seqeval VS Metrics

Compare seqeval vs Metrics and see what are their differences.

seqeval

A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) (by chakki-works)

Metrics

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave (by benhamner)
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seqeval Metrics
1 2
1,039 1,612
1.7% -
0.0 0.0
about 2 months ago about 1 year ago
Python Python
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

seqeval

Posts with mentions or reviews of seqeval. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning seqeval yet.
Tracking mentions began in Dec 2020.

Metrics

Posts with mentions or reviews of Metrics. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Metrics yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing seqeval and Metrics you can also consider the following projects:

scikit-learn - scikit-learn: machine learning in Python

SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

tensorflow - An Open Source Machine Learning Framework for Everyone

flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)

Keras - Deep Learning for humans

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

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.

gym - A toolkit for developing and comparing reinforcement learning algorithms.