dataqa
Gorgonia
Our great sponsors
dataqa | Gorgonia | |
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7 | 21 | |
245 | 5,326 | |
- | 1.0% | |
6.2 | 2.8 | |
almost 2 years ago | 21 days ago | |
JavaScript | Go | |
GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dataqa
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[D] Looking for open source projects to contribute
Hey, I am the creator and (only contributor today) of open-source https://github.com/dataqa/dataqa, a Python library to explore and annotate documents. It uses weak supervision, is based on spacy, and has a lot of opportunities to add more deep learning and ML functionality. I can guide you through it :-). This would be a great opportunity to be first and lead contributor of an open-source library (outside the creator).
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[P]: Extract and label data from Wikipedia with DataQA
I recently added a new feature to DataQA (https://github.com/dataqa/dataqa) to be able to extract entities from Wikipedia. All you need to do is upload a file with Wikipedia urls:
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Show HN: DataQA – now possible to link entities to large ontologies
The open-source project is here: https://github.com/dataqa/dataqa. I have just released a feature which I have been working on for a while to solve a problem which I've seen a lot in industry: how to map entities found in text to large knowledge base ontologies.
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[P] Using rules to speed up labelling by 2x
The tool I developed and used for this problem: https://github.com/dataqa/dataqa
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The First Rule of Machine Learning: Start Without Machine Learning
I have seen first hand at small and large companies how problems have been tackled with ML without trying a simple rule or heuristic first. And then, further down the line, the system has been compared to a few business rules put together, to find that the difference in performance did not explain the deployment of an ML system in the first place.
It's true that if your rules grow in complexity, this might make it harder to maintain, but the good thing about rules is that they tend to be fully explainable, and they can be encoded by domain experts. So the maintenance of such a system does not need to be done exclusively by an ML engineer anymore.
Here is where I insert my plug: I have developed a tool to create rules to solve NLP problems: https://github.com/dataqa/dataqa
- Show HN: Rules-based labelling tool for NLP
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DataQA: the new Python app to do rules-based text annotation
After working in ML for more than a decade, I became frustrated over time with the lack of tools to create baselines using simple rules and heuristics. It is well known that most business problems out there can achieve decent baselines using only heuristics. This is why I have developed DataQA (https://github.com/dataqa/dataqa), which uses NLP rules to do common NLP annotation tasks, such as multiclass classification or named entity recognition.
Gorgonia
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Machine Learning en GO! 🤯
GitHub - gorgonia/gorgonia: Gorgonia is a library that helps facilitate machine learning in Go.
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Machine Learning
I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.
- Why isn’t Go used in AI/ML?
- GoLang AI/ML open source projects
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A systematic framework for technical documentation authoring
Perhaps it's a product of French culture, but because Gorgonia[0] has a number of French contributors, this was actually the way we structured our documentation.
But this is the first time I've heard of the name of the framework.
[0]: https://gorgonia.org
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[D] When was the last time you wrote a custom neural net?
Oh it's.Gorgonia
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Most Popular GoLang Frameworks
Website: https://gorgonia.org
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[D] What framework are you using?
I use Gorgonia.
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Why can't Go be popular for machine learning?
What you think about this https://github.com/gorgonia/gorgonia ? I also recall there is something else out there but can't find it at the moment...
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Neural networks in golang
Yep, all of them: https://github.com/gorgonia/gorgonia
What are some alternatives?
diffgram - The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.
onnx-go - onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
GoLearn - Machine Learning for Go
general
tfgo - Tensorflow + Go, the gopher way
docarray - Represent, send, store and search multimodal data
goml - On-line Machine Learning in Go (and so much more)
poutyne - A simplified framework and utilities for PyTorch
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
bayesian - Naive Bayesian Classification for Golang.