srez VS seqeval

Compare srez vs seqeval and see what are their differences.

srez

Image super-resolution through deep learning (by david-gpu)

seqeval

A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...) (by chakki-works)
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srez seqeval
1 1
5,288 1,046
- 0.7%
0.0 0.0
over 6 years ago 12 days ago
Python Python
MIT License MIT License
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srez

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

seqeval

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 | /r/LanguageTechnology | 12 Mar 2021
    . 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?

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

tensorflow - An Open Source Machine Learning Framework for Everyone

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

Keras - Deep Learning for humans

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

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

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

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

Pylearn2 - Warning: This project does not have any current developer. See bellow.

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