%global _empty_manifest_terminate_build 0 Name: python-autonlp Version: 0.3.7 Release: 1 Summary: HuggingFace/AutoNLP License: Apache 2.0 URL: https://github.com/huggingface/autonlp Source0: https://mirrors.aliyun.com/pypi/web/packages/be/d3/e9843aa60363a0f21f5d02bbd71973694d74a75c187d9d67831af3f59cc4/autonlp-0.3.7.tar.gz BuildArch: noarch Requires: python3-loguru Requires: python3-requests Requires: python3-tqdm Requires: python3-prettytable Requires: python3-huggingface-hub Requires: python3-datasets Requires: python3-loguru Requires: python3-requests Requires: python3-tqdm Requires: python3-prettytable Requires: python3-huggingface-hub Requires: python3-datasets Requires: python3-black Requires: python3-isort Requires: python3-flake8 Requires: python3-pytest Requires: python3-loguru Requires: python3-requests Requires: python3-tqdm Requires: python3-prettytable Requires: python3-huggingface-hub Requires: python3-datasets Requires: python3-recommonmark Requires: python3-sphinx Requires: python3-sphinx-markdown-tables Requires: python3-sphinx-rtd-theme Requires: python3-sphinx-copybutton Requires: python3-loguru Requires: python3-requests Requires: python3-tqdm Requires: python3-prettytable Requires: python3-huggingface-hub Requires: python3-datasets Requires: python3-black Requires: python3-isort Requires: python3-flake8 %description # 🤗 AutoNLP AutoNLP: faster and easier training and deployments of SOTA NLP models ## Installation You can Install AutoNLP python package via PIP. Please note you will need python >= 3.7 for AutoNLP to work properly. pip install autonlp Please make sure that you have git lfs installed. Check out the instructions here: https://github.com/git-lfs/git-lfs/wiki/Installation ## Quick start - in the terminal Please take a look at [AutoNLP Documentation](https://huggingface.co/docs/autonlp/) for a list of supported tasks and languages. Note: AutoNLP is currently in beta release. To participate in the beta, just go to https://huggingface.co/autonlp and apply 🤗 First, create a project: ```bash autonlp login --api-key YOUR_HUGGING_FACE_API_TOKEN autonlp create_project --name sentiment_detection --language en --task binary_classification --max_models 5 ``` Upload files and start the training. You need a training and a validation split. Only CSV files are supported at the moment. ```bash # Train split autonlp upload --project sentiment_detection --split train \ --col_mapping review:text,sentiment:target \ --files ~/datasets/train.csv # Validation split autonlp upload --project sentiment_detection --split valid \ --col_mapping review:text,sentiment:target \ --files ~/datasets/valid.csv ``` Once the files are uploaded, you can start training the model: ```bash autonlp train --project sentiment_detection ``` Monitor the progress of your project. ```bash # Project progress autonlp project_info --name sentiment_detection # Model metrics autonlp metrics --project PROJECT_ID ``` ## Quick start - Python API Setting up: ```python from autonlp import AutoNLP client = AutoNLP() client.login(token="YOUR_HUGGING_FACE_API_TOKEN") ``` Creating a project and uploading files to it: ```python project = client.create_project(name="sentiment_detection", task="binary_classification", language="en", max_models=5) project.upload( filepaths=["/path/to/train.csv"], split="train", col_mapping={ "review": "text", "sentiment": "target", }) # also upload a validation with split="valid" ``` Start the training of your models: ```python project.train() ``` To monitor the progress of your training: ```python project.refresh() print(project) ``` After the training of your models has succeeded, you can retrieve the metrics for each model and test them with the 🤗 Inference API: ```python client.predict(project="sentiment_detection", model_id=42, input_text="i love autonlp") ``` or use command line: ```bash autonlp predict --project sentiment_detection --model_id 42 --sentence "i love autonlp" ``` ## How much do I have to pay? It's difficult to provide an exact answer to this question, however, we have an estimator that might help you. Just enter the number of samples and language and you will get an estimate. Please keep in mind that this is just an estimate and can easily over-estimate or under-estimate (we are actively working on this). ```bash autonlp estimate --num_train_samples 10000 --project_name sentiment_detection ``` %package -n python3-autonlp Summary: HuggingFace/AutoNLP Provides: python-autonlp BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-autonlp # 🤗 AutoNLP AutoNLP: faster and easier training and deployments of SOTA NLP models ## Installation You can Install AutoNLP python package via PIP. Please note you will need python >= 3.7 for AutoNLP to work properly. pip install autonlp Please make sure that you have git lfs installed. Check out the instructions here: https://github.com/git-lfs/git-lfs/wiki/Installation ## Quick start - in the terminal Please take a look at [AutoNLP Documentation](https://huggingface.co/docs/autonlp/) for a list of supported tasks and languages. Note: AutoNLP is currently in beta release. To participate in the beta, just go to https://huggingface.co/autonlp and apply 🤗 First, create a project: ```bash autonlp login --api-key YOUR_HUGGING_FACE_API_TOKEN autonlp create_project --name sentiment_detection --language en --task binary_classification --max_models 5 ``` Upload files and start the training. You need a training and a validation split. Only CSV files are supported at the moment. ```bash # Train split autonlp upload --project sentiment_detection --split train \ --col_mapping review:text,sentiment:target \ --files ~/datasets/train.csv # Validation split autonlp upload --project sentiment_detection --split valid \ --col_mapping review:text,sentiment:target \ --files ~/datasets/valid.csv ``` Once the files are uploaded, you can start training the model: ```bash autonlp train --project sentiment_detection ``` Monitor the progress of your project. ```bash # Project progress autonlp project_info --name sentiment_detection # Model metrics autonlp metrics --project PROJECT_ID ``` ## Quick start - Python API Setting up: ```python from autonlp import AutoNLP client = AutoNLP() client.login(token="YOUR_HUGGING_FACE_API_TOKEN") ``` Creating a project and uploading files to it: ```python project = client.create_project(name="sentiment_detection", task="binary_classification", language="en", max_models=5) project.upload( filepaths=["/path/to/train.csv"], split="train", col_mapping={ "review": "text", "sentiment": "target", }) # also upload a validation with split="valid" ``` Start the training of your models: ```python project.train() ``` To monitor the progress of your training: ```python project.refresh() print(project) ``` After the training of your models has succeeded, you can retrieve the metrics for each model and test them with the 🤗 Inference API: ```python client.predict(project="sentiment_detection", model_id=42, input_text="i love autonlp") ``` or use command line: ```bash autonlp predict --project sentiment_detection --model_id 42 --sentence "i love autonlp" ``` ## How much do I have to pay? It's difficult to provide an exact answer to this question, however, we have an estimator that might help you. Just enter the number of samples and language and you will get an estimate. Please keep in mind that this is just an estimate and can easily over-estimate or under-estimate (we are actively working on this). ```bash autonlp estimate --num_train_samples 10000 --project_name sentiment_detection ``` %package help Summary: Development documents and examples for autonlp Provides: python3-autonlp-doc %description help # 🤗 AutoNLP AutoNLP: faster and easier training and deployments of SOTA NLP models ## Installation You can Install AutoNLP python package via PIP. Please note you will need python >= 3.7 for AutoNLP to work properly. pip install autonlp Please make sure that you have git lfs installed. Check out the instructions here: https://github.com/git-lfs/git-lfs/wiki/Installation ## Quick start - in the terminal Please take a look at [AutoNLP Documentation](https://huggingface.co/docs/autonlp/) for a list of supported tasks and languages. Note: AutoNLP is currently in beta release. To participate in the beta, just go to https://huggingface.co/autonlp and apply 🤗 First, create a project: ```bash autonlp login --api-key YOUR_HUGGING_FACE_API_TOKEN autonlp create_project --name sentiment_detection --language en --task binary_classification --max_models 5 ``` Upload files and start the training. You need a training and a validation split. Only CSV files are supported at the moment. ```bash # Train split autonlp upload --project sentiment_detection --split train \ --col_mapping review:text,sentiment:target \ --files ~/datasets/train.csv # Validation split autonlp upload --project sentiment_detection --split valid \ --col_mapping review:text,sentiment:target \ --files ~/datasets/valid.csv ``` Once the files are uploaded, you can start training the model: ```bash autonlp train --project sentiment_detection ``` Monitor the progress of your project. ```bash # Project progress autonlp project_info --name sentiment_detection # Model metrics autonlp metrics --project PROJECT_ID ``` ## Quick start - Python API Setting up: ```python from autonlp import AutoNLP client = AutoNLP() client.login(token="YOUR_HUGGING_FACE_API_TOKEN") ``` Creating a project and uploading files to it: ```python project = client.create_project(name="sentiment_detection", task="binary_classification", language="en", max_models=5) project.upload( filepaths=["/path/to/train.csv"], split="train", col_mapping={ "review": "text", "sentiment": "target", }) # also upload a validation with split="valid" ``` Start the training of your models: ```python project.train() ``` To monitor the progress of your training: ```python project.refresh() print(project) ``` After the training of your models has succeeded, you can retrieve the metrics for each model and test them with the 🤗 Inference API: ```python client.predict(project="sentiment_detection", model_id=42, input_text="i love autonlp") ``` or use command line: ```bash autonlp predict --project sentiment_detection --model_id 42 --sentence "i love autonlp" ``` ## How much do I have to pay? It's difficult to provide an exact answer to this question, however, we have an estimator that might help you. Just enter the number of samples and language and you will get an estimate. Please keep in mind that this is just an estimate and can easily over-estimate or under-estimate (we are actively working on this). ```bash autonlp estimate --num_train_samples 10000 --project_name sentiment_detection ``` %prep %autosetup -n autonlp-0.3.7 %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-autonlp -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.3.7-1 - Package Spec generated