Universal Information Extraction (UIE) is a state-of-the-art (SOTA) method found in PaddleNLP. With the ability to effectively extract information, this model is highly useful for various natural language processing tasks. In this guide, we will walk you through the steps to get started with UIE, ensuring a user-friendly experience. Let’s dive in!

Steps to Use UIE

• Step 1: Clone the Model to Your Local File

To begin, you need to clone the UIE model. Open your terminal and follow the commands below:

git lfs install
git clone https://huggingface.co/Pkyuie-base

If you don’t have git-lfs installed, you can manually download the model:

• Click on Files and versions at the top of this card.
• Use this code example to download files.
• Step 2: Load the Model Locally

Now that you have the model stored locally, it’s time to load it for use. Here’s how you do it:

import os
import torch
from transformers import AutoTokenizer

uie_model = "uie-base-zh"
model = torch.load(os.path.join(uie_model, 'pytorch_model.bin'))  # load UIE model
tokenizer = AutoTokenizer.from_pretrained(uie_model)  # load tokenizer

# Here you can input your data and get probabilities
start_prob, end_prob = model(input_ids=batch['input_ids'],
                             token_type_ids=batch['token_type_ids'],
                             attention_mask=batch['attention_mask'])

print(f'start_prob (type: {type(start_prob)}): {start_prob.size()}')  # start_prob
print(f'end_prob (type: {type(end_prob)}): {end_prob.size()}')        # end_prob

The model output displayed with a batch size of 16 and maximum sequence length of 256 appears as follows:

start_prob (class torch.Tensor): torch.Size([16, 256])
end_prob (class torch.Tensor): torch.Size([16, 256])

Understanding the Process with an Analogy

Imagine you’re a chef preparing a feast. In this case, the UIE model is like your cooking pot, designed to combine various ingredients (data) and extract the most delicious dish (information) from them.

In our first step, cloning the model is akin to gathering all your ingredients into your kitchen. You’ll need to ensure you have everything before cooking starts. The git clone command is your personal grocery delivery service!

Next, when you load the model locally, it’s similar to placing your pot over heat. Here, you’re bringing your ingredients together in the pot (loading the model), all while making sure you have the right tools at hand (the tokenizer). Once everything is ready, you can begin cooking (running inference), and the start_prob and end_prob represent the delicious aroma wafting from your pot, giving you a sense of the goodness contained in your dish!

Troubleshooting

If you encounter any issues while using the UIE model, here are some tips to help you out:

• Make sure that git-lfs is installed on your machine if you are using the git clone method.
• Ensure that all file paths are correct when loading the model and tokenizer. Typos can lead to file-not-found errors.
• Check your Python environment to confirm that all necessary libraries are installed and updated.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.