Label-sleuth

**Features**
- Support import of weakly labeled data using `Weak` in `label_type` column
- Add training set selectors which use weakly labeled data

**UI changes**
- Avoid scrolling into an element when labeling it
- Improve notification of internal server errors (500 errors)

**Bug fixes**
- Fix get model predictions endpoint
- Fix element ids not properly converted into document ids when sending to the frontend

UI changes
* Unfocus text input when search is done.
* Add icons for accessing Github, Slack and Webpage.
* Decrease version font size.
* Expose shortcuts information so users are aware of them.

Performance improvements
* Positive predictions are loaded in batches instead of the whole corpus's positive predictions at once.
* Dataset is preloaded when the user enters a workspace.

Bug fixes
* Category name's whitespaces are replaced with underscore when importing labels into the workspace.
* Labeling a main element scrolls to top of the doc.
* Unexpected behavior due to shared state across tabs.

- Improved performance in backend and UI
- Display the system version in the UI
- Added keyboard shortcuts for labeling using the arrow keys
- Update requirements.txt to support newer Mac hardware
- Fixed a major bug in BERT model which caused all the predictions to be positive
- Various bug fixes

- Add option to delete workspaces
- Right panel is now resizable
- Add pagination in all right panel lists
- Search is no longer using regular expressions
- Support for Italian
- Advanced: Allow exporting the set of labels that are used for training a model (rather than the set of labeled elements labeled by the user)
- Improved error reporting in the frontend
- Various bug fixes

- Add model download button
- Bug fixes

- Support for exporting models so they can be used in python code independently of the Label Sleuth frontend
- New component for evaluating the precision of the current model: N examples are sampled from the model's positive predictions, the user labels these N examples in a dedicated `Precision evaluation` panel, and the system outputs a precision estimate based on these labels.
- Major refactor of the frontend code to improve stability, performance and code quality.
- Various bug fixes in the backend and frontend