Description
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
This is well-tested by using the Perl script conlleval,
which can be used for measuring the performance of a system that has processed the CoNLL-2000 shared task data.
seqeval alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view seqeval alternatives based on common mentions on social networks and blogs.
-
Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. -
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. -
Clairvoyant
Software designed to identify and monitor social/historical cues for short term stock movement -
awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. -
karateclub
A general purpose community detection and network embedding library for research built on NetworkX. -
SciKit-Learn Laboratory
SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments. -
Feature Forge
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API -
Data Flow Facilitator for Machine Learning (dffml)
The easiest way to use Machine Learning -
bodywork
ML-Ops framework for running containerised model-training workloads and deploying model-scoring services, using Kubernetes.
Get performance insights in less than 4 minutes
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of seqeval or a related project?
README
seqeval
seqeval is a Python framework for sequence labeling evaluation. seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
This is well-tested by using the Perl script conlleval, which can be used for measuring the performance of a system that has processed the CoNLL-2000 shared task data.
Support features
seqeval supports following schemes:
- IOB1
- IOB2
- IOE1
- IOE2
- IOBES(only in strict mode)
- BILOU(only in strict mode)
and following metrics:
metrics | description |
---|---|
accuracy_score(y_true, y_pred) | Compute the accuracy. |
precision_score(y_true, y_pred) | Compute the precision. |
recall_score(y_true, y_pred) | Compute the recall. |
f1_score(y_true, y_pred) | Compute the F1 score, also known as balanced F-score or F-measure. |
classification_report(y_true, y_pred, digits=2) | Build a text report showing the main classification metrics. digits is number of digits for formatting output floating point values. Default value is 2 . |
Usage
seqeval supports the two evaluation modes. You can specify the following mode to each metrics:
- default
- strict
The default mode is compatible with conlleval. If you want to use the default mode, you don't need to specify it:
>>> from seqeval.metrics import accuracy_score
>>> from seqeval.metrics import classification_report
>>> from seqeval.metrics import f1_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> f1_score(y_true, y_pred)
0.50
>>> classification_report(y_true, y_pred)
precision recall f1-score support
MISC 0.00 0.00 0.00 1
PER 1.00 1.00 1.00 1
micro avg 0.50 0.50 0.50 2
macro avg 0.50 0.50 0.50 2
weighted avg 0.50 0.50 0.50 2
In strict mode, the inputs are evaluated according to the specified schema. The behavior of the strict mode is different from the default one which is designed to simulate conlleval. If you want to use the strict mode, please specify mode='strict'
and scheme
arguments at the same time:
>>> from seqeval.scheme import IOB2
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
precision recall f1-score support
MISC 0.00 0.00 0.00 1
PER 1.00 1.00 1.00 1
micro avg 0.50 0.50 0.50 2
macro avg 0.50 0.50 0.50 2
weighted avg 0.50 0.50 0.50 2
A minimum case to explain differences between the default and strict mode:
>>> from seqeval.metrics import classification_report
>>> from seqeval.scheme import IOB2
>>> y_true = [['B-NP', 'I-NP', 'O']]
>>> y_pred = [['I-NP', 'I-NP', 'O']]
>>> classification_report(y_true, y_pred)
precision recall f1-score support
NP 1.00 1.00 1.00 1
micro avg 1.00 1.00 1.00 1
macro avg 1.00 1.00 1.00 1
weighted avg 1.00 1.00 1.00 1
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
precision recall f1-score support
NP 0.00 0.00 0.00 1
micro avg 0.00 0.00 0.00 1
macro avg 0.00 0.00 0.00 1
weighted avg 0.00 0.00 0.00 1
Installation
To install seqeval, simply run:
pip install seqeval
License
Citation
@misc{seqeval,
title={{seqeval}: A Python framework for sequence labeling evaluation},
url={https://github.com/chakki-works/seqeval},
note={Software available from https://github.com/chakki-works/seqeval},
author={Hiroki Nakayama},
year={2018},
}
*Note that all licence references and agreements mentioned in the seqeval README section above
are relevant to that project's source code only.