%global _empty_manifest_terminate_build 0
Name:		python-seqeval
Version:	1.2.2
Release:	1
Summary:	Testing framework for sequence labeling
License:	MIT
URL:		https://github.com/chakki-works/seqeval
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz
BuildArch:	noarch


%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 schemes:

- IOB1
- IOB2
- IOE1
- IOE2
- IOBES(only in strict mode)
- BILOU(only in strict mode)

and 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

seqeval supports the two evaluation modes. You can specify the following mode to each metrics:

- default
- strict

The default mode is compatible with [conlleval](https://www.clips.uantwerpen.be/conll2002/ner/bin/conlleval.txt). If you want to use the default mode, you don't need to specify it:

```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
>>> 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
weighted avg       0.50      0.50      0.50         2
```

In strict mode, the inputs are evaluated according to the specified schema. The behavior of the strict mode is different from the default one which is designed to simulate conlleval. If you want to use the strict mode, please specify `mode='strict'` and `scheme` arguments at the same time:

```python
>>> from seqeval.scheme import IOB2
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
              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
weighted avg       0.50      0.50      0.50         2
```

A minimum case to explain differences between the default and strict mode:

```python
>>> from seqeval.metrics import classification_report
>>> from seqeval.scheme import IOB2
>>> y_true = [['B-NP', 'I-NP', 'O']]
>>> y_pred = [['I-NP', 'I-NP', 'O']]
>>> classification_report(y_true, y_pred)
              precision    recall  f1-score   support
          NP       1.00      1.00      1.00         1
   micro avg       1.00      1.00      1.00         1
   macro avg       1.00      1.00      1.00         1
weighted avg       1.00      1.00      1.00         1
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
              precision    recall  f1-score   support
          NP       0.00      0.00      0.00         1
   micro avg       0.00      0.00      0.00         1
   macro avg       0.00      0.00      0.00         1
weighted avg       0.00      0.00      0.00         1
```

## Installation

To install seqeval, simply run:

```bash
pip install seqeval
```

## License

[MIT](https://github.com/chakki-works/seqeval/blob/master/LICENSE)

## Citation

```tex
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
```

%package -n python3-seqeval
Summary:	Testing framework for sequence labeling
Provides:	python-seqeval
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-seqeval
# 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 schemes:

- IOB1
- IOB2
- IOE1
- IOE2
- IOBES(only in strict mode)
- BILOU(only in strict mode)

and 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

seqeval supports the two evaluation modes. You can specify the following mode to each metrics:

- default
- strict

The default mode is compatible with [conlleval](https://www.clips.uantwerpen.be/conll2002/ner/bin/conlleval.txt). If you want to use the default mode, you don't need to specify it:

```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
>>> 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
weighted avg       0.50      0.50      0.50         2
```

In strict mode, the inputs are evaluated according to the specified schema. The behavior of the strict mode is different from the default one which is designed to simulate conlleval. If you want to use the strict mode, please specify `mode='strict'` and `scheme` arguments at the same time:

```python
>>> from seqeval.scheme import IOB2
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
              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
weighted avg       0.50      0.50      0.50         2
```

A minimum case to explain differences between the default and strict mode:

```python
>>> from seqeval.metrics import classification_report
>>> from seqeval.scheme import IOB2
>>> y_true = [['B-NP', 'I-NP', 'O']]
>>> y_pred = [['I-NP', 'I-NP', 'O']]
>>> classification_report(y_true, y_pred)
              precision    recall  f1-score   support
          NP       1.00      1.00      1.00         1
   micro avg       1.00      1.00      1.00         1
   macro avg       1.00      1.00      1.00         1
weighted avg       1.00      1.00      1.00         1
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
              precision    recall  f1-score   support
          NP       0.00      0.00      0.00         1
   micro avg       0.00      0.00      0.00         1
   macro avg       0.00      0.00      0.00         1
weighted avg       0.00      0.00      0.00         1
```

## Installation

To install seqeval, simply run:

```bash
pip install seqeval
```

## License

[MIT](https://github.com/chakki-works/seqeval/blob/master/LICENSE)

## Citation

```tex
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
```

%package help
Summary:	Development documents and examples for seqeval
Provides:	python3-seqeval-doc
%description help
# 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 schemes:

- IOB1
- IOB2
- IOE1
- IOE2
- IOBES(only in strict mode)
- BILOU(only in strict mode)

and 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

seqeval supports the two evaluation modes. You can specify the following mode to each metrics:

- default
- strict

The default mode is compatible with [conlleval](https://www.clips.uantwerpen.be/conll2002/ner/bin/conlleval.txt). If you want to use the default mode, you don't need to specify it:

```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
>>> 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
weighted avg       0.50      0.50      0.50         2
```

In strict mode, the inputs are evaluated according to the specified schema. The behavior of the strict mode is different from the default one which is designed to simulate conlleval. If you want to use the strict mode, please specify `mode='strict'` and `scheme` arguments at the same time:

```python
>>> from seqeval.scheme import IOB2
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
              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
weighted avg       0.50      0.50      0.50         2
```

A minimum case to explain differences between the default and strict mode:

```python
>>> from seqeval.metrics import classification_report
>>> from seqeval.scheme import IOB2
>>> y_true = [['B-NP', 'I-NP', 'O']]
>>> y_pred = [['I-NP', 'I-NP', 'O']]
>>> classification_report(y_true, y_pred)
              precision    recall  f1-score   support
          NP       1.00      1.00      1.00         1
   micro avg       1.00      1.00      1.00         1
   macro avg       1.00      1.00      1.00         1
weighted avg       1.00      1.00      1.00         1
>>> classification_report(y_true, y_pred, mode='strict', scheme=IOB2)
              precision    recall  f1-score   support
          NP       0.00      0.00      0.00         1
   micro avg       0.00      0.00      0.00         1
   macro avg       0.00      0.00      0.00         1
weighted avg       0.00      0.00      0.00         1
```

## Installation

To install seqeval, simply run:

```bash
pip install seqeval
```

## License

[MIT](https://github.com/chakki-works/seqeval/blob/master/LICENSE)

## Citation

```tex
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
```

%prep
%autosetup -n seqeval-1.2.2

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-seqeval -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.2-1
- Package Spec generated