In this repository we will continuously post automation-suites for the annotation process at SuperAnnotate. Notebooks are originally written and tested in Google Colab. So if you are looking for a plug and play Google Colab is the way to go.
| Notebook | Google Colab | Github | Tutorial Data |
|---|---|---|---|
| Text Classification with Cohere |  |
Open in Github | Amazon Product Reviews Dataset |
| Speech Recognition with Whisper | |
Open in Github | US Election 2020 - Presidential Debates Dataset |
| Image classification with Rekognition | |
Open in Github | RESISC45 Dataset |
| Named Entity Recognition with HuggingFace (BERT) | |
Open in Github | Legal NER Dataset |
| Named Entity Recognition with HuggingFace (QANer, BERT) | |
Open in Github | Legal NER Dataset |
| Text Classification with HuggingFace (BERT) | |
Open in Github | Ford Sentence Classification Dataset |
Object Tracking Tutorial with Google Vertex AI requires significant time for model training and certain steps for GCP account setup. Therefore we advice you to run it on your local machine. Open in Github
We encourage the community to open pull requests and share with us their automation pipelines in the form of IPython notebooks. Please find the API Reference for SuperAnnotate Python SDK here.