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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

label-studio-ml init my-ml-backend --script models/ner.py
cp models/utils.py my-ml-backend/utils.py

label-studio-ml start my-ml-backend

label-studio start my-annotation-project --init --ml-backend http://localhost:9090

The browser opens at http://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