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Use Custom NER with continuous learning
AI Center
Last updated Oct 10, 2024
Use Custom NER with continuous learning
This example is used to extract chemicals by the category mentioned in research papers. By following the procedure below you will extract the chemicals and categorize them as ABBREVIATION, FAMILY, FORMULA, IDENTIFIER, MULTIPLE, SYSTEMATIC, TRIVIAL and NO_CLASS.
This procedure uses the Custom Named Entity Recognition package. For more information on how this package works and what it can be used for, see Custom Named Entity Recognition.
For this procedure, we have provided sample files as follows:
- Pre-labeled training dataset in CoNLL format. You can download it from here.
- Pre-labeled test dataset. You can download it from here.
- Sample workflow for extracting categories of
chemicals mentioned in research papers. You can download it from here.
Note: Make sure that the following variables are filled in in the sample file:
in_emailAdress
- the email address to which the Action Center task will be assigned toin_MLSkillEndpoint
- public endpoint of the ML Skillin_MLSkillAPIKey
- API key of the ML Skillin_labelStudioEndpoint
- optional, to enable continuous labeling: provide import URL of a label studio project
Follow the procedure below to extract chemicals by their category from research papers.
To get started with Label Studio and export data to AI Center, follow the instructions below.
- Install Label Studio on your local machine or cloud instance. To do so, follow the instructions from here.
- Create a new project from the Named Entity Recognition Template and define your Label Names.
- Make sure that the label names have no special characters or spaces. For example, instead of
Set Date
, useSetDate
. - Make sure that the value of the
<Text>
tag is"$text"
. - Upload the data using the API from here.
cURL request example:
curl --location --request POST 'https://<label-studio-instance>/api/projects/<id>/import' \)\) --header 'Content-Type: application/json' \)\) --header 'Authorization: Token <Token>' \)\) --data-raw '[ { "data": { "text": "<Text1>" }, }, { "data": { "text": "<Text2>" } } ]'
curl --location --request POST 'https://<label-studio-instance>/api/projects/<id>/import' \)\) --header 'Content-Type: application/json' \)\) --header 'Authorization: Token <Token>' \)\) --data-raw '[ { "data": { "text": "<Text1>" }, }, { "data": { "text": "<Text2>" } } ]' - Annotate your data.
- Export the data in CoNLL 2003 format and upload it to AI Center.
- Provided the Label Studio instance URL and API key in the provided sample workflow in order to capture incorrect and low confidence predictions.