automatic import of python-autonlp

3 files changed, 431 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..7daae0b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/autonlp-0.3.7.tar.gz
diff --git a/python-autonlp.spec b/python-autonlp.spec
new file mode 100644
index 0000000..09932d9
--- /dev/null
+++ b/python-autonlp.spec
@@ -0,0 +1,429 @@
+%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.nju.edu.cn/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
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..1885fdf
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+37cbfc7da9850cbc4558e9d6b4055985  autonlp-0.3.7.tar.gz