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 supports following formats:
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. |
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)
0.50
>>> accuracy_score(y_true, y_pred)
0.80
>>> 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 2Seqeval provides a callback for Keras:
from seqeval.callbacks import F1Metrics
id2label = {0: '<PAD>', 1: 'B-LOC', 2: 'I-LOC'}
callbacks = [F1Metrics(id2label)]
model.fit(x, y, validation_data=(x_val, y_val), callbacks=callbacks)To install seqeval, simply run:
$ pip install seqeval[cpu]If you want to install seqeval on GPU environment, please run:
$ pip install seqeval[gpu]