%global _empty_manifest_terminate_build 0 Name: python-abdoTheBest Version: 1.0.0 Release: 1 Summary: A small example package License: MIT URL: https://github.com/gituser/example-pkg Source0: https://mirrors.nju.edu.cn/pypi/web/packages/55/f3/d948dfe7af193ea36a74b27f94c2c6d0490a6573a833982e13f3c207df03/abdoTheBest-1.0.0.tar.gz BuildArch: noarch %description # GeNN [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's. ## Features 1. Preprocessing: - Parsing txt, json, and csv files. - NLTK, regex and spacy tokenization support. - GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data. 2. Architectures and customization: - GPT2 with small, medium, and large variants. - LSTM and GRU, with variable size. - Variable number of layers and batches. - Dropout. 3. Text generation: - Random seed sampling from the n first tokens in all instances, or the most frequent token. - Top-K sampling for next token prediction with variable K. - Nucleus sampling for next token prediction with variable probability threshold. 4. Text Summarization: - All GPT2 variants can be trained to perform text summarization. ## Getting started ### How to install ```bash pip install genn ``` ### Prerequisites * PyTorch 1.4.0 ```bash pip install torch==1.4.0 ``` * Pytorch Transformers ```bash pip install pytorch_transformers ``` * NumPy ```bash pip install numpy ``` * fastText ```bash pip install fasttext ``` Use the package manager [pip](https://pypi.org/project/genn) to install genn. ## Usage ### Text Generation: ##### RNNs (You can switch LSTMGenerator with GRUGenerator: ```python from genn import Preprocessing, LSTMGenerator, GRUGenerator #LSTM example ds = Preprocessing("data.txt") gen = LSTMGenerator(ds, nLayers = 2, batchSize = 16, embSize = 64, lstmSize = 16, epochs = 20) #Train the model gen.run() # Generate 5 new documents print(gen.generate_document(5)) ``` ##### GPT2 Generator: ```python #GPT2 example gen = GPT2("data.txt", taskToken = "Movie:", epochs = 7, variant = "medium") #Train the model gen.run() #Generate 10 new documents print(gen.generate_document(10)) ``` ### Text Summarization: ##### GPT2 Summarizer: ```python #GPT2 Summarizer example from genn import GPT2Summarizer summ = GPT2Summarizer("data.txt", epochs=3, batch_size=8) #Train the model summ.run() #Create 5 summaries of a source document src_doc = "This is the source document to summarize" print(summ.summarize_document(n=5, setSeed = src_doc)) ``` #### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md). #### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md). #### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md). #### For more examples on how to use GPT2Summarizer, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_summarizer_examples.md). ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. ## License Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information. %package -n python3-abdoTheBest Summary: A small example package Provides: python-abdoTheBest BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-abdoTheBest # GeNN [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's. ## Features 1. Preprocessing: - Parsing txt, json, and csv files. - NLTK, regex and spacy tokenization support. - GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data. 2. Architectures and customization: - GPT2 with small, medium, and large variants. - LSTM and GRU, with variable size. - Variable number of layers and batches. - Dropout. 3. Text generation: - Random seed sampling from the n first tokens in all instances, or the most frequent token. - Top-K sampling for next token prediction with variable K. - Nucleus sampling for next token prediction with variable probability threshold. 4. Text Summarization: - All GPT2 variants can be trained to perform text summarization. ## Getting started ### How to install ```bash pip install genn ``` ### Prerequisites * PyTorch 1.4.0 ```bash pip install torch==1.4.0 ``` * Pytorch Transformers ```bash pip install pytorch_transformers ``` * NumPy ```bash pip install numpy ``` * fastText ```bash pip install fasttext ``` Use the package manager [pip](https://pypi.org/project/genn) to install genn. ## Usage ### Text Generation: ##### RNNs (You can switch LSTMGenerator with GRUGenerator: ```python from genn import Preprocessing, LSTMGenerator, GRUGenerator #LSTM example ds = Preprocessing("data.txt") gen = LSTMGenerator(ds, nLayers = 2, batchSize = 16, embSize = 64, lstmSize = 16, epochs = 20) #Train the model gen.run() # Generate 5 new documents print(gen.generate_document(5)) ``` ##### GPT2 Generator: ```python #GPT2 example gen = GPT2("data.txt", taskToken = "Movie:", epochs = 7, variant = "medium") #Train the model gen.run() #Generate 10 new documents print(gen.generate_document(10)) ``` ### Text Summarization: ##### GPT2 Summarizer: ```python #GPT2 Summarizer example from genn import GPT2Summarizer summ = GPT2Summarizer("data.txt", epochs=3, batch_size=8) #Train the model summ.run() #Create 5 summaries of a source document src_doc = "This is the source document to summarize" print(summ.summarize_document(n=5, setSeed = src_doc)) ``` #### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md). #### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md). #### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md). #### For more examples on how to use GPT2Summarizer, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_summarizer_examples.md). ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. ## License Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information. %package help Summary: Development documents and examples for abdoTheBest Provides: python3-abdoTheBest-doc %description help # GeNN [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) GeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's. ## Features 1. Preprocessing: - Parsing txt, json, and csv files. - NLTK, regex and spacy tokenization support. - GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data. 2. Architectures and customization: - GPT2 with small, medium, and large variants. - LSTM and GRU, with variable size. - Variable number of layers and batches. - Dropout. 3. Text generation: - Random seed sampling from the n first tokens in all instances, or the most frequent token. - Top-K sampling for next token prediction with variable K. - Nucleus sampling for next token prediction with variable probability threshold. 4. Text Summarization: - All GPT2 variants can be trained to perform text summarization. ## Getting started ### How to install ```bash pip install genn ``` ### Prerequisites * PyTorch 1.4.0 ```bash pip install torch==1.4.0 ``` * Pytorch Transformers ```bash pip install pytorch_transformers ``` * NumPy ```bash pip install numpy ``` * fastText ```bash pip install fasttext ``` Use the package manager [pip](https://pypi.org/project/genn) to install genn. ## Usage ### Text Generation: ##### RNNs (You can switch LSTMGenerator with GRUGenerator: ```python from genn import Preprocessing, LSTMGenerator, GRUGenerator #LSTM example ds = Preprocessing("data.txt") gen = LSTMGenerator(ds, nLayers = 2, batchSize = 16, embSize = 64, lstmSize = 16, epochs = 20) #Train the model gen.run() # Generate 5 new documents print(gen.generate_document(5)) ``` ##### GPT2 Generator: ```python #GPT2 example gen = GPT2("data.txt", taskToken = "Movie:", epochs = 7, variant = "medium") #Train the model gen.run() #Generate 10 new documents print(gen.generate_document(10)) ``` ### Text Summarization: ##### GPT2 Summarizer: ```python #GPT2 Summarizer example from genn import GPT2Summarizer summ = GPT2Summarizer("data.txt", epochs=3, batch_size=8) #Train the model summ.run() #Create 5 summaries of a source document src_doc = "This is the source document to summarize" print(summ.summarize_document(n=5, setSeed = src_doc)) ``` #### For more examples on how to use Preprocessing, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/preprocessing_examples.md). #### For more examples on how to use LSTMGenerator and GRUGenerator, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/generator_examples.md). #### For more examples on how to use GPT2, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_examples.md). #### For more examples on how to use GPT2Summarizer, please refer to [this file](https://github.com/FahedSabellioglu/genn/blob/master/gpt2_summarizer_examples.md). ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. ## License Distributed under the MIT License. See [LICENSE](https://github.com/FahedSabellioglu/genn/blob/master/LICENSE.txt) for more information. %prep %autosetup -n abdoTheBest-1.0.0 %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-abdoTheBest -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.0.0-1 - Package Spec generated