Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Updated links in README.md #140

Merged
merged 1 commit into from Feb 11, 2022
Merged

Updated links in README.md #140

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
10 changes: 5 additions & 5 deletions README.md
View file
Open in desktop
Expand Up @@ -8,9 +8,9 @@ Real world data contains multiple records belonging to the same customer. These

Zingg is an ML based tool for entity resolution. The following features set Zingg apart from other tools and libraries
- Ability to handle any entity like customer, patient, supplier, product etc
- Ability to connect to [disparate data sources](https://docs.zingg.ai/docs/dataSourcesAndSinks/connectors.html). Local and cloud file systems in any format, enterprise applications and relational, NoSQL and cloud databases and warehouses
- Ability to scale to large volumes of data. [See why this is important](https://docs.zingg.ai/zingg/zModels) and [Zingg performance numbers](https://docs.zingg.ai/zingg/setup/hardwareSizing)
- [Interactive training data builder](https://docs.zingg.ai/docs/setup/createTrainingData.html#label---user-feedback-on-the-training-pairs) using active learning that builds models on frugally small training samples to high accuracy.
- Ability to connect to [disparate data sources](https://docs.zingg.ai/zingg/connectors). Local and cloud file systems in any format, enterprise applications and relational, NoSQL and cloud databases and warehouses
- Ability to scale to large volumes of data. [See why this is important](https://docs.zingg.ai/zingg/zmodels/) and [Zingg performance numbers](https://docs.zingg.ai/zingg/stepbystep/hardwaresizing)
- [Interactive training data builder](https://docs.zingg.ai/zingg/stepbystep/createtrainingdata/label) using active learning that builds models on frugally small training samples to high accuracy.
![Shows records and asks user to mark yes, no, cant say on the cli.](/assets/label.gif)
- Ability to define domain specific functions to improve matching
- Out of the box support for English as well as Chinese, Thai, Japanese, Hindi and other languages
Expand Down Expand Up @@ -40,15 +40,15 @@ docker run -it zingg/zingg:0.3.2 bash
./scripts/zingg.sh --phase match --conf examples/febrl/config.json
```

Check the [step by step guide](https://docs.zingg.ai/docs/stepByStep.html) for more details.
Check the [step by step guide](https://docs.zingg.ai/zingg/stepbystep) for more details.

## The Story

What is the [backstory behind Zingg](https://sonalgoyal.substack.com/p/time-to-zingg)?

## Documentation

Check detailed Zingg [documentation](https://docs.zingg.ai)
Check detailed Zingg [documentation](https://docs.zingg.ai/zingg/)

## Community

Expand Down