%global _empty_manifest_terminate_build 0
Name:		python-ml-datasets
Version:	0.2.0
Release:	1
Summary:	Machine Learning dataset loaders
License:	MIT
URL:		https://github.com/explosion/ml-datasets
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3c/a8/149700bd6087fbffdbe85d32a7587f497cf45c432864d0000eef6bad1020/ml_datasets-0.2.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-tqdm
Requires:	python3-srsly
Requires:	python3-catalogue

%description
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>

# Machine learning dataset loaders for testing and examples

Loaders for various machine learning datasets for testing and example scripts.
Previously in `thinc.extra.datasets`.

[![PyPi Version](https://img.shields.io/pypi/v/ml-datasets.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/ml-datasets)

## Setup and installation

The package can be installed via pip:

```bash
pip install ml-datasets
```

## Loaders

Loaders can be imported directly or used via their string name (which is useful if they're set via command line arguments). Some loaders may take arguments – see the source for details.

```python
# Import directly
from ml_datasets import imdb
train_data, dev_data = imdb()
```

```python
# Load via registry
from ml_datasets import loaders
imdb_loader = loaders.get("imdb")
train_data, dev_data = imdb_loader()
```

### Available loaders

#### NLP datasets

| ID / Function        | Description                                  | NLP task                                  | From URL |
| -------------------- | -------------------------------------------- | ----------------------------------------- | :------: |
| `imdb`               | IMDB sentiment dataset                       | Binary classification: sentiment analysis |    ✓     |
| `dbpedia`            | DBPedia ontology dataset                     | Multi-class single-label classification   |    ✓     |
| `cmu`                | CMU movie genres dataset                     | Multi-class, multi-label classification   |    ✓     |
| `quora_questions`    | Duplicate Quora questions dataset            | Detecting duplicate questions             |    ✓     |
| `reuters`            | Reuters dataset (texts not included)         | Multi-class multi-label classification    |    ✓     |
| `snli`               | Stanford Natural Language Inference corpus   | Recognizing textual entailment            |    ✓     |
| `stack_exchange`     | Stack Exchange dataset                       | Question Answering                        |          |
| `ud_ancora_pos_tags` | Universal Dependencies Spanish AnCora corpus | POS tagging                               |    ✓     |
| `ud_ewtb_pos_tags`   | Universal Dependencies English EWT corpus    | POS tagging                               |    ✓     |
| `wikiner`            | WikiNER data                                 | Named entity recognition                  |          |

#### Other ML datasets

| ID / Function | Description | ML task           | From URL |
| ------------- | ----------- | ----------------- | :------: |
| `mnist`       | MNIST data  | Image recognition |    ✓     |

### Dataset details

#### IMDB

Each instance contains the text of a movie review, and a sentiment expressed as `0` or `1`.

```python
train_data, dev_data = ml_datasets.imdb()
for text, annot in train_data[0:5]:
    print(f"Review: {text}")
    print(f"Sentiment: {annot}")
```

- Download URL: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
- Citation: [Andrew L. Maas et al., 2011](https://www.aclweb.org/anthology/P11-1015/)

| Property            | Training         | Dev              |
| ------------------- | ---------------- | ---------------- |
| # Instances         | 25000            | 25000            |
| Label values        | {`0`, `1`}       | {`0`, `1`}       |
| Labels per instance | Single           | Single           |
| Label distribution  | Balanced (50/50) | Balanced (50/50) |

#### DBPedia

Each instance contains an ontological description, and a classification into one of the 14 distinct labels.

```python
train_data, dev_data = ml_datasets.dbpedia()
for text, annot in train_data[0:5]:
    print(f"Text: {text}")
    print(f"Category: {annot}")
```

- Download URL: [Via fast.ai](https://course.fast.ai/datasets)
- Original citation: [Xiang Zhang et al., 2015](https://arxiv.org/abs/1509.01626)

| Property            | Training | Dev      |
| ------------------- | -------- | -------- |
| # Instances         | 560000   | 70000    |
| Label values        | `1`-`14` | `1`-`14` |
| Labels per instance | Single   | Single   |
| Label distribution  | Balanced | Balanced |

#### CMU

Each instance contains a movie description, and a classification into a list of appropriate genres.

```python
train_data, dev_data = ml_datasets.cmu()
for text, annot in train_data[0:5]:
    print(f"Text: {text}")
    print(f"Genres: {annot}")
```

- Download URL: [http://www.cs.cmu.edu/~ark/personas/](http://www.cs.cmu.edu/~ark/personas/)
- Original citation: [David Bamman et al., 2013](https://www.aclweb.org/anthology/P13-1035/)

| Property            | Training                                                                                      | Dev |
| ------------------- | --------------------------------------------------------------------------------------------- | --- |
| # Instances         | 41793                                                                                         | 0   |
| Label values        | 363 different genres                                                                          | -   |
| Labels per instance | Multiple                                                                                      | -   |
| Label distribution  | Imbalanced: 147 labels with less than 20 examples, while `Drama` occurs more than 19000 times | -   |

#### Quora

```python
train_data, dev_data = ml_datasets.quora_questions()
for questions, annot in train_data[0:50]:
    q1, q2 = questions
    print(f"Question 1: {q1}")
    print(f"Question 2: {q2}")
    print(f"Similarity: {annot}")
```

Each instance contains two quora questions, and a label indicating whether or not they are duplicates (`0`: no, `1`: yes).
The ground-truth labels contain some amount of noise: they are not guaranteed to be perfect.

- Download URL: [http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv](http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv)
- Original citation: [Kornél Csernai et al., 2017](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs)

| Property            | Training                  | Dev                       |
| ------------------- | ------------------------- | ------------------------- |
| # Instances         | 363859                    | 40429                     |
| Label values        | {`0`, `1`}                | {`0`, `1`}                |
| Labels per instance | Single                    | Single                    |
| Label distribution  | Imbalanced: 63% label `0` | Imbalanced: 63% label `0` |

### Registering loaders

Loaders can be registered externally using the `loaders` registry as a decorator. For example:

```python
@ml_datasets.loaders("my_custom_loader")
def my_custom_loader():
    return load_some_data()

assert "my_custom_loader" in ml_datasets.loaders
```




%package -n python3-ml-datasets
Summary:	Machine Learning dataset loaders
Provides:	python-ml-datasets
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-ml-datasets
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>

# Machine learning dataset loaders for testing and examples

Loaders for various machine learning datasets for testing and example scripts.
Previously in `thinc.extra.datasets`.

[![PyPi Version](https://img.shields.io/pypi/v/ml-datasets.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/ml-datasets)

## Setup and installation

The package can be installed via pip:

```bash
pip install ml-datasets
```

## Loaders

Loaders can be imported directly or used via their string name (which is useful if they're set via command line arguments). Some loaders may take arguments – see the source for details.

```python
# Import directly
from ml_datasets import imdb
train_data, dev_data = imdb()
```

```python
# Load via registry
from ml_datasets import loaders
imdb_loader = loaders.get("imdb")
train_data, dev_data = imdb_loader()
```

### Available loaders

#### NLP datasets

| ID / Function        | Description                                  | NLP task                                  | From URL |
| -------------------- | -------------------------------------------- | ----------------------------------------- | :------: |
| `imdb`               | IMDB sentiment dataset                       | Binary classification: sentiment analysis |    ✓     |
| `dbpedia`            | DBPedia ontology dataset                     | Multi-class single-label classification   |    ✓     |
| `cmu`                | CMU movie genres dataset                     | Multi-class, multi-label classification   |    ✓     |
| `quora_questions`    | Duplicate Quora questions dataset            | Detecting duplicate questions             |    ✓     |
| `reuters`            | Reuters dataset (texts not included)         | Multi-class multi-label classification    |    ✓     |
| `snli`               | Stanford Natural Language Inference corpus   | Recognizing textual entailment            |    ✓     |
| `stack_exchange`     | Stack Exchange dataset                       | Question Answering                        |          |
| `ud_ancora_pos_tags` | Universal Dependencies Spanish AnCora corpus | POS tagging                               |    ✓     |
| `ud_ewtb_pos_tags`   | Universal Dependencies English EWT corpus    | POS tagging                               |    ✓     |
| `wikiner`            | WikiNER data                                 | Named entity recognition                  |          |

#### Other ML datasets

| ID / Function | Description | ML task           | From URL |
| ------------- | ----------- | ----------------- | :------: |
| `mnist`       | MNIST data  | Image recognition |    ✓     |

### Dataset details

#### IMDB

Each instance contains the text of a movie review, and a sentiment expressed as `0` or `1`.

```python
train_data, dev_data = ml_datasets.imdb()
for text, annot in train_data[0:5]:
    print(f"Review: {text}")
    print(f"Sentiment: {annot}")
```

- Download URL: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
- Citation: [Andrew L. Maas et al., 2011](https://www.aclweb.org/anthology/P11-1015/)

| Property            | Training         | Dev              |
| ------------------- | ---------------- | ---------------- |
| # Instances         | 25000            | 25000            |
| Label values        | {`0`, `1`}       | {`0`, `1`}       |
| Labels per instance | Single           | Single           |
| Label distribution  | Balanced (50/50) | Balanced (50/50) |

#### DBPedia

Each instance contains an ontological description, and a classification into one of the 14 distinct labels.

```python
train_data, dev_data = ml_datasets.dbpedia()
for text, annot in train_data[0:5]:
    print(f"Text: {text}")
    print(f"Category: {annot}")
```

- Download URL: [Via fast.ai](https://course.fast.ai/datasets)
- Original citation: [Xiang Zhang et al., 2015](https://arxiv.org/abs/1509.01626)

| Property            | Training | Dev      |
| ------------------- | -------- | -------- |
| # Instances         | 560000   | 70000    |
| Label values        | `1`-`14` | `1`-`14` |
| Labels per instance | Single   | Single   |
| Label distribution  | Balanced | Balanced |

#### CMU

Each instance contains a movie description, and a classification into a list of appropriate genres.

```python
train_data, dev_data = ml_datasets.cmu()
for text, annot in train_data[0:5]:
    print(f"Text: {text}")
    print(f"Genres: {annot}")
```

- Download URL: [http://www.cs.cmu.edu/~ark/personas/](http://www.cs.cmu.edu/~ark/personas/)
- Original citation: [David Bamman et al., 2013](https://www.aclweb.org/anthology/P13-1035/)

| Property            | Training                                                                                      | Dev |
| ------------------- | --------------------------------------------------------------------------------------------- | --- |
| # Instances         | 41793                                                                                         | 0   |
| Label values        | 363 different genres                                                                          | -   |
| Labels per instance | Multiple                                                                                      | -   |
| Label distribution  | Imbalanced: 147 labels with less than 20 examples, while `Drama` occurs more than 19000 times | -   |

#### Quora

```python
train_data, dev_data = ml_datasets.quora_questions()
for questions, annot in train_data[0:50]:
    q1, q2 = questions
    print(f"Question 1: {q1}")
    print(f"Question 2: {q2}")
    print(f"Similarity: {annot}")
```

Each instance contains two quora questions, and a label indicating whether or not they are duplicates (`0`: no, `1`: yes).
The ground-truth labels contain some amount of noise: they are not guaranteed to be perfect.

- Download URL: [http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv](http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv)
- Original citation: [Kornél Csernai et al., 2017](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs)

| Property            | Training                  | Dev                       |
| ------------------- | ------------------------- | ------------------------- |
| # Instances         | 363859                    | 40429                     |
| Label values        | {`0`, `1`}                | {`0`, `1`}                |
| Labels per instance | Single                    | Single                    |
| Label distribution  | Imbalanced: 63% label `0` | Imbalanced: 63% label `0` |

### Registering loaders

Loaders can be registered externally using the `loaders` registry as a decorator. For example:

```python
@ml_datasets.loaders("my_custom_loader")
def my_custom_loader():
    return load_some_data()

assert "my_custom_loader" in ml_datasets.loaders
```




%package help
Summary:	Development documents and examples for ml-datasets
Provides:	python3-ml-datasets-doc
%description help
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>

# Machine learning dataset loaders for testing and examples

Loaders for various machine learning datasets for testing and example scripts.
Previously in `thinc.extra.datasets`.

[![PyPi Version](https://img.shields.io/pypi/v/ml-datasets.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/ml-datasets)

## Setup and installation

The package can be installed via pip:

```bash
pip install ml-datasets
```

## Loaders

Loaders can be imported directly or used via their string name (which is useful if they're set via command line arguments). Some loaders may take arguments – see the source for details.

```python
# Import directly
from ml_datasets import imdb
train_data, dev_data = imdb()
```

```python
# Load via registry
from ml_datasets import loaders
imdb_loader = loaders.get("imdb")
train_data, dev_data = imdb_loader()
```

### Available loaders

#### NLP datasets

| ID / Function        | Description                                  | NLP task                                  | From URL |
| -------------------- | -------------------------------------------- | ----------------------------------------- | :------: |
| `imdb`               | IMDB sentiment dataset                       | Binary classification: sentiment analysis |    ✓     |
| `dbpedia`            | DBPedia ontology dataset                     | Multi-class single-label classification   |    ✓     |
| `cmu`                | CMU movie genres dataset                     | Multi-class, multi-label classification   |    ✓     |
| `quora_questions`    | Duplicate Quora questions dataset            | Detecting duplicate questions             |    ✓     |
| `reuters`            | Reuters dataset (texts not included)         | Multi-class multi-label classification    |    ✓     |
| `snli`               | Stanford Natural Language Inference corpus   | Recognizing textual entailment            |    ✓     |
| `stack_exchange`     | Stack Exchange dataset                       | Question Answering                        |          |
| `ud_ancora_pos_tags` | Universal Dependencies Spanish AnCora corpus | POS tagging                               |    ✓     |
| `ud_ewtb_pos_tags`   | Universal Dependencies English EWT corpus    | POS tagging                               |    ✓     |
| `wikiner`            | WikiNER data                                 | Named entity recognition                  |          |

#### Other ML datasets

| ID / Function | Description | ML task           | From URL |
| ------------- | ----------- | ----------------- | :------: |
| `mnist`       | MNIST data  | Image recognition |    ✓     |

### Dataset details

#### IMDB

Each instance contains the text of a movie review, and a sentiment expressed as `0` or `1`.

```python
train_data, dev_data = ml_datasets.imdb()
for text, annot in train_data[0:5]:
    print(f"Review: {text}")
    print(f"Sentiment: {annot}")
```

- Download URL: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
- Citation: [Andrew L. Maas et al., 2011](https://www.aclweb.org/anthology/P11-1015/)

| Property            | Training         | Dev              |
| ------------------- | ---------------- | ---------------- |
| # Instances         | 25000            | 25000            |
| Label values        | {`0`, `1`}       | {`0`, `1`}       |
| Labels per instance | Single           | Single           |
| Label distribution  | Balanced (50/50) | Balanced (50/50) |

#### DBPedia

Each instance contains an ontological description, and a classification into one of the 14 distinct labels.

```python
train_data, dev_data = ml_datasets.dbpedia()
for text, annot in train_data[0:5]:
    print(f"Text: {text}")
    print(f"Category: {annot}")
```

- Download URL: [Via fast.ai](https://course.fast.ai/datasets)
- Original citation: [Xiang Zhang et al., 2015](https://arxiv.org/abs/1509.01626)

| Property            | Training | Dev      |
| ------------------- | -------- | -------- |
| # Instances         | 560000   | 70000    |
| Label values        | `1`-`14` | `1`-`14` |
| Labels per instance | Single   | Single   |
| Label distribution  | Balanced | Balanced |

#### CMU

Each instance contains a movie description, and a classification into a list of appropriate genres.

```python
train_data, dev_data = ml_datasets.cmu()
for text, annot in train_data[0:5]:
    print(f"Text: {text}")
    print(f"Genres: {annot}")
```

- Download URL: [http://www.cs.cmu.edu/~ark/personas/](http://www.cs.cmu.edu/~ark/personas/)
- Original citation: [David Bamman et al., 2013](https://www.aclweb.org/anthology/P13-1035/)

| Property            | Training                                                                                      | Dev |
| ------------------- | --------------------------------------------------------------------------------------------- | --- |
| # Instances         | 41793                                                                                         | 0   |
| Label values        | 363 different genres                                                                          | -   |
| Labels per instance | Multiple                                                                                      | -   |
| Label distribution  | Imbalanced: 147 labels with less than 20 examples, while `Drama` occurs more than 19000 times | -   |

#### Quora

```python
train_data, dev_data = ml_datasets.quora_questions()
for questions, annot in train_data[0:50]:
    q1, q2 = questions
    print(f"Question 1: {q1}")
    print(f"Question 2: {q2}")
    print(f"Similarity: {annot}")
```

Each instance contains two quora questions, and a label indicating whether or not they are duplicates (`0`: no, `1`: yes).
The ground-truth labels contain some amount of noise: they are not guaranteed to be perfect.

- Download URL: [http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv](http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv)
- Original citation: [Kornél Csernai et al., 2017](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs)

| Property            | Training                  | Dev                       |
| ------------------- | ------------------------- | ------------------------- |
| # Instances         | 363859                    | 40429                     |
| Label values        | {`0`, `1`}                | {`0`, `1`}                |
| Labels per instance | Single                    | Single                    |
| Label distribution  | Imbalanced: 63% label `0` | Imbalanced: 63% label `0` |

### Registering loaders

Loaders can be registered externally using the `loaders` registry as a decorator. For example:

```python
@ml_datasets.loaders("my_custom_loader")
def my_custom_loader():
    return load_some_data()

assert "my_custom_loader" in ml_datasets.loaders
```




%prep
%autosetup -n ml-datasets-0.2.0

%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-ml-datasets -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.0-1
- Package Spec generated