代码拉取完成，页面将自动刷新
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. It contains the following components:
We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications.
TF-Ranking was presented at premier conferences in Information Retrieval, SIGIR 2019 and ICTIR 2019! The slides are available here.
We provide a demo, with no installation required, to get started on using
TF-Ranking. This demo runs on a
colaboratory notebook, an
interactive Python environment. Using sparse features and embeddings in
TF-Ranking
.
This demo demonstrates how to:
Also see Running Scripts for executable scripts.
To install the latest version from PyPI, run the following:
# Installing with the `--upgrade` flag ensures you'll get the latest version.
pip install --user --upgrade tensorflow_ranking
To force a Python 3-specific install, replace pip with pip3 in the above
commands. For additional installation help, guidance installing prerequisites,
and (optionally) setting up virtual environments, see the
TensorFlow installation guide.
Note: Since TensorFlow is now included as a dependency of the TensorFlow Ranking
package (in setup.py). If you wish to use different versions of TensorFlow
(e.g., tensorflow-gpu), you may need to uninstall the existing verison and
then install your desired version:
$ pip uninstall tensorflow
$ pip install tensorflow-gpu
To build TensorFlow Ranking locally, you will need to install:
Bazel, an open source build tool.
$ sudo apt-get update && sudo apt-get install bazel
Pip, a Python package manager.
$ sudo apt-get install python-pip
VirtualEnv, a tool to create isolated Python environments.
$ pip install --user virtualenv
Clone the TensorFlow Ranking repository.
$ git clone https://github.com/tensorflow/ranking.git
Build TensorFlow Ranking wheel file and store them in /tmp/ranking_pip
folder.
$ cd ranking # The folder which was cloned in Step 2.
$ bazel build //tensorflow_ranking/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow_ranking/tools/pip_package/build_pip_package /tmp/ranking_pip
Install the wheel package using pip. Test in virtualenv, to avoid clash with any system dependencies.
$ ~/.local/bin/virtualenv -p python3 /tmp/tfr
$ source /tmp/tfr/bin/activate
(tfr) $ pip install /tmp/ranking_pip/tensorflow_ranking*.whl
In some cases, you may want to install a specific version of tensorflow,
e.g., tensorflow-gpu or tensorflow==2.0.0. To do so you can either
(tfr) $ pip uninstall tensorflow
(tfr) $ pip install tensorflow==2.0.0
or
(tfr) $ pip uninstall tensorflow
(tfr) $ pip install tensorflow-gpu
Run all TensorFlow Ranking tests.
(tfr) $ bazel test //tensorflow_ranking/...
Invoke TensorFlow Ranking package in python (within virtualenv).
(tfr) $ python -c "import tensorflow_ranking"
For ease of experimentation, we also provide a TFRecord example and a LIBSVM example in the form of executable scripts. This is particularly useful for hyperparameter tuning, where the hyperparameters are supplied as flags to the script.
Set up the data and directory.
MODEL_DIR=/tmp/tf_record_model && \
TRAIN=tensorflow_ranking/examples/data/train_elwc.tfrecord && \
EVAL=tensorflow_ranking/examples/data/eval_elwc.tfrecord && \
VOCAB=tensorflow_ranking/examples/data/vocab.txt
Build and run.
rm -rf $MODEL_DIR && \
bazel build -c opt \
tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary && \
./bazel-bin/tensorflow_ranking/examples/tf_ranking_tfrecord_py_binary \
--train_path=$TRAIN \
--eval_path=$EVAL \
--vocab_path=$VOCAB \
--model_dir=$MODEL_DIR \
--data_format=example_list_with_context
Set up the data and directory.
OUTPUT_DIR=/tmp/libsvm && \
TRAIN=tensorflow_ranking/examples/data/train.txt && \
VALI=tensorflow_ranking/examples/data/vali.txt && \
TEST=tensorflow_ranking/examples/data/test.txt
Build and run.
rm -rf $OUTPUT_DIR && \
bazel build -c opt \
tensorflow_ranking/examples/tf_ranking_libsvm_py_binary && \
./bazel-bin/tensorflow_ranking/examples/tf_ranking_libsvm_py_binary \
--train_path=$TRAIN \
--vali_path=$VALI \
--test_path=$TEST \
--output_dir=$OUTPUT_DIR \
--num_features=136 \
--num_train_steps=100
The training results such as loss and metrics can be visualized using Tensorboard.
(Optional) If you are working on remote server, set up port forwarding with this command.
$ ssh <remote-server> -L 8888:127.0.0.1:8888
Install Tensorboard and invoke it with the following commands.
(tfr) $ pip install tensorboard
(tfr) $ tensorboard --logdir $OUTPUT_DIR
An example jupyter notebook is available in
tensorflow_ranking/examples/handling_sparse_features.ipynb.
To run this notebook, first follow the steps in installation to set up
virtualenv environment with tensorflow_ranking package installed.
Install jupyter within virtualenv.
(tfr) $ pip install jupyter
Start a jupyter notebook instance on remote server.
(tfr) $ jupyter notebook tensorflow_ranking/examples/handling_sparse_features.ipynb \
--NotebookApp.allow_origin='https://colab.research.google.com' \
--port=8888
(Optional) If you are working on remote server, set up port forwarding with this command.
$ ssh <remote-server> -L 8888:127.0.0.1:8888
Running the notebook.
Start jupyter notebook on your local machine at http://localhost:8888/ and browse to the ipython notebook.
An alternative is to use colaboratory notebook via colab.research.google.com and open the notebook in the browser. Choose local runtime and link to port 8888.
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf. TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. KDD 2019.
Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, Marc Najork. Learning Groupwise Scoring Functions Using Deep Neural Networks. ICTIR 2019
Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. Learning to Rank with Selection Bias in Personal Search. SIGIR 2016.
Xuanhui Wang, Cheng Li, Nadav Golbandi, Mike Bendersky, Marc Najork. The LambdaLoss Framework for Ranking Metric Optimization. CIKM 2018.
If you use TensorFlow Ranking in your research and would like to cite it, we suggest you use the following citation:
@inproceedings{TensorflowRankingKDD2019,
author = {Rama Kumar Pasumarthi and Sebastian Bruch and Xuanhui Wang and Cheng Li and Michael Bendersky and Marc Najork and Jan Pfeifer and Nadav Golbandi and Rohan Anil and Stephan Wolf},
title = {TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2019},
pages = {2970--2978},
location = {Anchorage, AK}
}
此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。