capreolus-端到端神經自組織檢索工具集-Andrew Yates

發佈:2020-12-23 10:46:27.088100

做者:Andrew Yates

### 做者郵箱:initial-then-last@mpi-inf.mpg.de

### 首頁:https://capreolus.ai

### 文檔:None

### 下載連接 

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

Capreolus

Capreolus is a toolkit for conducting end-to-end ad hoc retrieval experiments. Capreolus provides fine control over the entire experimental pipeline through the use of interchangeable and configurable modules.html

Get started with a Notebook python

Read the documentation for a detailed overview.git

Quick Start

Prerequisites: Python 3.7+ and Java 11. See the installation instructions
Install the pip package: pip install capreolus
Train a model: capreolus rerank.traineval with benchmark.name=nf reranker.name=KNRM reranker.trainer.niters=2
If the train command completed successfully, you've trained your first Capreolus reranker on NFCorpus! This command created several outputs, such as model checkpoints and TREC-format run files. To learn about these files, read about running experiments with Capreolus.
To learn about different configuration options, try: capreolus rerank.print_config with benchmark.name=nf reranker.name=KNRM
To learn about different modules you can use, such as reranker.name=DRMM, try: capreolus modules
Learn about running experiments via the Python API

Environment Variables

Capreolus uses environment variables to indicate where outputs should be stored and where document inputs can be found. Consult the table below to determine which variables should be set. Set them either on the fly before running Capreolus (export CAPREOLUS_RESULTS=...) or by editing your shell's initialization files (e.g., ~/.bashrc or ~/.zshrc).github

| Environment Variable | Default Value | Purpose | |-------------------------------|---------------|---------| | CAPREOLUS_RESULTS | ~/.capreolus/results/ | Directory where results will be stored | | CAPREOLUS_CACHE | ~/.capreolus/cache/ | Directory used for cache files | | CUDA_VISIBLE_DEVICES | (unset) | Indicates GPUs available to PyTorch, starting from 0. For example, set to '1' the system's 2nd GPU (as numbered by nvidia-smi). Set to '' (an empty string) to force CPU.shell

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capreolus-端到端神經自組織檢索工具集-Andrew Yates Quick Start Environment Variables