Metadata-Version: 2.1
Name: seqeval
Version: 0.0.12
Summary: Testing framework for sequence labeling
Home-page: https://github.com/chakki-works/seqeval
Author: Hironsan
Author-email: hiroki.nakayama.py@gmail.com
License: MIT
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](https://www.clips.uantwerpen.be/conll2002/ner/bin/conlleval.txt),
        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
        * IOBES
        
        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:
        
        ```python
        >>> 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         2
        ```
        
        ### Keras Callback
        
        Seqeval provides a callback for Keras:
        
        ```python
        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)
        ```
        
        ## Installation
        To install seqeval, simply run:
        
        ```
        $ pip install seqeval[cpu]
        ```
        
        If you want to install seqeval on GPU environment, please run:
        
        ```bash
        $ pip install seqeval[gpu]
        ```
        
        ## Requirement
        
        * numpy >= 1.14.0
        * tensorflow(optional)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/markdown
Provides-Extra: cpu
Provides-Extra: gpu
