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Testing framework for sequence labeling

Project description

# 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 formats:
* IOB1
* IOB2
* IOE1
* IOE2

and supports 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
Behold, the power of seqeval:

>>> 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)
>>> accuracy_score(y_true, y_pred)
>>> 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

avg / total 0.50 0.50 0.50 2

## Installation
To install seqeval, simply run:

$ pip install seqeval

## Requirement
* numpy >= 1.14.0

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