| title | titleSuffix | description | author | manager | ms.service | ms.topic | ms.date | ms.author | ms.custom |
|---|---|---|---|---|---|---|---|---|---|
How to train your Custom Named Entity Recognition (NER) model |
Azure AI services |
Learn about how to train your model for Custom Named Entity Recognition (NER). |
jboback |
nitinme |
azure-ai-language |
how-to |
12/19/2023 |
jboback |
language-service-custom-ner |
Training is the process where the model learns from your labeled data. After training is completed, you'll be able to view the model's performance to determine if you need to improve your model.
To train a model, you start a training job and only successfully completed jobs create a model. Training jobs expire after seven days, which means you won't be able to retrieve the job details after this time. If your training job completed successfully and a model was created, the model won't be affected. You can only have one training job running at a time, and you can't start other jobs in the same project.
The training times can be anywhere from a few minutes when dealing with few documents, up to several hours depending on the dataset size and the complexity of your schema.
- A successfully created project with a configured Azure blob storage account
- Text data that has been uploaded to your storage account.
- Labeled data
See the project development lifecycle for more information.
Before you start the training process, labeled documents in your project are divided into a training set and a testing set. Each one of them serves a different function. The training set is used in training the model, this is the set from which the model learns the labeled entities and what spans of text are to be extracted as entities. The testing set is a blind set that is not introduced to the model during training but only during evaluation. After model training is completed successfully, the model is used to make predictions from the documents in the testing and based on these predictions evaluation metrics are calculated. It's recommended to make sure that all your entities are adequately represented in both the training and testing set.
Custom NER supports two methods for data splitting:
- Automatically splitting the testing set from training data:The system will split your labeled data between the training and testing sets, according to the percentages you choose. The recommended percentage split is 80% for training and 20% for testing.
Note
If you choose the Automatically splitting the testing set from training data option, only the data assigned to training set will be split according to the percentages provided.
- Use a manual split of training and testing data: This method enables users to define which labeled documents should belong to which set. This step is only enabled if you have added documents to your testing set during data labeling.
[!INCLUDE Train model]
[!INCLUDE train model]
Training could take sometime depending on the size of your training data and complexity of your schema. You can use the following request to keep polling the status of the training job until it's successfully completed.
[!INCLUDE get training model status]
[!INCLUDE Cancel training]
[!INCLUDE Cancel training]
After training is completed, you'll be able to view model performance to optionally improve your model if needed. Once you're satisfied with your model, you can deploy it, making it available to use for extracting entities from text.