LayoutLMv3: A Beginner's Guide to Creating and Training a Custom Dataset | label Studio | NLP

How to Create a Custom Dataset for Training with LayoutLMv3

In this video, I will show you how to create a custom dataset for training with the LayoutLMv3 model. LayoutLMv3 is a powerful language model that can be used for a variety of tasks, including text classification, question answering, and summarization. However, in order to get the most out of LayoutLMv3, you need to train it on a custom dataset that is relevant to your specific task.

In this video, I will walk you through the steps involved in creating a custom dataset for LayoutLMv3. I will also provide you with tips and tricks for creating a high-quality dataset. By the end of this video, you will know how to create a custom dataset that will help you to get the most out of LayoutLMv3.

Here are the steps involved in creating a custom dataset for LayoutLMv3:

Identify your task. The first step is to identify the task that you want to use LayoutLMv3 for. Once you know the task, you can start to collect data that is relevant to that task.
Clean your data. Once you have collected your data, you need to clean it. This means removing any errors or inconsistencies from the data.
Label your data. Once your data is clean, you need to label it. This means assigning each piece of data to a specific category.
Split your data. Once your data is labeled, you need to split it into two sets: a training set and a test set. The training set will be used to train LayoutLMv3, and the test set will be used to evaluate the model's performance.
Train LayoutLMv3. Once you have split your data, you can start to train LayoutLMv3. This process can take several hours, so be patient.
Evaluate LayoutLMv3. Once LayoutLMv3 has finished training, you can evaluate its performance on the test set. This will give you an idea of how well the model will perform on new data.
Here are some tips for creating a high-quality dataset:

Use a variety of sources to collect your data. This will help to ensure that your dataset is representative of the real world.
Make sure that your data is clean and error-free. This will help LayoutLMv3 to learn more effectively.
Label your data carefully. This will help LayoutLMv3 to understand the meaning of the data.
Split your data evenly. This will help to ensure that LayoutLMv3 is trained on a representative sample of the data.
Train LayoutLMv3 for a sufficient amount of time. This will help the model to learn the patterns in the data.
Evaluate LayoutLMv3 on a test set. This will help you to ensure that the model is performing well on new data.

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[DePhpBug]

been heavily invested my time into OCR and ML these few days.
been lucky also able to came across this, as I'm searching also for tools to label my financial document

[KrishnamoorthyP-qppl]

Hi bro, Very good explanation. Henceforth i will watch your videos. keep it up.

[ewzbxxs]

When doing inference on the trained model, do we just need to pass the image to the model or should we send the boundingboxes as well? most examples I have seen is the second scenario. One might ask what's the point of using layoutlmv3 in that case?

[karishmagoswami]

can you please help me with finetuning paddle ocr ?
I watched all 4 videos of yours on that topic but I am getting many errors at the time of training, please help me

[gewrueb]

hello, nice playlist
one question is, can we extract those key value pair in json format for extracting data?

[nnugdld]

Hi I'm AH** , helpfull but where we ca find the json file ???

[vikramm]

Is there some tool which automatically takes the text inside the bounding box instead of manually writing it?

[cooltrucly]

nice video. the background music is unnecessary though.