semvec/pypi-packages huggingface dataset:
ngram_graphs 0.1.13
An igraph or networkx based ngram graph library for NLP
License: MIT
Keywords: ngrams graph NLP

Library for ngram graph comparison based on papers from George Giannakopoulos and Vangelis Karkaletsis. The library uses igraph to create directed and weighted graphs from documents and provides means to compare them using the distance metrics defined in 'Giannakopoulos, George & Karkaletsis, Vangelis. (2009). N-gram graphs: Representing documents and document sets in summary system evaluation. '
test-travis-neko 0.0.2
Testing framework for sequence labeling
License: MIT

# 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)