Fastrank

We revamped how CI scripts work and upgraded Maturin in order to get "Apple Silicon" support.

What's Changed
* Update zstd requirement from 0.4 to 0.9 by dependabot-preview in https://github.com/jjfiv/fastrank/pull/45
* Upgrade to GitHub-native Dependabot by dependabot-preview in https://github.com/jjfiv/fastrank/pull/42
* Update zstd requirement from 0.9 to 0.10 by dependabot in https://github.com/jjfiv/fastrank/pull/48
* Update ordered-float requirement from 2.0 to 3.0 by dependabot in https://github.com/jjfiv/fastrank/pull/50
* Update zstd requirement from 0.10 to 0.11 by dependabot in https://github.com/jjfiv/fastrank/pull/49
* Update zstd requirement from 0.11 to 0.12 by dependabot in https://github.com/jjfiv/fastrank/pull/51
* Try CI.yml modified from cramjam project by jjfiv in https://github.com/jjfiv/fastrank/pull/54

**Full Changelog**: https://github.com/jjfiv/fastrank/compare/0.7.0...0.8.0

- CModel now has ``predict_scores`` that returns a sparse representation of ``Dict[int, float]`` where the position in the arrays you've loaded correspond to the score.
- We also have ``predict_dense_scores`` which returns a ``List[float]`` with the same semantics. If you have subsampled queries, this may make less sense than the aforementioned method.
- We have some better testing covering these features.
- Note: trying again because of a glitch in automatic releases ('fix' for 32 insufficient)

Rather than require the arbitrary libc etc. from the gh-actions publish, align with manylinux2010. Fixed some documentation nits along the way.

Updates in 0.6.0 are:

- ***Minimum python version now 3.6*** -- 3.5 started failing on CI, so it's gone now.
- ***support for faster float parsing*** -- on my machine the msn30k dataset took 90s to load, and now only takes 60s. Thanks rust libraries!
- ***Windows supported*** - now that I have regular access to a windows machine, I will make sure PyPI has windows builds.

FastRank

``FastRank`` is an implementation of ``CoordinateAscent``[1] from [Ranklib](https://sourceforge.net/p/lemur/wiki/RankLib/) that you can pip install; written in Rust and uses threads for efficiency; it will scale much better than the Java version to large datasets and many features.

It also has ``RandomForests``, and someday ``LambdaMART`` (others depending on interest). I've been thinking a lot about what the limits of coordinate ascent are (linearity), and will probably play with that in future versions.

This is ready for production use in the sense that I used it for my TREC submission this year. The python API could use some thoughts and experiences (post issues on Github).

bash
pip install fastrank


- [Blog Post](https://jjfoley.me/2019/10/11/fastrank-alpha.html)
- [Jupyter Notebook Demo](https://colab.research.google.com/drive/1IjF7yTin1XaNO_6mBNxAoQYTmF0nckk1)


[1] Metzler, D., & Croft, W. B. (2007). Linear feature-based models for information retrieval. Information Retrieval, 10(3), 257-274.