author: @kawam tags:#nlp ,#ml,#transformers
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- Prepare the dataset: You will need a dataset of invoices and the corresponding labeled fields. You can create your own dataset by manually labeling the fields or use a pre-existing dataset. Some publicly available datasets for invoice parsing include:
- PubLayNet: A large dataset of document images and annotations, which includes invoices.
- InvoiceNet: A dataset of 10,000 annotated invoices from various countries and industries.
- SROIE: A dataset of receipts, invoices, and other types of documents with labeled fields.
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Preprocess the data: Once you have your dataset, you will need to preprocess it for use with the transformer model. This includes tokenization, converting the text into numerical representations, and preparing the data in a format that can be fed into the model. You can use the Transformers library from Hugging Face to perform these tasks.
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Fine-tune the model: You can use the pre-trained transformers from Hugging Face such as BERT, RoBERTa or ELECTRA for fine-tuning on the labeled invoice dataset. You can use the PyTorch or TensorFlow framework for this task. Here is an example code for fine-tuning BERT on the invoice dataset using PyTorch:
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Evaluate the model: After training the model, evaluate its performance on a held-out dataset or using cross-validation techniques. You can use metrics such as precision, recall, and F1 score to evaluate the model’s performance.
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Use the model for mapping fields: Once the model is trained and evaluated, you can use it to map fields in new invoices by feeding the invoice data through the model and extracting the relevant fields based on the model’s predictions.
Here are some resources to help you get started with the code implementation:
- Hugging Face Transformers library: https://github.com/huggingface/transformers
- PyTorch tutorial on fine-tuning BERT: https://pytorch.org/tutorials/beginner/transformer_tutorial.html
- TensorFlow tutorial on fine-tuning BERT: https://www.tensorflow.org/text/tutorials/classify_text_with_bert
Here are some alternative tool: