software stringlengths 4 47 | repo_name stringclasses 2
values | readme_url stringlengths 59 105 | content stringclasses 28
values | plan stringclasses 6
values | steps stringclasses 29
values | optional_steps stringclasses 3
values | extra_info_optional stringclasses 10
values |
|---|---|---|---|---|---|---|---|
vcr-video-representation-for-contextual | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/oronnir/VCR/main/README.md | null | null | null | null | null |
ensuring-trustworthy-and-ethical-behaviour-in | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/AAAI-DISIM-UnivAQ/DALI/master/README.md | ## Installation
**OS X & Linux:**
1. To download and install SICStus Prolog (it is needed), follow the instructions at https://sicstus.sics.se/download4.html.
2. Then, you can download DALI and test it by running an example DALI MAS:
```sh
git clone https://github.com/AAAI-DISIM-UnivAQ/DALI.git
cd DALI/Examples/adva... | binary, source | [plan binary]>>step1. follow the instructions at https://sicstus.sics.se/download4.html.
[plan source]>>step 2. download DALI. step3. test it by running an example DALI MAS:
```sh
git clone https://github.com/AAAI-DISIM-UnivAQ/DALI.git
cd DALI/Examples/advanced
bash startmas.sh
``` | **Windows:**
1. To download and install SICStus Prolog (it is needed), follow the instructions at https://sicstus.sics.se/download4.html.
2. Then, you can download DALI from https://github.com/AAAI-DISIM-UnivAQ/DALI.git.
3. Unzip the repository, go to the folder "DALI/Examples/basic", and test if DALI works by duble... | You will see different windows opening:
Prolog LINDA server (active_server_wi.pl)
Prolog FIPA client (active_user_wi.pl)
1 instance of DALI metaintepreter for each agent (active_dali_wi.pl) |
synthesizing-sentiment-controlled-feedback | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/MIntelligence-Group/CMFeed/main/README.md | null | null | null | null | null |
only-the-curve-shape-matters-training | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/cfeng783/GTT/main/README.md | ## Getting Started
#### Install dependencies (with python 3.10)
```shell
pip install -r requirements.txt
``` | source | [plan source]>> [INCOMPLETE] step1. Install dependencies with ```pip install -r requirements.txt``` | null | null |
from-uncertainty-to-precision-enhancing | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/fer-agathe/calibration_binary_classifier/main/README.md | null | null | null | null | null |
stochastic-gradient-flow-dynamics-of-test | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/rodsveiga/sgf_dyn/main/README.md | null | null | null | null | null |
accuracy-of-textfooler-black-box-adversarial | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/zero-one-loss/wordcnn01/main/LICENSE | null | null | null | null | null |
differentially-private-decentralized-learning-1 | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/totilas/DPrandomwalk/main/README.md | null | null | null | null | null |
aydiv-adaptable-yielding-3d-object-detection | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/sanjay-810/AYDIV2/main/README.md | ### **Installation**
1. Prepare for the running environment.
You can use the docker image provided by [`OpenPCDet`](https://github.com/open-mmlab/OpenPCDet). Our experiments are based on the
docker provided by Voxel-R-CNN and we use NVIDIA Tesla V100 to train our Aydiv.
2. Prepare for the data.
Convert... | source,docker | [plan source]>> step1. Prepare for the running environment. step2. prepare for the data:```cd Aydiv python depth_to_lidar.py ```
[plan docker]>> step1. You can use the docker image provided by [`OpenPCDet`](https://github.com/open-mmlab/OpenPCDet) | null | null |
cartesian-atomic-cluster-expansion-for | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/BingqingCheng/cace/main/README.md | ## Installation
Please refer to the `setup.py` file for installation instructions. | source | [plan source]>>[INCOMPLETE] step1. please refer to the `setup.py` file for installation instructions. | null | null |
teller-a-trustworthy-framework-for | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/less-and-less-bugs/Trust_TELLER/main/README.md | ## Getting Started
Step 1: Download the dataset folder from onedrive by [data.zip](https://portland-my.sharepoint.com/:u:/g/personal/liuhui3-c_my_cityu_edu_hk/EfApQlFP3PhFjUW4527STo0BALMdP16zs-HPMNgwQVFWsA?e=zoHlW2). Unzip this folder into the project directory. You can find four orginal datasets, pre-processed dat... | binary | [plan binary]>> step1: Download the dataset folder from onedrive by https://portland-my.sharepoint.com/:u:/g/personal/liuhui3-c_my_cityu_edu_hk/EfApQlFP3PhFjUW4527STo0BALMdP16zs-HPMNgwQVFWsA?e=zoHlW2.
step2. Unzip this folder into the project directory.
step3. Place you OpenAI key into the file named api_key.txt.
```... | null | null |
continuous-time-radar-inertial-and-lidar | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/utiasASRL/steam_icp/master/README.md | ## Installation
Clone this repository and its submodules.
We use docker to install dependencies The recommended way to build the docker image is
```bash
docker build -t steam_icp \
--build-arg USERID=$(id -u) \
--build-arg GROUPID=$(id -g) \
--build-arg USERNAME=$(whoami) \
--build-arg HOMEDIR=${HOME} .
```
... | source | [plan source]>> step1. clone this repository and its submodules. step2. Use docker to install dependencies ```docker build -t steam_icp \
--build-arg USERID=$(id -u) \
--build-arg GROUPID=$(id -g) \
--build-arg USERNAME=$(whoami) \
--build-arg HOMEDIR=${HOME} .
```
step3. mount the code, dataset, and output ... | null | null |
towards-a-thermodynamical-deep-learning | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/fedezocco/ThermoVisMedRob/main/README.md | null | null | null | null | null |
robust-parameter-fitting-to-realistic-network | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/PFischbeck/parameter-fitting-experiments/main/Readme.md | # Installation
- Make sure you have Python, Pip and R installed.
- Checkout this repository
- Install the python dependencies with
```
pip3 install -r requirements.txt
```
- Install the `pygirgs` package at https://github.com/PFischbeck/pygirgs
- Install the R dependencies (used for plots) with
```
R -e 'install.p... | source | [plan source]>> step1. Make sure you have Python, Pip and R installed.
step2. Checkout this repository
step3. Install the python dependencies with
```
pip3 install -r requirements.txt
```
step4. Install the `pygirgs` package at https://github.com/PFischbeck/pygirgs
step5. Install the R dependencies (used for plots) wit... | step7. Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself. | null |
get-tok-a-genai-enriched-multimodal-tiktok | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/gabbypinto/GET-Tok-Peru/main/README.md | ## Installation
pip install -r requirements.txt
*Note: I did not us a virtual environment so the packages in the requirements.txt file are probably not reflective of all the packages used in this project. If some issues pop up please don't hesitate to email me at: gpinto@usc.edu* | packagemanager | [plan packagemanager]>>step1. pip install -r requirements.txt | null | *Note: I did not us a virtual environment so the packages in the requirements.txt file are probably not reflective of all the packages used in this project. If some issues pop up please don't hesitate to email me at: gpinto@usc.edu* |
a-longitudinal-study-of-italian-and-french | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/orsoFra/LS_FRIT_UKR/main/README.md | null | null | null | null | null |
geometric-slosh-free-tracking-for-robotic | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/jonarriza96/gsft/main/README.md | ## Installation
### Dependencies
Initialize git submodules with
```
git submodule init
git submodule update
```
### Python environment
Install the specific versions of every package from `requirements.txt` in a new conda environment:
```
conda create --name gsft python=3.9
conda activate gsft
pip install ... | source | [plan source]>> step1. Check dependencies. step2. Initialize git submodules with
```
git submodule init
git submodule update
```
step3. Create conda environment and install requirements:
```
conda create --name gsft python=3.9
conda activate gsft
pip install -r requirements.txt
```
step4. Create variables to en... | null | null |
real-time-line-based-room-segmentation-and | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/EricssonResearch/Line-Based-Room-Segmentation-and-EDF/release/README.md | ## Installation
The project can be installed by running the following command in your terminal:
```bash
pip install -r requirements.txt
``` | source | [plan source]>>[INCOMPLETE]step1. Run the command in your terminal:
```
pip install -r requirements.txt
``` | null | null |
viga | https://bio.tools/ | https://raw.githubusercontent.com/viralInformatics/VIGA/master/README.md | ## Installation
### Step1: Download VIGA
Download VIGA with Git from GitHub
```
git clone https://github.com/viralInformatics/VIGA.git
```
or Download ZIP to local
### Step 2: Download Database
```
1. download taxdmp.zip [Index of /pub/taxonomy (nih.gov)](https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/) and unzip taxd... | source | [plan source]>> step1. Download VIGA with Git from GitHub:
```
git clone https://github.com/viralInformatics/VIGA.git
(stepOptional). or Download ZIP to local
step2.download Database:
step2.1.download taxdmp.zip: https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/ and unzip taxdmp.zip and put it in ./db/
step2.2.download "prot.... | null | manual Installation of MetaCompass
https://github.com/marbl/MetaCompass |
lncrtpred | https://bio.tools/ | https://raw.githubusercontent.com/zglabDIB/LncRTPred/main/README.md | null | null | null | null | null |
nrn-ez | https://bio.tools/ | https://raw.githubusercontent.com/scimemia/NRN-EZ/master/README.md | **INSTALLATION FOR VERSION 1.1.6**
NRN-EZ was built with PyInstaller 3.6, and requires the following languages and libraries:
ÔøΩ Python 3.6.9 and higher (currently up to 3.10)
ÔøΩ PyQt 5.10.1
ÔøΩ PyQtGraph 0.11.0
Installation instructions for Linux (Ubuntu and Pop!_OS): download the Linux zip file and, from the c... | binary | [plan binary]>> step1. install requirements:
Python 3.6.9 and higher (currently up to 3.10)
PyQt 5.10.1
PyQtGraph 0.11.0
step2. for linux:download the Linux zip file and, from the command window. step3. run a bash command for the install.sh file in the corresponding installation folder. | null | 2. for linux:download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder.
2. for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder.
2. for Windows: download the Win zip file and run the installation w... |
causnet | https://bio.tools/ | https://raw.githubusercontent.com/nand1155/CausNet/main/README.md | ## Installation
You can install the development version from GitHub with:
``` r
require("devtools")
install_github("https://github.com/nand1155/CausNet")
``` | source | [plan source]>>step1.install the development version from GitHub with:
``` r
require("devtools")
install_github("https://github.com/nand1155/CausNet")
``` | null | null |
viralcc | https://bio.tools/ | https://raw.githubusercontent.com/dyxstat/Reproduce_ViralCC/main/README.md | "# Instruction of reproducing results in ViralCC paper
We take the cow fecal datasets for example. The other two datasets were processed following the same procedure.
Scripts to process the intermediate data and plot figures are available in the folder [Scripts](https://github.com/dyxstat/Reproduce_ViralCC/tree/main/S... | source | [plan source]>>step1.download and preprocess the raw data.
```
wget https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-run-13/ERR2282092/ERR2282092.1
wget https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-run-13/ERR2530126/ERR2530126.1
wget https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-run-13... | null | (extra comment: NCBI may update its links for downloading the database. Please check the latest link at [NCBI](https://www.ncbi.nlm.nih.gov/) if you meet the download error) |
DRaW | https://bio.tools/ | https://raw.githubusercontent.com/BioinformaticsIASBS/DRaW/main/README.md | # Running DRaW on COVID-19 datasets
The DRaW has been applied on three COVID-19 datasets, DS1, DS2, and DS3. There are three subdirectories, ÔøΩDS1_repurÔøΩ, ÔøΩDS2_repurÔøΩ, and ÔøΩDS3_repurÔøΩ, in the ÔøΩDrug-RepurposingÔøΩ directory. Each subdirectory has been assigned to one of the mentioned datasets. We put the Dr... | source | [plan source]>>step1.execute "Drug-Repurposing.py" script in the command line. step2. after that, execute "score.py":
```bash
cd Drug-Repurposing\DS1_repur
python Drug-Repurposing.py
python score.py
``` | null | The repurposed drugs will be stored in the "meanScore.csv" spreadsheet. It contains the average of ach drug ranking. The lower, the better. For example, to run the DRaW on DS1 |
NRN-EZ | https://bio.tools/ | https://raw.githubusercontent.com/scimemia/NRN-EZ/master/README.md | **INSTALLATION FOR VERSION 1.1.6**
NRN-EZ was built with PyInstaller 3.6, and requires the following languages and libraries:
ÔøΩ Python 3.6.9 and higher (currently up to 3.10)
ÔøΩ PyQt 5.10.1
ÔøΩ PyQtGraph 0.11.0
Installation instructions for Linux (Ubuntu and Pop!_OS): download the Linux zip file and, from the c... | source | [plan source]>>step1. install the requirements:Python 3.6.9 and higher (currently up to 3.10), PyQt 5.10.1, PyQtGraph 0.11.0
step2. for Linux: download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder.
step2. for Mac OS: download the ... | step2. for Linux: download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder.
step2. for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder.
step2. for Windows: download the Win zip file and run the i... | null |
guiding-instruction-based-image-editing-via | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/apple/ml-mgie/main/README.md | ## Requirements
```
conda create -n mgie python=3.10 -y
conda activate mgie
conda update -n base -c defaults conda setuptools -y
conda install -c conda-forge git git-lfs ffmpeg vim htop ninja gpustat -y
conda clean -a -y
pip install -U pip cmake cython==0.29.36 pydantic==1.10 numpy
pip install -U gdown pydrive2 wget j... | source | [plan source]>> step1. create conda environment ```
conda create -n mgie python=3.10 -y
conda activate mgie
conda update -n base -c defaults conda setuptools -y
conda install -c conda-forge git git-lfs ffmpeg vim htop ninja gpustat -y
conda clean -a -y ```
step2. install dependencies ```
pip install -U pip cmake cython... | null | null |
self-play-fine-tuning-converts-weak-language | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/uclaml/SPIN/main/README.md | ## Setup
The following steps provide the necessary setup to run our codes.
1. Create a Python virtual environment with Conda:
```
conda create -n myenv python=3.10
conda activate myenv
```
2. Install PyTorch `v2.1.0` with compatible cuda version, following instructions from [PyTorch Installation Page](https://pytorch.o... | source | [plan source]>>step1.create a Python virtual environment with Conda:
```
conda create -n myenv python=3.10
conda activate myenv
```
step2.install PyTorch `v2.1.0` with compatible cuda version, following instructions from [PyTorch Installation Page](https://pytorch.org/get-started/locally/). For example with cuda 11:
``... | null | null |
genegpt-teaching-large-language-models-to-use | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/ncbi/GeneGPT/main/README.md | # Requirements
The code has been tested with Python 3.9.13. Please first install the required packages by:
```bash
pip install -r requirements.txt
```
You also need an OpenAI API key to run GeneGPT with Codex. Replace the placeholder with your key in `config.py`:
```bash
$ cat config.py
API_KEY = 'YOUR_OPENAI_API_KE... | source | [plan source]>>step1.install requirements:
```bash
pip install -r requirements.txt
```
step2.set OpenAI API key to run GeneGPT with Codex. replace the placeholder with your key in `config.py`:
```bash
$ cat config.py
API_KEY = 'YOUR_OPENAI_API_KEY'
```
step3. execute GeneGPT
After setting up the environment, one can r... | null | The code has been tested with Python 3.9.13 |
the-boundary-of-neural-network-trainability | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/Sohl-Dickstein/fractal/main/README.md | null | null | null | null | null |
learning-to-fly-in-seconds | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/arplaboratory/learning-to-fly/master/README.MD | ## Instructions to run the code
### Docker (isolated)
We provide a pre-built Docker image with a simple web interface that can be executed using a single command (given that Docker is already installed on your machine):
```
docker run -it --rm -p 8000:8000 arpllab/learning_to_fly
```
After the container is running, nav... | source,docker | [plan>>Docker(isolated)]
step1: Execute a single command (given that Docker is already installed on your machine):
```
docker run -it --rm -p 8000:8000 arpllab/learning_to_fly
```
step2. the container is running, now step3. navigate to [https://0.0.0.0:8000](https://0.0.0.0:8000) and step 4. you should see something li... | null | null |
/LargeWorldModel/LWM | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/LargeWorldModel/LWM/main/README.md | ## Setup
Install the requirements with:
```
conda create -n lwm python=3.10
pip install -U "jax[cuda12_pip]==0.4.23" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install -r requirements.txt
```
or set up TPU VM with:
```
sh tpu_requirements.sh
``` | packagemanager, source | [plan packagemanager]>>step1.install the requirements with:
```
conda create -n lwm python=3.10
pip install -U "jax[cuda12_pip]==0.4.23" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install -r requirements.txt
```
optional. set up TPU VM with:
```
sh tpu_requirements.sh
``` | null | optional. set up TPU VM with:
```
sh tpu_requirements.sh
``` |
microsoft/UFO | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/microsoft/UFO/main/README.md | ### ___ Step 1: Installation
UFO requires **Python >= 3.10** running on **Windows OS >= 10**. It can be installed by running the following command:
```bash
# [optional to create conda environment]
# conda create -n ufo python=3.10
# conda activate ufo
# clone the repository
git clone https://github.com/microsoft/UFO.g... | source | [plan source]>>step1: Run the following command:
```
conda create -n ufo python=3.10
conda activate ufo
clone the repository
git clone https://github.com/microsoft/UFO.git
cd UFO```
step2. install the requirements:
```pip install -r requirements.txt
```
step 3: configure the LLMs `ufo/config/config.yaml` file as follow... | null | #### __Reminder: ####
- Before UFO executing your request, please make sure the targeted applications are active on the system.
- The GPT-V accepts screenshots of your desktop and application GUI as input. Please ensure that no sensitive or confidential information is visible or captured during the execution process. ... |
/catid/dora | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/catid/dora/main/README.md | ## Demo
Install conda: https://docs.conda.io/projects/miniconda/en/latest/index.html
```bash
git clone https://github.com/catid/dora.git
cd dora
conda create -n dora python=3.10 -y && conda activate dora
pip install -U -r requirements.txt
python dora.py
``` | source | [plan source]>>step1. install conda:https://docs.conda.io/projects/miniconda/en/latest/index.html. step2. clone the repository and move to the folder:
```bash
git clone https://github.com/catid/dora.git
cd dora
step3. create conda environment:```
conda create -n dora python=3.10 -y && conda activate dora```
step4. inst... | null | null |
YOLO-World | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/AILab-CVC/YOLO-World/master/README.md | ### 1. Installation
YOLO-World is developed based on `torch==1.11.0` `mmyolo==0.6.0` and `mmdetection==3.0.0`.
#### Clone Project
```bash
git clone --recursive https://github.com/AILab-CVC/YOLO-World.git
```
#### Install
```bash
pip install torch wheel -q
pip install -e .
``` | source | [plan source]>>step1. clone repository:
```
git clone --recursive https://github.com/AILab-CVC/YOLO-World.git
```
step2. install module:
pip install torch wheel -q
pip install -e .
``` | null | null |
FasterDecoding/BitDelta | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/FasterDecoding/BitDelta/main/README.md | ## Install
1. Clone the repo and navigate to BitDelta:
```
git clone https://github.com/FasterDecoding/BitDelta
cd BitDelta
```
2. Set up environment:
```bash
conda create -yn bitdelta python=3.9
conda activate bitdelta
pip install -e .
``` | source | [plan source]>>step1.clone the repo and navigate to BitDelta:
```
git clone https://github.com/FasterDecoding/BitDelta
cd BitDelta
```
step2.set up environment:
```bash
conda create -yn bitdelta python=3.9
conda activate bitdelta
pip install -e .
``` | null | null |
tensorflow | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/tensorflow/tensorflow/master/README.md | ## Install
See the [TensorFlow install guide](https://www.tensorflow.org/install) for the
[pip package](https://www.tensorflow.org/install/pip), to
[enable GPU support](https://www.tensorflow.org/install/gpu), use a
[Docker container](https://www.tensorflow.org/install/docker), and
[build from source](https://www.tens... | packagemanager | [plan packagemanager]>>via pip. step1.:
```
$ pip install tensorflow
```
step2. optional. A smaller CPU-only package is also available:
```
$ pip install tensorflow-cpu
```
step3. optional.
To update TensorFlow to the latest version, add `--upgrade` flag to the above
commands.
[plan binary]>> binaries are available for... | null | null |
transformers | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/huggingface/transformers/main/README.md | ## Installation
### With pip
This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+.
You should install __ Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https:/... | packagemanager | [plan packagemanager]>>via pip:
step1. install __ Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html).(extra information) If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)... | null | requirements >> This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+. |
langchain | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md | ## Quick Install
With pip:
```bash
pip install langchain
```
With conda:
```bash
conda install langchain -c conda-forge
``` | packagemanager | [plan packagemanager]>>step1: via pip
```bash
pip install langchain
```
[plan packagemanager]>>step1: via conda:
```bash
conda install langchain -c conda-forge
``` | null | null |
DIG/dig-stable | https://paperwithcode.com/paper/ | https://raw.githubusercontent.com/divelab/DIG/dig-stable/README.md | ## Installation
### Install from pip
The key dependencies of DIG: Dive into Graphs are PyTorch (>=1.10.0), PyTorch Geometric (>=2.0.0), and RDKit.
1. Install [PyTorch](https://pytorch.org/get-started/locally/) (>=1.10.0)
```shell script
$ python -c "import torch; print(torch.__version__)"
>>> 1.10.0
```
2. Insta... | packagemanager | [plan packagemanager]>>step 1. Install [PyTorch](https://pytorch.org/get-started/locally/) (>=1.10.0)
```python -c "import torch; print(torch.__version__)"
```
step2. Install [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#) (>=2.0.0)
```
$ python -c "import torch_geometric; print(torch... | null | null |
Dataset Card for RSInstall Corpus
Dataset Description
Links
- Repository:
- Point of Contact:
Dataset Summary
RSInstall is a small-scale text to unified representation dataset, consisting of 30 installation instructions with corresponding manually labeled plans, steps and topics. annotations each. For more information about the definition please go: repo
Language
English
Data Structure
Data Instance
....
Data Fields
- software,
- repo_name,
- readme_url,
- content,
- plan,
- steps,
- optional_steps,
- extra_info_optional
Dataset Creation
Curation Rationale
...
Who are the source language producers?
Humans creating software
Who are the annotators
Researchers on AI/ML
Licensing Information
mit
Citation
....
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