add to pip

README.md

@@ -4,7 +4,18 @@ Pointwise, Listwise, Pairwise and [Setwise](https://arxiv.org/pdf/2310.09497.pdf
 
 ---
 ## Installation
-Git clone this repository, then pip install the following libraries:
+Install via PyP
+```bash
+pip install llmrankers
+```
+Or typically for development and research, clone this repo and install as editable,
+```bash
+https://github.com/ielab/llm-rankers.git
+cd llm-rankers
+pip install -e .
+```
+
+The code is tested with the following dependencies:
 ```bash
 torch==2.0.1
 transformers==4.31.0
@@ -54,8 +65,8 @@ In this repository, we use DL 2019 as an example. That is, we always re-rank `ru
 ### Python code example:
 
 ```Python
-from rankers.setwise import SetwiseLlmRanker
-from rankers.rankers import SearchResult
+from llmrankers.setwise import SetwiseLlmRanker
+from llmrankers.rankers import SearchResult
 
 docs = [SearchResult(docid=i, text=f'this is passage {i}', score=None) for i in range(100)]
 query = 'Give me passage 34'

rankers/listwise.py → llmrankers/listwise.py

@@ -1,6 +1,6 @@
 import tiktoken
 from .rankers import LlmRanker, SearchResult
-from typing import List, Tuple
+from typing import List
 import copy
 import openai
 import torch

rankers/pairwise.py → llmrankers/pairwise.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple
+from typing import List
 from .rankers import LlmRanker, SearchResult
 from itertools import combinations
 from collections import defaultdict

rankers/pointwise.py → llmrankers/pointwise.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple
+from typing import List
 from .rankers import LlmRanker, SearchResult
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 from torch.utils.data import DataLoader

rankers/setwise.py → llmrankers/setwise.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple
+from typing import List
 from .rankers import LlmRanker, SearchResult
 import openai
 import time

run.py

@@ -2,11 +2,11 @@
 import ir_datasets
 from pyserini.search.lucene import LuceneSearcher
 from pyserini.search._base import get_topics
-from rankers.rankers import SearchResult
-from rankers.pointwise import PointwiseLlmRanker, MonoT5LlmRanker
-from rankers.setwise import SetwiseLlmRanker, OpenAiSetwiseLlmRanker
-from rankers.pairwise import PairwiseLlmRanker, DuoT5LlmRanker, OpenAiPairwiseLlmRanker
-from rankers.listwise import OpenAiListwiseLlmRanker, ListwiseLlmRanker
+from llmrankers.rankers import SearchResult
+from llmrankers.pointwise import PointwiseLlmRanker, MonoT5LlmRanker
+from llmrankers.setwise import SetwiseLlmRanker, OpenAiSetwiseLlmRanker
+from llmrankers.pairwise import PairwiseLlmRanker, DuoT5LlmRanker, OpenAiPairwiseLlmRanker
+from llmrankers.listwise import OpenAiListwiseLlmRanker, ListwiseLlmRanker
 from tqdm import tqdm
 import argparse
 import sys

setup.py

@@ -0,0 +1,24 @@
+from setuptools import setup, find_packages
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+setup(
+    name='llm-rankers',
+    version='0.0.1',
+    packages=find_packages(),
+    url='https://github.com/ielab/llm-rankers',
+    license='Apache 2.0',
+    author='Shengyao Zhuang',
+    author_email='[email protected]',
+    description='Pointwise, Listwise, Pairwise and Setwise Document Ranking with Large Language Models.',
+    python_requires='>=3.8',
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    install_requires=[
+        "transformers>=4.31.0",
+        "openai>=0.27.10",
+        "tiktoken>=0.4.0",
+        "accelerate>=0.22.0"
+    ]
+)