readme


please read this before working with model

Working with an invoice information extraction model is a common use case in natural language processing (NLP) and document processing.
In this model, we'll create a simple summary of building an invoice information extraction model using Python, PyTorch, and pretrained models.

1.    Data Preparation:
        Collect a dataset of labeled invoices where you've identified and labeled the key information you want to extract
        (e.g., invoice date, total amount, billing address, items, etc.).

2.    Data Preprocessing:
        Preprocess the text in your invoices, which may involve cleaning, tokenization, and converting it into a suitable 
        format for training (e.g., tokenized text or numerical features).

3.    Model Building:
        Choose a machine learning or deep learning model for information extraction. In this case, you can use a sequence
        labeling model such as Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), or a pre-trained transformer model like BERT.
        Train the model on your labeled invoice data to predict the relevant fields (e.g., entity recognition for extracting dates, addresses, amounts, etc.).
4.   Model Testing:
        test your model by asking relevent question.