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|
%global _empty_manifest_terminate_build 0
Name: python-smile-datasets
Version: 0.0.6
Release: 1
Summary: La**S**t **mile** datasets: Use `tf.data` to solve the last mile data loading problem for tensorflow.
License: Apache Software License
URL: https://github.com/luozhouyang/smile-datasets
Source0: https://mirrors.aliyun.com/pypi/web/packages/72/28/bdee1d8fadf99f99daea01b0a09546932871fb995e82838c6c59e5c94ffb/smile-datasets-0.0.6.tar.gz
BuildArch: noarch
Requires: python3-tokenizers
Requires: python3-tensorflow
%description
# smile-datasets
[](https://badge.fury.io/py/smile-datasets)
[](https://badge.fury.io/py/smile-datasets)
La**S**t **mile** Datasets: Use `tf.data` to solve the last mile data loading problem for tensorflow.
If you want to load public datasets, try:
* [tensorflow/datasets](https://github.com/tensorflow/datasets)
* [huggingface/datasets](https://github.com/huggingface/datasets)
If you want to load local, personal dataset with minimized boilerplate, use **Smile Dataset**!
## Support Matrix
| task | supported | core abstractions |
|:-----------------------|:-----------|:------------------|
| question answering | [x] | `ExampleForQuestionAnswering`, `DatasetForQuestionAnswering`, `DatapipeForQuestionAnswering`|
| masked language model | [x] | `ExampleForMaskedLanguageModel`, `DatasetForMaskedLanguageModel`, `DatapipeForMaskedLanguageModel`|
| sequence classification| [x] | `ExampleForSequenceClassification`, `DatasetForSequenceClassification`, `DatapipeForSequenceClassification`|
| token classification | [x] | `ExampleForTokenClassification`, `DatasetForTokenClassification`, `DatapipeForTokenClassification`|
| unsupervised simcse | [x] | `ExampleForUnsupervisedSimCSE`, `DatasetForUnsupervisedSimCSE`, `DatapipeForUnsupervisedSimCSE`|
| supervised simcse | [x] | `ExampleForSupervisedSimCSE`, `DatasetForSupervisedSimCSE`, `DatapipeForSupervisedSimCSE`|
| hard negative simcse | [x] | `ExampleForHardNegativeSimCSE`, `DatasetForHardNegativeSimCSE`, `DatapipeForHardNegativeSimCSE`|
## Usage
All datapipes for different tasks has the same interface.
Here is an example for question answering task, but you can use datapipe the same way for other tasks.
### Example for Question Answering
```python
from smile_datasets import DatasetForQuestionAnswering, DatapipeForQuestionAnswering
# each line is a JSON {"sequece": "我喜欢自然语言处理(NLP)"}
train_input_jsonl_files = ["data/train.jsonl"]
train_dataset = DatapipeForQuestionAnswering.from_jsonl_files(
input_files=train_input_jsonl_files,
vocab_file="bert/vocab.txt",
batch_size=32,
)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
For maximum flexibility, you can always subclass `DatasetForQuestionAnswering` to load your dataset, just like `torch.utils.data.Dataset`:
```python
from smile_datasets import DatasetForQuestionAnswering, DatapipeForQuestionAnswering, ParserForQuestionAnswering
class DuReaderDatasetForQuestionAnswering(DatasetForQuestionAnswering):
"""Dataset reader for DuReader dataset."""
def __init__(self, input_files, vocab_file, subset="rubost", **kwargs) -> None:
super().__init__()
self.parser = ParserForQuestionAnswering(tokenizer=None, vocab_file=vocab_file, **kwargs)
if subset == "rubost":
self.instances = list(readers.read_dureader_rubost(input_files, **kwargs))
else:
self.instances = list(readers.read_dureader_checklist(input_files, **kwargs))
self.examples = []
for instance in self.instances:
e = self.parser.parse(instance)
if not e:
continue
self.examples.append(e)
def __len__(self):
return len(self.examples)
def __getitem__(self, index) -> ExampleForQuestionAnswering:
return self.examples[index]
dataset = DuReaderDatasetForQuestionAnswering(input_files=["data/trian.jsonl"], vocab_file="bert/vocab.txt")
train_dataset = DatapipeForQuestionAnswering.from_dataset(dataset, batch_size=32)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
For better performance, you can convert `dataset` to `tfrecord` ahead of time, and then build datapipe from tfrecord files directly:
```python
# save dataset in tfrecord format
dataset.save_tfrecord(output_files="data/train.tfrecord")
# build datapipe from tfrecord files
train_dataset = DatapipeForQuestionAnswering.from_tfrecord_files(input_files="data/train.tfrecord", batch_size=32)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
%package -n python3-smile-datasets
Summary: La**S**t **mile** datasets: Use `tf.data` to solve the last mile data loading problem for tensorflow.
Provides: python-smile-datasets
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-smile-datasets
# smile-datasets
[](https://badge.fury.io/py/smile-datasets)
[](https://badge.fury.io/py/smile-datasets)
La**S**t **mile** Datasets: Use `tf.data` to solve the last mile data loading problem for tensorflow.
If you want to load public datasets, try:
* [tensorflow/datasets](https://github.com/tensorflow/datasets)
* [huggingface/datasets](https://github.com/huggingface/datasets)
If you want to load local, personal dataset with minimized boilerplate, use **Smile Dataset**!
## Support Matrix
| task | supported | core abstractions |
|:-----------------------|:-----------|:------------------|
| question answering | [x] | `ExampleForQuestionAnswering`, `DatasetForQuestionAnswering`, `DatapipeForQuestionAnswering`|
| masked language model | [x] | `ExampleForMaskedLanguageModel`, `DatasetForMaskedLanguageModel`, `DatapipeForMaskedLanguageModel`|
| sequence classification| [x] | `ExampleForSequenceClassification`, `DatasetForSequenceClassification`, `DatapipeForSequenceClassification`|
| token classification | [x] | `ExampleForTokenClassification`, `DatasetForTokenClassification`, `DatapipeForTokenClassification`|
| unsupervised simcse | [x] | `ExampleForUnsupervisedSimCSE`, `DatasetForUnsupervisedSimCSE`, `DatapipeForUnsupervisedSimCSE`|
| supervised simcse | [x] | `ExampleForSupervisedSimCSE`, `DatasetForSupervisedSimCSE`, `DatapipeForSupervisedSimCSE`|
| hard negative simcse | [x] | `ExampleForHardNegativeSimCSE`, `DatasetForHardNegativeSimCSE`, `DatapipeForHardNegativeSimCSE`|
## Usage
All datapipes for different tasks has the same interface.
Here is an example for question answering task, but you can use datapipe the same way for other tasks.
### Example for Question Answering
```python
from smile_datasets import DatasetForQuestionAnswering, DatapipeForQuestionAnswering
# each line is a JSON {"sequece": "我喜欢自然语言处理(NLP)"}
train_input_jsonl_files = ["data/train.jsonl"]
train_dataset = DatapipeForQuestionAnswering.from_jsonl_files(
input_files=train_input_jsonl_files,
vocab_file="bert/vocab.txt",
batch_size=32,
)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
For maximum flexibility, you can always subclass `DatasetForQuestionAnswering` to load your dataset, just like `torch.utils.data.Dataset`:
```python
from smile_datasets import DatasetForQuestionAnswering, DatapipeForQuestionAnswering, ParserForQuestionAnswering
class DuReaderDatasetForQuestionAnswering(DatasetForQuestionAnswering):
"""Dataset reader for DuReader dataset."""
def __init__(self, input_files, vocab_file, subset="rubost", **kwargs) -> None:
super().__init__()
self.parser = ParserForQuestionAnswering(tokenizer=None, vocab_file=vocab_file, **kwargs)
if subset == "rubost":
self.instances = list(readers.read_dureader_rubost(input_files, **kwargs))
else:
self.instances = list(readers.read_dureader_checklist(input_files, **kwargs))
self.examples = []
for instance in self.instances:
e = self.parser.parse(instance)
if not e:
continue
self.examples.append(e)
def __len__(self):
return len(self.examples)
def __getitem__(self, index) -> ExampleForQuestionAnswering:
return self.examples[index]
dataset = DuReaderDatasetForQuestionAnswering(input_files=["data/trian.jsonl"], vocab_file="bert/vocab.txt")
train_dataset = DatapipeForQuestionAnswering.from_dataset(dataset, batch_size=32)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
For better performance, you can convert `dataset` to `tfrecord` ahead of time, and then build datapipe from tfrecord files directly:
```python
# save dataset in tfrecord format
dataset.save_tfrecord(output_files="data/train.tfrecord")
# build datapipe from tfrecord files
train_dataset = DatapipeForQuestionAnswering.from_tfrecord_files(input_files="data/train.tfrecord", batch_size=32)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
%package help
Summary: Development documents and examples for smile-datasets
Provides: python3-smile-datasets-doc
%description help
# smile-datasets
[](https://badge.fury.io/py/smile-datasets)
[](https://badge.fury.io/py/smile-datasets)
La**S**t **mile** Datasets: Use `tf.data` to solve the last mile data loading problem for tensorflow.
If you want to load public datasets, try:
* [tensorflow/datasets](https://github.com/tensorflow/datasets)
* [huggingface/datasets](https://github.com/huggingface/datasets)
If you want to load local, personal dataset with minimized boilerplate, use **Smile Dataset**!
## Support Matrix
| task | supported | core abstractions |
|:-----------------------|:-----------|:------------------|
| question answering | [x] | `ExampleForQuestionAnswering`, `DatasetForQuestionAnswering`, `DatapipeForQuestionAnswering`|
| masked language model | [x] | `ExampleForMaskedLanguageModel`, `DatasetForMaskedLanguageModel`, `DatapipeForMaskedLanguageModel`|
| sequence classification| [x] | `ExampleForSequenceClassification`, `DatasetForSequenceClassification`, `DatapipeForSequenceClassification`|
| token classification | [x] | `ExampleForTokenClassification`, `DatasetForTokenClassification`, `DatapipeForTokenClassification`|
| unsupervised simcse | [x] | `ExampleForUnsupervisedSimCSE`, `DatasetForUnsupervisedSimCSE`, `DatapipeForUnsupervisedSimCSE`|
| supervised simcse | [x] | `ExampleForSupervisedSimCSE`, `DatasetForSupervisedSimCSE`, `DatapipeForSupervisedSimCSE`|
| hard negative simcse | [x] | `ExampleForHardNegativeSimCSE`, `DatasetForHardNegativeSimCSE`, `DatapipeForHardNegativeSimCSE`|
## Usage
All datapipes for different tasks has the same interface.
Here is an example for question answering task, but you can use datapipe the same way for other tasks.
### Example for Question Answering
```python
from smile_datasets import DatasetForQuestionAnswering, DatapipeForQuestionAnswering
# each line is a JSON {"sequece": "我喜欢自然语言处理(NLP)"}
train_input_jsonl_files = ["data/train.jsonl"]
train_dataset = DatapipeForQuestionAnswering.from_jsonl_files(
input_files=train_input_jsonl_files,
vocab_file="bert/vocab.txt",
batch_size=32,
)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
For maximum flexibility, you can always subclass `DatasetForQuestionAnswering` to load your dataset, just like `torch.utils.data.Dataset`:
```python
from smile_datasets import DatasetForQuestionAnswering, DatapipeForQuestionAnswering, ParserForQuestionAnswering
class DuReaderDatasetForQuestionAnswering(DatasetForQuestionAnswering):
"""Dataset reader for DuReader dataset."""
def __init__(self, input_files, vocab_file, subset="rubost", **kwargs) -> None:
super().__init__()
self.parser = ParserForQuestionAnswering(tokenizer=None, vocab_file=vocab_file, **kwargs)
if subset == "rubost":
self.instances = list(readers.read_dureader_rubost(input_files, **kwargs))
else:
self.instances = list(readers.read_dureader_checklist(input_files, **kwargs))
self.examples = []
for instance in self.instances:
e = self.parser.parse(instance)
if not e:
continue
self.examples.append(e)
def __len__(self):
return len(self.examples)
def __getitem__(self, index) -> ExampleForQuestionAnswering:
return self.examples[index]
dataset = DuReaderDatasetForQuestionAnswering(input_files=["data/trian.jsonl"], vocab_file="bert/vocab.txt")
train_dataset = DatapipeForQuestionAnswering.from_dataset(dataset, batch_size=32)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
For better performance, you can convert `dataset` to `tfrecord` ahead of time, and then build datapipe from tfrecord files directly:
```python
# save dataset in tfrecord format
dataset.save_tfrecord(output_files="data/train.tfrecord")
# build datapipe from tfrecord files
train_dataset = DatapipeForQuestionAnswering.from_tfrecord_files(input_files="data/train.tfrecord", batch_size=32)
# check dataset
print(next(iter(train_dataset)))
# model = build_keras_model(...)
# model.compile(...)
# train model
model.fit(train_dataset, callbacks=[...])
```
%prep
%autosetup -n smile-datasets-0.0.6
%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-smile-datasets -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.6-1
- Package Spec generated
|
