# seqeval

**Repository Path**: honkor/seqeval

## Basic Information

- **Project Name**: seqeval
- **Description**: https://github.com/chakki-works/seqeval
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-06-16
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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](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)