scikit-learn VS seqeval

Compare scikit-learn vs seqeval and see what are their differences.


scikit-learn: machine learning in Python (by scikit-learn)


A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) (by chakki-works)
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scikit-learn seqeval
30 1
48,549 684
1.0% 3.8%
9.9 0.0
4 days ago 6 months ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
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Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-17.


Posts with mentions or reviews of seqeval. We have used some of these posts to build our list of alternatives and similar projects.
  • Beginner questions about NER model evaluation.
    1 project | | 12 Mar 2021
    . The standard way to evaluate NER (or any other sequence labelling problem) is to use the conlleval script ( or through the seqeval package in python ( . 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?

When comparing scikit-learn and seqeval you can also consider the following projects:

Keras - Deep Learning for humans

Surprise - A Python scikit for building and analyzing recommender systems

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

tensorflow - An Open Source Machine Learning Framework for Everyone

gensim - Topic Modelling for Humans


TFLearn - Deep learning library featuring a higher-level API for TensorFlow.

MLflow - Open source platform for the machine learning lifecycle

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

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.

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