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|
%global _empty_manifest_terminate_build 0
Name: python-OpenNMT-tf
Version: 2.31.0
Release: 1
Summary: Neural machine translation and sequence learning using TensorFlow
License: MIT
URL: https://opennmt.net
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5d/8d/4b12ae213b41eb63e19b1d0a7f4e71287dc0ff5b3e13ef8b098fbd8ab169/OpenNMT-tf-2.31.0.tar.gz
BuildArch: noarch
Requires: python3-ctranslate2
Requires: python3-packaging
Requires: python3-pyonmttok
Requires: python3-pyyaml
Requires: python3-rouge
Requires: python3-sacrebleu
Requires: python3-tensorflow-addons
Requires: python3-myst-parser
Requires: python3-sphinx-rtd-theme
Requires: python3-sphinx
Requires: python3-tensorflow
Requires: python3-tensorflow-text
Requires: python3-black
Requires: python3-flake8
Requires: python3-isort
Requires: python3-parameterized
Requires: python3-pytest-cov
%description
OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
* automatic evaluation during the training
* multiple decoding strategy: greedy search, beam search, random sampling
* N-best rescoring
* gradient accumulation
* scheduled sampling
* checkpoint averaging
* ... and more!
*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
## Usage
OpenNMT-tf requires:
* Python 3.7 or above
* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, or 2.11
We recommend installing it with `pip`:
```bash
pip install --upgrade pip
pip install OpenNMT-tf
```
*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
### Command line
OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
* the **model** type
* the **parameters** described in a YAML file
* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
that are passed to the main script:
```
onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>
```
*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
### Library
OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
For example, the `Runner` class can be used to train and evaluate models with few lines of code:
```python
import opennmt
config = {
"model_dir": "/data/wmt-ende/checkpoints/",
"data": {
"source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"train_features_file": "/data/wmt-ende/train.en",
"train_labels_file": "/data/wmt-ende/train.de",
"eval_features_file": "/data/wmt-ende/valid.en",
"eval_labels_file": "/data/wmt-ende/valid.de",
}
}
model = opennmt.models.TransformerBase()
runner = opennmt.Runner(model, config, auto_config=True)
runner.train(num_devices=2, with_eval=True)
```
Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
```python
decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
initial_state = decoder.initial_state(
memory=memory, memory_sequence_length=memory_sequence_length
)
batch_size = tf.shape(memory)[0]
start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
decoding_result = decoder.dynamic_decode(
target_embedding,
start_ids=start_ids,
initial_state=initial_state,
decoding_strategy=opennmt.utils.BeamSearch(4),
)
```
More examples using OpenNMT-tf as a library can be found online:
* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
## Additional resources
* [Documentation](https://opennmt.net/OpenNMT-tf)
* [Forum](https://forum.opennmt.net)
* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)
%package -n python3-OpenNMT-tf
Summary: Neural machine translation and sequence learning using TensorFlow
Provides: python-OpenNMT-tf
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-OpenNMT-tf
OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
* automatic evaluation during the training
* multiple decoding strategy: greedy search, beam search, random sampling
* N-best rescoring
* gradient accumulation
* scheduled sampling
* checkpoint averaging
* ... and more!
*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
## Usage
OpenNMT-tf requires:
* Python 3.7 or above
* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, or 2.11
We recommend installing it with `pip`:
```bash
pip install --upgrade pip
pip install OpenNMT-tf
```
*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
### Command line
OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
* the **model** type
* the **parameters** described in a YAML file
* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
that are passed to the main script:
```
onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>
```
*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
### Library
OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
For example, the `Runner` class can be used to train and evaluate models with few lines of code:
```python
import opennmt
config = {
"model_dir": "/data/wmt-ende/checkpoints/",
"data": {
"source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"train_features_file": "/data/wmt-ende/train.en",
"train_labels_file": "/data/wmt-ende/train.de",
"eval_features_file": "/data/wmt-ende/valid.en",
"eval_labels_file": "/data/wmt-ende/valid.de",
}
}
model = opennmt.models.TransformerBase()
runner = opennmt.Runner(model, config, auto_config=True)
runner.train(num_devices=2, with_eval=True)
```
Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
```python
decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
initial_state = decoder.initial_state(
memory=memory, memory_sequence_length=memory_sequence_length
)
batch_size = tf.shape(memory)[0]
start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
decoding_result = decoder.dynamic_decode(
target_embedding,
start_ids=start_ids,
initial_state=initial_state,
decoding_strategy=opennmt.utils.BeamSearch(4),
)
```
More examples using OpenNMT-tf as a library can be found online:
* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
## Additional resources
* [Documentation](https://opennmt.net/OpenNMT-tf)
* [Forum](https://forum.opennmt.net)
* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)
%package help
Summary: Development documents and examples for OpenNMT-tf
Provides: python3-OpenNMT-tf-doc
%description help
OpenNMT-tf also implements most of the techniques commonly used to train and evaluate sequence models, such as:
* automatic evaluation during the training
* multiple decoding strategy: greedy search, beam search, random sampling
* N-best rescoring
* gradient accumulation
* scheduled sampling
* checkpoint averaging
* ... and more!
*See the [documentation](https://opennmt.net/OpenNMT-tf/) to learn how to use these features.*
## Usage
OpenNMT-tf requires:
* Python 3.7 or above
* TensorFlow 2.6, 2.7, 2.8, 2.9, 2.10, or 2.11
We recommend installing it with `pip`:
```bash
pip install --upgrade pip
pip install OpenNMT-tf
```
*See the [documentation](https://opennmt.net/OpenNMT-tf/installation.html) for more information.*
### Command line
OpenNMT-tf comes with several command line utilities to prepare data, train, and evaluate models.
For all tasks involving a model execution, OpenNMT-tf uses a unique entrypoint: `onmt-main`. A typical OpenNMT-tf run consists of 3 elements:
* the **model** type
* the **parameters** described in a YAML file
* the **run** type such as `train`, `eval`, `infer`, `export`, `score`, `average_checkpoints`, or `update_vocab`
that are passed to the main script:
```
onmt-main --model_type <model> --config <config_file.yml> --auto_config <run_type> <run_options>
```
*For more information and examples on how to use OpenNMT-tf, please visit [our documentation](https://opennmt.net/OpenNMT-tf).*
### Library
OpenNMT-tf also exposes [well-defined and stable APIs](https://opennmt.net/OpenNMT-tf/package/overview.html), from high-level training utilities to low-level model layers and dataset transformations.
For example, the `Runner` class can be used to train and evaluate models with few lines of code:
```python
import opennmt
config = {
"model_dir": "/data/wmt-ende/checkpoints/",
"data": {
"source_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"target_vocabulary": "/data/wmt-ende/joint-vocab.txt",
"train_features_file": "/data/wmt-ende/train.en",
"train_labels_file": "/data/wmt-ende/train.de",
"eval_features_file": "/data/wmt-ende/valid.en",
"eval_labels_file": "/data/wmt-ende/valid.de",
}
}
model = opennmt.models.TransformerBase()
runner = opennmt.Runner(model, config, auto_config=True)
runner.train(num_devices=2, with_eval=True)
```
Here is another example using OpenNMT-tf to run efficient beam search with a self-attentional decoder:
```python
decoder = opennmt.decoders.SelfAttentionDecoder(num_layers=6, vocab_size=32000)
initial_state = decoder.initial_state(
memory=memory, memory_sequence_length=memory_sequence_length
)
batch_size = tf.shape(memory)[0]
start_ids = tf.fill([batch_size], opennmt.START_OF_SENTENCE_ID)
decoding_result = decoder.dynamic_decode(
target_embedding,
start_ids=start_ids,
initial_state=initial_state,
decoding_strategy=opennmt.utils.BeamSearch(4),
)
```
More examples using OpenNMT-tf as a library can be found online:
* The directory [examples/library](https://github.com/OpenNMT/OpenNMT-tf/tree/master/examples/library) contains additional examples that use OpenNMT-tf as a library
* [nmt-wizard-docker](https://github.com/OpenNMT/nmt-wizard-docker) uses the high-level `opennmt.Runner` API to wrap OpenNMT-tf with a custom interface for training, translating, and serving
*For a complete overview of the APIs, see the [package documentation](https://opennmt.net/OpenNMT-tf/package/overview.html).*
## Additional resources
* [Documentation](https://opennmt.net/OpenNMT-tf)
* [Forum](https://forum.opennmt.net)
* [Gitter](https://gitter.im/OpenNMT/OpenNMT-tf)
%prep
%autosetup -n OpenNMT-tf-2.31.0
%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-OpenNMT-tf -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 2.31.0-1
- Package Spec generated
|
