%global _empty_manifest_terminate_build 0 Name: python-tfkit Version: 0.8.20 Release: 1 Summary: Transformers kit - Multi-task QA/Tagging/Multi-label Multi-Class Classification/Generation with BERT/ALBERT/T5/BERT License: Apache URL: https://github.com/voidful/TFkit Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e4/2c/3fcd1b598bf9df1fa113f9a6b962ac9be7cf4f2ba61683c1c9b7b775c238/tfkit-0.8.20.tar.gz BuildArch: noarch Requires: python3-transformers Requires: python3-tensorboard Requires: python3-tensorboardX Requires: python3-torch Requires: python3-matplotlib Requires: python3-nlp2 Requires: python3-tqdm Requires: python3-inquirer Requires: python3-numpy Requires: python3-scipy Requires: python3-pytorch-crf Requires: python3-sentencepiece Requires: python3-pandas Requires: python3-accelerate Requires: python3-joblib Requires: python3-scikit-learn Requires: python3-editdistance %description




PyPI Download Build Last Commit CodeFactor Visitor

## What is it TFKit is a tool kit mainly for language generation. It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. ## Task Supported With transformer models - BERT/ALBERT/T5/BART...... | | | |-|-| | Text Generation | :memo: seq2seq language model | | Text Generation | :pen: causal language model | | Text Generation | :printer: once generation model / once generation model with ctc loss | | Text Generation | :pencil: onebyone generation model | # Getting Started Learn more from the [document](https://voidful.github.io/TFkit/). ## How To Use ### Step 0: Install Simple installation from PyPI ```bash pip install git+https://github.com/voidful/TFkit.git@refactor-dataset ``` ### Step 1: Prepare dataset in csv format [Task format](https://voidful.tech/TFkit/tasks/) ``` input, target ``` ### Step 2: Train model ```bash tfkit-train \ --task clas \ --config xlm-roberta-base \ --train training_data.csv \ --test testing_data.csv \ --lr 4e-5 \ --maxlen 384 \ --epoch 10 \ --savedir roberta_sentiment_classificer ``` ### Step 3: Evaluate ```bash tfkit-eval \ --task roberta_sentiment_classificer/1.pt \ --metric clas \ --valid testing_data.csv ``` ## Advanced features
Multi-task training ```bash tfkit-train \ --task clas clas \ --config xlm-roberta-base \ --train training_data_taskA.csv training_data_taskB.csv \ --test testing_data_taskA.csv testing_data_taskB.csv \ --lr 4e-5 \ --maxlen 384 \ --epoch 10 \ --savedir roberta_sentiment_classificer_multi_task ```
## Not maintained task Due to time constraints, the following tasks are temporarily not supported | | | |-|-| | Classification | :label: multi-class and multi-label classification | | Question Answering | :page_with_curl: extractive qa | | Question Answering | :radio_button: multiple-choice qa | | Tagging | :eye_speech_bubble: sequence level tagging / sequence level with crf | | Self-supervise Learning | :diving_mask: mask language model | ## Supplement - [transformers models list](https://huggingface.co/models): you can find any pretrained models here - [nlprep](https://github.com/voidful/NLPrep): download and preprocessing data in one line - [nlp2go](https://github.com/voidful/nlp2go): create demo api as quickly as possible. ## Contributing Thanks for your interest.There are many ways to contribute to this project. Get started [here](https://github.com/voidful/tfkit/blob/master/CONTRIBUTING.md). ## License ![PyPI - License](https://img.shields.io/github/license/voidful/tfkit) * [License](https://github.com/voidful/tfkit/blob/master/LICENSE) ## Icons reference Icons modify from Freepik from www.flaticon.com Icons modify from Nikita Golubev from www.flaticon.com %package -n python3-tfkit Summary: Transformers kit - Multi-task QA/Tagging/Multi-label Multi-Class Classification/Generation with BERT/ALBERT/T5/BERT Provides: python-tfkit BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-tfkit




PyPI Download Build Last Commit CodeFactor Visitor

## What is it TFKit is a tool kit mainly for language generation. It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. ## Task Supported With transformer models - BERT/ALBERT/T5/BART...... | | | |-|-| | Text Generation | :memo: seq2seq language model | | Text Generation | :pen: causal language model | | Text Generation | :printer: once generation model / once generation model with ctc loss | | Text Generation | :pencil: onebyone generation model | # Getting Started Learn more from the [document](https://voidful.github.io/TFkit/). ## How To Use ### Step 0: Install Simple installation from PyPI ```bash pip install git+https://github.com/voidful/TFkit.git@refactor-dataset ``` ### Step 1: Prepare dataset in csv format [Task format](https://voidful.tech/TFkit/tasks/) ``` input, target ``` ### Step 2: Train model ```bash tfkit-train \ --task clas \ --config xlm-roberta-base \ --train training_data.csv \ --test testing_data.csv \ --lr 4e-5 \ --maxlen 384 \ --epoch 10 \ --savedir roberta_sentiment_classificer ``` ### Step 3: Evaluate ```bash tfkit-eval \ --task roberta_sentiment_classificer/1.pt \ --metric clas \ --valid testing_data.csv ``` ## Advanced features
Multi-task training ```bash tfkit-train \ --task clas clas \ --config xlm-roberta-base \ --train training_data_taskA.csv training_data_taskB.csv \ --test testing_data_taskA.csv testing_data_taskB.csv \ --lr 4e-5 \ --maxlen 384 \ --epoch 10 \ --savedir roberta_sentiment_classificer_multi_task ```
## Not maintained task Due to time constraints, the following tasks are temporarily not supported | | | |-|-| | Classification | :label: multi-class and multi-label classification | | Question Answering | :page_with_curl: extractive qa | | Question Answering | :radio_button: multiple-choice qa | | Tagging | :eye_speech_bubble: sequence level tagging / sequence level with crf | | Self-supervise Learning | :diving_mask: mask language model | ## Supplement - [transformers models list](https://huggingface.co/models): you can find any pretrained models here - [nlprep](https://github.com/voidful/NLPrep): download and preprocessing data in one line - [nlp2go](https://github.com/voidful/nlp2go): create demo api as quickly as possible. ## Contributing Thanks for your interest.There are many ways to contribute to this project. Get started [here](https://github.com/voidful/tfkit/blob/master/CONTRIBUTING.md). ## License ![PyPI - License](https://img.shields.io/github/license/voidful/tfkit) * [License](https://github.com/voidful/tfkit/blob/master/LICENSE) ## Icons reference Icons modify from Freepik from www.flaticon.com Icons modify from Nikita Golubev from www.flaticon.com %package help Summary: Development documents and examples for tfkit Provides: python3-tfkit-doc %description help




PyPI Download Build Last Commit CodeFactor Visitor

## What is it TFKit is a tool kit mainly for language generation. It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. ## Task Supported With transformer models - BERT/ALBERT/T5/BART...... | | | |-|-| | Text Generation | :memo: seq2seq language model | | Text Generation | :pen: causal language model | | Text Generation | :printer: once generation model / once generation model with ctc loss | | Text Generation | :pencil: onebyone generation model | # Getting Started Learn more from the [document](https://voidful.github.io/TFkit/). ## How To Use ### Step 0: Install Simple installation from PyPI ```bash pip install git+https://github.com/voidful/TFkit.git@refactor-dataset ``` ### Step 1: Prepare dataset in csv format [Task format](https://voidful.tech/TFkit/tasks/) ``` input, target ``` ### Step 2: Train model ```bash tfkit-train \ --task clas \ --config xlm-roberta-base \ --train training_data.csv \ --test testing_data.csv \ --lr 4e-5 \ --maxlen 384 \ --epoch 10 \ --savedir roberta_sentiment_classificer ``` ### Step 3: Evaluate ```bash tfkit-eval \ --task roberta_sentiment_classificer/1.pt \ --metric clas \ --valid testing_data.csv ``` ## Advanced features
Multi-task training ```bash tfkit-train \ --task clas clas \ --config xlm-roberta-base \ --train training_data_taskA.csv training_data_taskB.csv \ --test testing_data_taskA.csv testing_data_taskB.csv \ --lr 4e-5 \ --maxlen 384 \ --epoch 10 \ --savedir roberta_sentiment_classificer_multi_task ```
## Not maintained task Due to time constraints, the following tasks are temporarily not supported | | | |-|-| | Classification | :label: multi-class and multi-label classification | | Question Answering | :page_with_curl: extractive qa | | Question Answering | :radio_button: multiple-choice qa | | Tagging | :eye_speech_bubble: sequence level tagging / sequence level with crf | | Self-supervise Learning | :diving_mask: mask language model | ## Supplement - [transformers models list](https://huggingface.co/models): you can find any pretrained models here - [nlprep](https://github.com/voidful/NLPrep): download and preprocessing data in one line - [nlp2go](https://github.com/voidful/nlp2go): create demo api as quickly as possible. ## Contributing Thanks for your interest.There are many ways to contribute to this project. Get started [here](https://github.com/voidful/tfkit/blob/master/CONTRIBUTING.md). ## License ![PyPI - License](https://img.shields.io/github/license/voidful/tfkit) * [License](https://github.com/voidful/tfkit/blob/master/LICENSE) ## Icons reference Icons modify from Freepik from www.flaticon.com Icons modify from Nikita Golubev from www.flaticon.com %prep %autosetup -n tfkit-0.8.20 %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-tfkit -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 0.8.20-1 - Package Spec generated