diffgram
dataqa
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diffgram | dataqa | |
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9 | 7 | |
1,795 | 245 | |
1.3% | - | |
9.1 | 6.2 | |
3 days ago | almost 2 years ago | |
Python | JavaScript | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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.
diffgram
- Open source tool scrapes git commit email addresses to send spam to.
- Open Source Training Data – Diffgram
- Open Source Data Annotation - Diffgram
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[D] Labelbox threatens to sue small open-source startup Diffgram
As a few examples of how much depth we have considered, here's a detailed comparison with sagemaker. Part of an integration with scale. Part of code for labelbox integration, datasaur (scroll to trusted by for our logo) etc. To the best of my knowledge I am trying to track every firm that is in this direct space.
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[P] 😃🎉 Open source AI/ML data annotation platform for free [Product Hunt]
(Direct repo link https://github.com/diffgram/diffgram)
- Open Source AI Data Annotation 2.0 (Source Code)
- Open Source AI Data Annotation 2.0
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[P] Diffgram - Open Annotation Platform
We list some benefits here but if I had to pick one thing, it's that it's a complete system. You can be up and running in 2 minutes on docker. And scale to "big tech co" level on multiple k8s clusters.
- Show HN: Open Core Diffgram (GitHub)
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.
What are some alternatives?
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
awesome-data-labeling - A curated list of awesome data labeling tools
general
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
docarray - Represent, send, store and search multimodal data
hover - :speedboat: Label data at scale. Fun and precision included.
poutyne - A simplified framework and utilities for PyTorch
auto_annotate - Labeling is boring. Use this tool to speed up your next object detection project!
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node