Label Studio for Transformers
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
Quick Usage
Install Label Studio and other dependencies
pip install -r requirements.txt
Create ML backend with BERT classifier
label-studio-ml init my-ml-backend --script models/bert_classifier.py
cp models/utils.py my-ml-backend/utils.py
Create ML backend with BERT named entity recognizer
label-studio-ml init my-ml-backend --script models/ner.py
cp models/utils.py my-ml-backend/utils.py
Start ML backend at http://localhost:9090
label-studio-ml start my-ml-backend
Start Label Studio with ML backend connection
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
