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Transfer learning for NLP models by annotating your textual data without any additional coding.

This package provides a ready-to-use container that links together:

• Label Studio as annotation frontend
• Hugging Face's transformers as machine learning backend for NLP

pip install -r requirements.txt

label-studio-ml init my-ml-backend --script models/bert_classifier.py
cp models/utils.py my-ml-backend/utils.py
# Start ML backend at https://localhost:9090
label-studio-ml start my-ml-backend
# Start Label Studio in the new terminal with the same python environment
label-studio start

1. Create a project with Choices and Text tags in the labeling config.
2. Connect the ML backend in the Project settings with https://localhost:9090

label-studio-ml init my-ml-backend --script models/ner.py
cp models/utils.py my-ml-backend/utils.py
# Start ML backend at https://localhost:9090
label-studio-ml start my-ml-backend
# Start Label Studio in the new terminal with the same python environment
label-studio start

1. Create a project with Labels and Text tags in the labeling config.
2. Connect the ML backend in the Project settings with https://localhost:9090

The browser opens at https://localhost:8080. Upload your data on Import page then annotate by selecting Labeling page. Once you've annotate sufficient amount of data, go to Model page and press Start Training button. Once training is finished, model automatically starts serving for inference from Label Studio, and you'll find all model checkpoints inside my-ml-backend/<ml-backend-id>/ directory.

Click here to read more about how to use Machine Learning backend and build Human-in-the-Loop pipelines with Label Studio

This software is licensed under the Apache 2.0 LICENSE © Heartex. 2020