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README.md
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**A Quick Example**
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```python
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from networks.modeling_erine_layout import ErnieLayoutConfig, ErnieLayoutForQuestionAnswering
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from networks.tokenizer import ErnieLayoutTokenizer
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pretrain_torch_model_or_path = "path/to/pretrained-model"
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# initialize tokenizer
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tokenizer = ErnieLayoutTokenizer.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
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# initialize config
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config = ErnieLayoutConfig.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
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config.num_classes = 2
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# initialize ERNIE for VQA
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model = ErnieLayoutForQuestionAnswering.from_pretrained(
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config=config,
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```
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**A Quick Example**
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```python
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from networks.modeling_erine_layout import ErnieLayoutConfig, ErnieLayoutForQuestionAnswering
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from networks.feature_extractor import ErnieFeatureExtractor
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from networks.tokenizer import ErnieLayoutTokenizer
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from networks.model_util import ernie_qa_tokenize, prepare_context_info
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from PIL import Image
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pretrain_torch_model_or_path = "path/to/pretrained-model"
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# initialize tokenizer
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tokenizer = ErnieLayoutTokenizer.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
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context = ['This is an example document', 'All ocr boxes are inserted into this list']
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layout = [[381, 91, 505, 115], [738, 96, 804, 122]]
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# intialize feature extractor
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feature_extractor = ErnieFeatureExtractor()
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# open the image of the document
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pil_image = Image.open("/path/to/image").convert("RGB")
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# Process image
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tokenized_res['pixel_values'] = feature_extractor(pil_image)
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# Tokenize context & questions
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context_encodings, = prepare_context_info(tokenizer, context, layout)
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question = "what is it?"
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tokenized_res = ernie_qa_tokenize(tokenizer, question, context_encodings)
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# answer start && end index
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tokenized_res['start_positions'] = 6
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tokenized_res['end_positions'] = 12
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# initialize config
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config = ErnieLayoutConfig.from_pretrained(pretrained_model_name_or_path=pretrain_torch_model_or_path)
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config.num_classes = 2 # start and end
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# initialize ERNIE for VQA
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model = ErnieLayoutForQuestionAnswering.from_pretrained(
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config=config,
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)
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output = model(**tokenized_res)
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```
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