flair VS seqeval

Compare flair vs seqeval and see what are their differences.

seqeval

A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) (by chakki-works)
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flair seqeval
9 1
13,487 1,039
0.9% 1.7%
9.4 0.0
6 days ago about 2 months ago
Python Python
GNU General Public License v3.0 or later MIT License
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.

flair

Posts with mentions or reviews of flair. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-22.

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.

What are some alternatives?

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

spacy-models - 💫 Models for the spaCy Natural Language Processing (NLP) library

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

BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

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

Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

gensim - Topic Modelling for Humans

Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

tensorflow - An Open Source Machine Learning Framework for Everyone

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.