0.0023 | 63.16 | 1200 | 1.6463 | {'precision': 0.8961201501877347, 'recall': 0.8763769889840881, 'f1': 0.886138613861386, 'number': 817} | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119} | {'precision': 0.888, 'recall': 0.9275766016713092, 'f1': 0.9073569482288827, 'number': 1077} | 0.8733 | 0.8833 | 0.8782 | 0.8082 | |
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| 0.001 | 73.68 | 1400 | 1.6476 | {'precision': 0.8676814988290398, 'recall': 0.9069767441860465, 'f1': 0.8868940754039496, 'number': 817} | {'precision': 0.6571428571428571, 'recall': 0.5798319327731093, 'f1': 0.6160714285714286, 'number': 119} | {'precision': 0.908256880733945, 'recall': 0.9192200557103064, 'f1': 0.9137055837563451, 'number': 1077} | 0.8785 | 0.8942 | 0.8863 | 0.8137 | |
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| 0.0014 | 84.21 | 1600 | 1.6493 | {'precision': 0.8814814814814815, 'recall': 0.8739290085679314, 'f1': 0.8776889981561156, 'number': 817} | {'precision': 0.6194690265486725, 'recall': 0.5882352941176471, 'f1': 0.603448275862069, 'number': 119} | {'precision': 0.894404332129964, 'recall': 0.9201485608170845, 'f1': 0.9070938215102976, 'number': 1077} | 0.8740 | 0.8818 | 0.8778 | 0.8041 | |
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| 0.0006 | 94.74 | 1800 | 1.6193 | {'precision': 0.8766467065868263, 'recall': 0.8959608323133414, 'f1': 0.8861985472154963, 'number': 817} | {'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119} | {'precision': 0.8946428571428572, 'recall': 0.9303621169916435, 'f1': 0.912152935821575, 'number': 1077} | 0.8711 | 0.8967 | 0.8837 | 0.8137 | |
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| 0.0001 | 105.26 | 2000 | 1.6048 | {'precision': 0.8751472320376914, 'recall': 0.9094247246022031, 'f1': 0.8919567827130852, 'number': 817} | {'precision': 0.6140350877192983, 'recall': 0.5882352941176471, 'f1': 0.6008583690987125, 'number': 119} | {'precision': 0.9062784349408554, 'recall': 0.924791086350975, 'f1': 0.9154411764705882, 'number': 1077} | 0.8773 | 0.8987 | 0.8879 | 0.8194 | |
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| 0.0001 | 115.79 | 2200 | 1.6117 | {'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817} | {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119} | {'precision': 0.9045045045045045, 'recall': 0.9322191272051996, 'f1': 0.9181527206218564, 'number': 1077} | 0.8797 | 0.9006 | 0.8900 | 0.8204 | |
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| 0.0001 | 126.32 | 2400 | 1.6163 | {'precision': 0.8799048751486326, 'recall': 0.9057527539779682, 'f1': 0.8926417370325694, 'number': 817} | {'precision': 0.6052631578947368, 'recall': 0.5798319327731093, 'f1': 0.5922746781115881, 'number': 119} | {'precision': 0.9062784349408554, 'recall': 0.924791086350975, 'f1': 0.9154411764705882, 'number': 1077} | 0.8788 | 0.8967 | 0.8876 | 0.8192 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.0 |
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- Tokenizers 0.12.1 |
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