xorbits
seqeval
| xorbits | seqeval | |
|---|---|---|
| 7 | 1 | |
| 1,011 | 1,046 | |
| 1.7% | 0.7% | |
| 8.8 | 0.0 | |
| about 1 month ago | 11 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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xorbits
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Everything you need to know about pandas 2.0.0!
Here’s our project: https://github.com/xprobe-inc/xorbits
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Introducing Xorbits: A Distributed Python Data Science Framework for Large Dataset Analysis
Hey everyone, we are excited to announce our new project, Xorbits, a scalable data science framework that aims to scale the entire Python data science world.
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Use maximum PC Hardware Resources
My suggestion is to use some parallel computing framework like Xorbits. The framework will parallel your workload automatically. For data processing tasks, just use xorbits.pandas or xorbits.numpy, and you can run almost any python workload with xorbits.remote.
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Use "distributed pandas" to scale your data science workflow!
If you are interested in learning more about Xorbits, please visit our project's Github for more information: https://github.com/xprobe-inc/xorbits
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A new way to accelerate your data science workflow
Xorbits can be an ideal solution for these issues. Xorbits is a scalable Python data science framework that aims to scale the Python data science stack while keeping the API compatibility. You can get an out-of-box performance gain by changing `import pandas as pd` to `import xorbits.pandas as pd`.
- Scalable Python data science, in an API compatible and fast way
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?
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.
scikit-learn - scikit-learn: machine learning in Python
tensorflow - An Open Source Machine Learning Framework for Everyone
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
PyBrain
trueskill - An implementation of the TrueSkill rating system for Python
Keras - Deep Learning for humans
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)