openskill.py
seqeval
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| openskill.py | seqeval | |
|---|---|---|
| 22 | 1 | |
| 241 | 1,045 | |
| 4.1% | 1.4% | |
| 7.3 | 0.0 | |
| 6 days ago | 7 days ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
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openskill.py
- Show HN: Predict team ranks in sports and video games with openskill.py
- Predict how teams will rank in sports/video games using our rating system.
- I made a project with the ability to predict ranks of teams in a sports/video game match.
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Openskill: A patent-free alternative to TrueSkill
Linked is the Javascript library. There are also ports of this to Python, Kotlin, Lua, and Elixir.
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Predicting Overwatch Match Outcomes with 90% Accuracy
The new benchmark code can be found here.
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openskill.py VS trueskill - a user suggested alternative
2 projects | 30 Jan 2022
- openskill.py can now predict the winners of any game with 90% accuracy and faster than TrueSkill
seqeval
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Beginner questions about NER model evaluation.
. The standard way to evaluate NER (or any other sequence labelling problem) is to use the conlleval script (https://www.clips.uantwerpen.be/conll2000/chunking/output.html) or through the seqeval package in python (https://github.com/chakki-works/seqeval) . Either way, you need a list of predicted labels and a list of gold labels (see the code example in the link, it should be trivial to converse your output to the same data format).
What are some alternatives?
trueskill - An implementation of the TrueSkill rating system for Python
scikit-learn - scikit-learn: machine learning in Python
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
Data Flow Facilitator for Machine Learning (dffml) - The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
tensorflow - An Open Source Machine Learning Framework for Everyone
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
Keras - Deep Learning for humans