langhuan
markup
| langhuan | markup | |
|---|---|---|
| 2 | 3 | |
| 12 | 232 | |
| - | - | |
| 1.8 | 6.9 | |
| over 2 years ago | 4 months ago | |
| Jupyter Notebook | TypeScript | |
| - | MIT License |
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langhuan
markup
-
Show HN: An annotation tool for ML and NLP
Hey HN! I'm super excited to share Markup with you, which is a totally free & open-source annotation tool that helps you transform unstructured text (e.g. news articles) into structured data that you can use for building, training, or fine-tuning ML models!
Check it out: https://github.com/samueldobbie/markup
Just to preface this summary, it's all a bit hacked together at the moment, and I'm in the process of rewriting the tool from scratch so this description is privy to change.
To generate the suggestions there's an active learner with an underlying random forest classifier, that has been fed ~60 seed sentences [1], to classify positive sentences (e.g. contains a prescription) and negative sentences (e.g. doesn't contain a prescription).
All positive sentences are fed into a sequence-to-sequence RNN model, that has been trained on ~50k synthetic rows of data [2] which maps unstructured sentences (e.g. patient is on pheneturide 250mg twice a day) to a structured output with the desired features (e.g. name: pheneturide; dose: 285; unit: g; frequency: 2). These synthetic sentences were generated with the in-built data generator [3].
The outputs of the RNN are validated to ensure they meet the expected structure and are valid for the sentence (e.g. the predicted drug name must exist somewhere within the sentence).
All non-junk predictions are shown to the user who can accept, edit, or reject each. Based on the users' response, the active learner is refined (currently nothing is fed back into the RNN).
[1] https://github.com/samueldobbie/markup/blob/master/data/text...
[2] https://raw.githubusercontent.com/samueldobbie/markup/master...
[3] https://www.getmarkup.com/tools/data-generator/
What are some alternatives?
Universal Data Tool - Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
pawls - Software that makes labeling PDFs easy.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
xtreme1 - Xtreme1 is an all-in-one data labeling and annotation platform for multimodal data training and supports 3D LiDAR point cloud, image, and LLM.
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
force-multiplier - Use AI to edit your documents in real-time. Provide feedback and let the AI do all the work.
datalabel - datalabel is a UI-based data editing tool that makes it easy to create labeled text data in a dataframe. With datalabel, you can quickly and effortlessly edit your data without having to write any code. Its intuitive interface makes it ideal for both experienced data professionals and those new to data editing.