%global _empty_manifest_terminate_build 0 Name: python-deeppavlov Version: 1.1.1 Release: 1 Summary: An open source library for building end-to-end dialog systems and training chatbots. License: Apache License, Version 2.0 URL: https://github.com/deeppavlov/DeepPavlov Source0: https://mirrors.nju.edu.cn/pypi/web/packages/63/ca/92187ea41f6a26200ba1bbdb46a4b894d088ba4cfc7ef22e716e5609e5ba/deeppavlov-1.1.1.tar.gz BuildArch: noarch Requires: python3-aio-pika Requires: python3-fastapi Requires: python3-filelock Requires: python3-nltk Requires: python3-numpy Requires: python3-overrides Requires: python3-pandas Requires: python3-prometheus-client Requires: python3-pydantic Requires: python3-pybind11 Requires: python3-requests Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-tqdm Requires: python3-uvicorn Requires: python3-sphinx-rtd-theme Requires: python3-nbsphinx Requires: python3-ipykernel Requires: python3-jinja2 Requires: python3-sphinx-copybutton Requires: python3-pandoc Requires: python3-ipython-genutils Requires: python3-sphinx Requires: python3-sphinx Requires: python3-boto3 Requires: python3-flake8 Requires: python3-pytest Requires: python3-pytest-instafail Requires: python3-pexpect %description [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deeppavlov/DeepPavlov/blob/master/LICENSE) ![Python 3.6, 3.7, 3.8, 3.9, 3.10](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-green.svg) [![Downloads](https://pepy.tech/badge/deeppavlov)](https://pepy.tech/project/deeppavlov) DeepPavlov is an open-source conversational AI library built on [PyTorch](https://pytorch.org/). DeepPavlov is designed for * development of production ready chat-bots and complex conversational systems, * research in the area of NLP and, particularly, of dialog systems. ## Quick Links * Demo [*demo.deeppavlov.ai*](https://demo.deeppavlov.ai/) * Documentation [*docs.deeppavlov.ai*](http://docs.deeppavlov.ai/) * Model List [*docs:features/*](http://docs.deeppavlov.ai/en/master/features/overview.html) * Contribution Guide [*docs:contribution_guide/*](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html) * Issues [*github/issues/*](https://github.com/deeppavlov/DeepPavlov/issues) * Forum [*forum.deeppavlov.ai*](https://forum.deeppavlov.ai/) * Blogs [*medium.com/deeppavlov*](https://medium.com/deeppavlov) * [Extended colab tutorials](https://github.com/deeppavlov/dp_tutorials) * Docker Hub [*hub.docker.com/u/deeppavlov/*](https://hub.docker.com/u/deeppavlov/) * Docker Images Documentation [*docs:docker-images/*](http://docs.deeppavlov.ai/en/master/intro/installation.html#docker-images) Please leave us [your feedback](https://forms.gle/i64fowQmiVhMMC7f9) on how we can improve the DeepPavlov framework. **Models** [Named Entity Recognition](http://docs.deeppavlov.ai/en/master/features/models/NER.html) | [Intent/Sentence Classification](http://docs.deeppavlov.ai/en/master/features/models/classifiers.html) | [Question Answering over Text (SQuAD)](http://docs.deeppavlov.ai/en/master/features/models/SQuAD.html) | [Knowledge Base Question Answering](http://docs.deeppavlov.ai/en/master/features/models/kbqa.html) [Sentence Similarity/Ranking](http://docs.deeppavlov.ai/en/master/features/models/neural_ranking.html) | [TF-IDF Ranking](http://docs.deeppavlov.ai/en/master/features/models/tfidf_ranking.html) [Automatic Spelling Correction](http://docs.deeppavlov.ai/en/master/features/models/spelling_correction.html) | [Entity Linking](http://docs.deeppavlov.ai/en/master/features/models/entity_linking.html) [Open Domain Questions Answering](http://docs.deeppavlov.ai/en/master/features/models/odqa.html) | [Russian SuperGLUE](http://docs.deeppavlov.ai/en/master/features/models/superglue.html) **Embeddings** [BERT embeddings for the Russian, Polish, Bulgarian, Czech, and informal English](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#bert) [ELMo embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#elmo) [FastText embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#fasttext) **Auto ML** [Tuning Models](http://docs.deeppavlov.ai/en/master/features/hypersearch.html) **Integrations** [REST API](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html) | [Socket API](http://docs.deeppavlov.ai/en/master/integrations/socket_api.html) [Amazon AWS](http://docs.deeppavlov.ai/en/master/integrations/aws_ec2.html) ## Installation 0. We support `Linux` platform, `Python 3.6`, `3.7`, `3.8`, `3.9` and `3.10` * **`Python 3.5` is not supported!** 1. Create and activate a virtual environment: * `Linux` ``` python -m venv env source ./env/bin/activate ``` 2. Install the package inside the environment: ``` pip install deeppavlov ``` ## QuickStart There is a bunch of great pre-trained NLP models in DeepPavlov. Each model is determined by its config file. List of models is available on [the doc page](http://docs.deeppavlov.ai/en/master/features/overview.html) in the `deeppavlov.configs` (Python): ```python from deeppavlov import configs ``` When you're decided on the model (+ config file), there are two ways to train, evaluate and infer it: * via [Command line interface (CLI)](https://github.com/deeppavlov/DeepPavlov/blob/master/#command-line-interface-cli) and * via [Python](https://github.com/deeppavlov/DeepPavlov/blob/master/#python). #### GPU requirements By default, DeepPavlov installs models requirements from PyPI. PyTorch from PyPI could not support your device CUDA capability. To run supported DeepPavlov models on GPU you should have [CUDA](https://developer.nvidia.com/cuda-toolkit) compatible with used GPU and [PyTorch version](https://github.com/deeppavlov/DeepPavlov/blob/master/deeppavlov/requirements/pytorch.txt) required by DeepPavlov models. See [docs](https://docs.deeppavlov.ai/en/master/intro/quick_start.html#using-gpu) for details. ### Command line interface (CLI) To get predictions from a model interactively through CLI, run ```bash python -m deeppavlov interact [-d] [-i] ``` * `-d` downloads required data - pretrained model files and embeddings (optional). * `-i` installs model requirements (optional). You can train it in the same simple way: ```bash python -m deeppavlov train [-d] [-i] ``` Dataset will be downloaded regardless of whether there was `-d` flag or not. To train on your own data you need to modify dataset reader path in the [train config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config). The data format is specified in the corresponding model doc page. There are even more actions you can perform with configs: ```bash python -m deeppavlov [-d] [-i] ``` * `` can be * `install` to install model requirements (same as `-i`), * `download` to download model's data (same as `-d`), * `train` to train the model on the data specified in the config file, * `evaluate` to calculate metrics on the same dataset, * `interact` to interact via CLI, * `riseapi` to run a REST API server (see [doc](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html)), * `predict` to get prediction for samples from *stdin* or from ** if `-f ` is specified. * `` specifies path (or name) of model's config file * `-d` downloads required data * `-i` installs model requirements ### Python To get predictions from a model interactively through Python, run ```python from deeppavlov import build_model model = build_model(, install=True, download=True) # get predictions for 'input_text1', 'input_text2' model(['input_text1', 'input_text2']) ``` where * `install=True` installs model requirements (optional), * `download=True` downloads required data from web - pretrained model files and embeddings (optional), * `` is model name (e.g. `'ner_ontonotes_bert_mult'`), path to the chosen model's config file (e.g. `"deeppavlov/configs/ner/ner_ontonotes_bert_mult.json"`), or `deeppavlov.configs` attribute (e.g. `deeppavlov.configs.ner.ner_ontonotes_bert_mult` without quotation marks). You can train it in the same simple way: ```python from deeppavlov import train_model model = train_model(, install=True, download=True) ``` To train on your own data you need to modify dataset reader path in the [train config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config). The data format is specified in the corresponding model doc page. You can also calculate metrics on the dataset specified in your config file: ```python from deeppavlov import evaluate_model model = evaluate_model(, install=True, download=True) ``` DeepPavlov also [allows](https://docs.deeppavlov.ai/en/master/features/python.html) to build a model from components for inference using Python. ## License DeepPavlov is Apache 2.0 - licensed. %package -n python3-deeppavlov Summary: An open source library for building end-to-end dialog systems and training chatbots. Provides: python-deeppavlov BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-deeppavlov [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deeppavlov/DeepPavlov/blob/master/LICENSE) ![Python 3.6, 3.7, 3.8, 3.9, 3.10](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-green.svg) [![Downloads](https://pepy.tech/badge/deeppavlov)](https://pepy.tech/project/deeppavlov) DeepPavlov is an open-source conversational AI library built on [PyTorch](https://pytorch.org/). DeepPavlov is designed for * development of production ready chat-bots and complex conversational systems, * research in the area of NLP and, particularly, of dialog systems. ## Quick Links * Demo [*demo.deeppavlov.ai*](https://demo.deeppavlov.ai/) * Documentation [*docs.deeppavlov.ai*](http://docs.deeppavlov.ai/) * Model List [*docs:features/*](http://docs.deeppavlov.ai/en/master/features/overview.html) * Contribution Guide [*docs:contribution_guide/*](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html) * Issues [*github/issues/*](https://github.com/deeppavlov/DeepPavlov/issues) * Forum [*forum.deeppavlov.ai*](https://forum.deeppavlov.ai/) * Blogs [*medium.com/deeppavlov*](https://medium.com/deeppavlov) * [Extended colab tutorials](https://github.com/deeppavlov/dp_tutorials) * Docker Hub [*hub.docker.com/u/deeppavlov/*](https://hub.docker.com/u/deeppavlov/) * Docker Images Documentation [*docs:docker-images/*](http://docs.deeppavlov.ai/en/master/intro/installation.html#docker-images) Please leave us [your feedback](https://forms.gle/i64fowQmiVhMMC7f9) on how we can improve the DeepPavlov framework. **Models** [Named Entity Recognition](http://docs.deeppavlov.ai/en/master/features/models/NER.html) | [Intent/Sentence Classification](http://docs.deeppavlov.ai/en/master/features/models/classifiers.html) | [Question Answering over Text (SQuAD)](http://docs.deeppavlov.ai/en/master/features/models/SQuAD.html) | [Knowledge Base Question Answering](http://docs.deeppavlov.ai/en/master/features/models/kbqa.html) [Sentence Similarity/Ranking](http://docs.deeppavlov.ai/en/master/features/models/neural_ranking.html) | [TF-IDF Ranking](http://docs.deeppavlov.ai/en/master/features/models/tfidf_ranking.html) [Automatic Spelling Correction](http://docs.deeppavlov.ai/en/master/features/models/spelling_correction.html) | [Entity Linking](http://docs.deeppavlov.ai/en/master/features/models/entity_linking.html) [Open Domain Questions Answering](http://docs.deeppavlov.ai/en/master/features/models/odqa.html) | [Russian SuperGLUE](http://docs.deeppavlov.ai/en/master/features/models/superglue.html) **Embeddings** [BERT embeddings for the Russian, Polish, Bulgarian, Czech, and informal English](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#bert) [ELMo embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#elmo) [FastText embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#fasttext) **Auto ML** [Tuning Models](http://docs.deeppavlov.ai/en/master/features/hypersearch.html) **Integrations** [REST API](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html) | [Socket API](http://docs.deeppavlov.ai/en/master/integrations/socket_api.html) [Amazon AWS](http://docs.deeppavlov.ai/en/master/integrations/aws_ec2.html) ## Installation 0. We support `Linux` platform, `Python 3.6`, `3.7`, `3.8`, `3.9` and `3.10` * **`Python 3.5` is not supported!** 1. Create and activate a virtual environment: * `Linux` ``` python -m venv env source ./env/bin/activate ``` 2. Install the package inside the environment: ``` pip install deeppavlov ``` ## QuickStart There is a bunch of great pre-trained NLP models in DeepPavlov. Each model is determined by its config file. List of models is available on [the doc page](http://docs.deeppavlov.ai/en/master/features/overview.html) in the `deeppavlov.configs` (Python): ```python from deeppavlov import configs ``` When you're decided on the model (+ config file), there are two ways to train, evaluate and infer it: * via [Command line interface (CLI)](https://github.com/deeppavlov/DeepPavlov/blob/master/#command-line-interface-cli) and * via [Python](https://github.com/deeppavlov/DeepPavlov/blob/master/#python). #### GPU requirements By default, DeepPavlov installs models requirements from PyPI. PyTorch from PyPI could not support your device CUDA capability. To run supported DeepPavlov models on GPU you should have [CUDA](https://developer.nvidia.com/cuda-toolkit) compatible with used GPU and [PyTorch version](https://github.com/deeppavlov/DeepPavlov/blob/master/deeppavlov/requirements/pytorch.txt) required by DeepPavlov models. See [docs](https://docs.deeppavlov.ai/en/master/intro/quick_start.html#using-gpu) for details. ### Command line interface (CLI) To get predictions from a model interactively through CLI, run ```bash python -m deeppavlov interact [-d] [-i] ``` * `-d` downloads required data - pretrained model files and embeddings (optional). * `-i` installs model requirements (optional). You can train it in the same simple way: ```bash python -m deeppavlov train [-d] [-i] ``` Dataset will be downloaded regardless of whether there was `-d` flag or not. To train on your own data you need to modify dataset reader path in the [train config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config). The data format is specified in the corresponding model doc page. There are even more actions you can perform with configs: ```bash python -m deeppavlov [-d] [-i] ``` * `` can be * `install` to install model requirements (same as `-i`), * `download` to download model's data (same as `-d`), * `train` to train the model on the data specified in the config file, * `evaluate` to calculate metrics on the same dataset, * `interact` to interact via CLI, * `riseapi` to run a REST API server (see [doc](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html)), * `predict` to get prediction for samples from *stdin* or from ** if `-f ` is specified. * `` specifies path (or name) of model's config file * `-d` downloads required data * `-i` installs model requirements ### Python To get predictions from a model interactively through Python, run ```python from deeppavlov import build_model model = build_model(, install=True, download=True) # get predictions for 'input_text1', 'input_text2' model(['input_text1', 'input_text2']) ``` where * `install=True` installs model requirements (optional), * `download=True` downloads required data from web - pretrained model files and embeddings (optional), * `` is model name (e.g. `'ner_ontonotes_bert_mult'`), path to the chosen model's config file (e.g. `"deeppavlov/configs/ner/ner_ontonotes_bert_mult.json"`), or `deeppavlov.configs` attribute (e.g. `deeppavlov.configs.ner.ner_ontonotes_bert_mult` without quotation marks). You can train it in the same simple way: ```python from deeppavlov import train_model model = train_model(, install=True, download=True) ``` To train on your own data you need to modify dataset reader path in the [train config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config). The data format is specified in the corresponding model doc page. You can also calculate metrics on the dataset specified in your config file: ```python from deeppavlov import evaluate_model model = evaluate_model(, install=True, download=True) ``` DeepPavlov also [allows](https://docs.deeppavlov.ai/en/master/features/python.html) to build a model from components for inference using Python. ## License DeepPavlov is Apache 2.0 - licensed. %package help Summary: Development documents and examples for deeppavlov Provides: python3-deeppavlov-doc %description help [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deeppavlov/DeepPavlov/blob/master/LICENSE) ![Python 3.6, 3.7, 3.8, 3.9, 3.10](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-green.svg) [![Downloads](https://pepy.tech/badge/deeppavlov)](https://pepy.tech/project/deeppavlov) DeepPavlov is an open-source conversational AI library built on [PyTorch](https://pytorch.org/). DeepPavlov is designed for * development of production ready chat-bots and complex conversational systems, * research in the area of NLP and, particularly, of dialog systems. ## Quick Links * Demo [*demo.deeppavlov.ai*](https://demo.deeppavlov.ai/) * Documentation [*docs.deeppavlov.ai*](http://docs.deeppavlov.ai/) * Model List [*docs:features/*](http://docs.deeppavlov.ai/en/master/features/overview.html) * Contribution Guide [*docs:contribution_guide/*](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html) * Issues [*github/issues/*](https://github.com/deeppavlov/DeepPavlov/issues) * Forum [*forum.deeppavlov.ai*](https://forum.deeppavlov.ai/) * Blogs [*medium.com/deeppavlov*](https://medium.com/deeppavlov) * [Extended colab tutorials](https://github.com/deeppavlov/dp_tutorials) * Docker Hub [*hub.docker.com/u/deeppavlov/*](https://hub.docker.com/u/deeppavlov/) * Docker Images Documentation [*docs:docker-images/*](http://docs.deeppavlov.ai/en/master/intro/installation.html#docker-images) Please leave us [your feedback](https://forms.gle/i64fowQmiVhMMC7f9) on how we can improve the DeepPavlov framework. **Models** [Named Entity Recognition](http://docs.deeppavlov.ai/en/master/features/models/NER.html) | [Intent/Sentence Classification](http://docs.deeppavlov.ai/en/master/features/models/classifiers.html) | [Question Answering over Text (SQuAD)](http://docs.deeppavlov.ai/en/master/features/models/SQuAD.html) | [Knowledge Base Question Answering](http://docs.deeppavlov.ai/en/master/features/models/kbqa.html) [Sentence Similarity/Ranking](http://docs.deeppavlov.ai/en/master/features/models/neural_ranking.html) | [TF-IDF Ranking](http://docs.deeppavlov.ai/en/master/features/models/tfidf_ranking.html) [Automatic Spelling Correction](http://docs.deeppavlov.ai/en/master/features/models/spelling_correction.html) | [Entity Linking](http://docs.deeppavlov.ai/en/master/features/models/entity_linking.html) [Open Domain Questions Answering](http://docs.deeppavlov.ai/en/master/features/models/odqa.html) | [Russian SuperGLUE](http://docs.deeppavlov.ai/en/master/features/models/superglue.html) **Embeddings** [BERT embeddings for the Russian, Polish, Bulgarian, Czech, and informal English](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#bert) [ELMo embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#elmo) [FastText embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#fasttext) **Auto ML** [Tuning Models](http://docs.deeppavlov.ai/en/master/features/hypersearch.html) **Integrations** [REST API](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html) | [Socket API](http://docs.deeppavlov.ai/en/master/integrations/socket_api.html) [Amazon AWS](http://docs.deeppavlov.ai/en/master/integrations/aws_ec2.html) ## Installation 0. We support `Linux` platform, `Python 3.6`, `3.7`, `3.8`, `3.9` and `3.10` * **`Python 3.5` is not supported!** 1. Create and activate a virtual environment: * `Linux` ``` python -m venv env source ./env/bin/activate ``` 2. Install the package inside the environment: ``` pip install deeppavlov ``` ## QuickStart There is a bunch of great pre-trained NLP models in DeepPavlov. Each model is determined by its config file. List of models is available on [the doc page](http://docs.deeppavlov.ai/en/master/features/overview.html) in the `deeppavlov.configs` (Python): ```python from deeppavlov import configs ``` When you're decided on the model (+ config file), there are two ways to train, evaluate and infer it: * via [Command line interface (CLI)](https://github.com/deeppavlov/DeepPavlov/blob/master/#command-line-interface-cli) and * via [Python](https://github.com/deeppavlov/DeepPavlov/blob/master/#python). #### GPU requirements By default, DeepPavlov installs models requirements from PyPI. PyTorch from PyPI could not support your device CUDA capability. To run supported DeepPavlov models on GPU you should have [CUDA](https://developer.nvidia.com/cuda-toolkit) compatible with used GPU and [PyTorch version](https://github.com/deeppavlov/DeepPavlov/blob/master/deeppavlov/requirements/pytorch.txt) required by DeepPavlov models. See [docs](https://docs.deeppavlov.ai/en/master/intro/quick_start.html#using-gpu) for details. ### Command line interface (CLI) To get predictions from a model interactively through CLI, run ```bash python -m deeppavlov interact [-d] [-i] ``` * `-d` downloads required data - pretrained model files and embeddings (optional). * `-i` installs model requirements (optional). You can train it in the same simple way: ```bash python -m deeppavlov train [-d] [-i] ``` Dataset will be downloaded regardless of whether there was `-d` flag or not. To train on your own data you need to modify dataset reader path in the [train config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config). The data format is specified in the corresponding model doc page. There are even more actions you can perform with configs: ```bash python -m deeppavlov [-d] [-i] ``` * `` can be * `install` to install model requirements (same as `-i`), * `download` to download model's data (same as `-d`), * `train` to train the model on the data specified in the config file, * `evaluate` to calculate metrics on the same dataset, * `interact` to interact via CLI, * `riseapi` to run a REST API server (see [doc](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html)), * `predict` to get prediction for samples from *stdin* or from ** if `-f ` is specified. * `` specifies path (or name) of model's config file * `-d` downloads required data * `-i` installs model requirements ### Python To get predictions from a model interactively through Python, run ```python from deeppavlov import build_model model = build_model(, install=True, download=True) # get predictions for 'input_text1', 'input_text2' model(['input_text1', 'input_text2']) ``` where * `install=True` installs model requirements (optional), * `download=True` downloads required data from web - pretrained model files and embeddings (optional), * `` is model name (e.g. `'ner_ontonotes_bert_mult'`), path to the chosen model's config file (e.g. `"deeppavlov/configs/ner/ner_ontonotes_bert_mult.json"`), or `deeppavlov.configs` attribute (e.g. `deeppavlov.configs.ner.ner_ontonotes_bert_mult` without quotation marks). You can train it in the same simple way: ```python from deeppavlov import train_model model = train_model(, install=True, download=True) ``` To train on your own data you need to modify dataset reader path in the [train config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config). The data format is specified in the corresponding model doc page. You can also calculate metrics on the dataset specified in your config file: ```python from deeppavlov import evaluate_model model = evaluate_model(, install=True, download=True) ``` DeepPavlov also [allows](https://docs.deeppavlov.ai/en/master/features/python.html) to build a model from components for inference using Python. ## License DeepPavlov is Apache 2.0 - licensed. %prep %autosetup -n deeppavlov-1.1.1 %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-deeppavlov -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed Apr 12 2023 Python_Bot - 1.1.1-1 - Package Spec generated