| Name | rank-llm JSON |
| Version |
0.20.2
JSON |
| download |
| home_page | None |
| Summary | A Package for running prompt decoders like RankVicuna |
| upload_time | 2024-09-07 23:12:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
prompt-decoder
rankvicuna
rankzephyr
rankllm
information retrieval
neural ranking
llm
listwise
pointwise
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# RankLLM
[](https://pypi.org/project/rank-llm/)
[](https://pepy.tech/project/rank-llm)
[](https://pepy.tech/project/rank-llm)
[](https://arxiv.org/abs/2309.15088)
[](https://www.apache.org/licenses/LICENSE-2.0)
We offer a suite of rerankers - pointwise models like monoT5 and listwise models with a focus on open source LLMs compatible with [FastChat](https://github.com/lm-sys/FastChat?tab=readme-ov-file#supported-models) (e.g., Vicuna, Zephyr, etc.) or [vLLM](https://https://github.com/vllm-project/vllm). We also support RankGPT variants, which are proprietary listwise rerankers. Some of the code in this repository is borrowed from [RankGPT](https://github.com/sunnweiwei/RankGPT), [PyGaggle](https://github.com/castorini/pygaggle), and [LiT5](https://github.com/castorini/LiT5)!
# Releases
current_version = 0.20.2
## 📟 Instructions
### Create Conda Environment
```bash
conda create -n rankllm python=3.10
conda activate rankllm
```
### Install Pytorch with CUDA
```bash
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```
### Install openjdk with maven if you want to use the retriever
```bash
conda install -c conda-forge openjdk=21 maven -y
```
### Install Dependencies
```bash
pip install -r requirements.txt
```
### Run end to end - RankZephyr
We can run the RankZephyr model with the following command:
```bash
python src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/rank_zephyr_7b_v1_full --top_k_candidates=100 --dataset=dl20 \
--retrieval_method=SPLADE++_EnsembleDistil_ONNX --prompt_mode=rank_GPT --context_size=4096 --variable_passages
```
Including the `--vllm_batched` flag will allow you to run the model in batched mode using the `vllm` library.
If you want to run multiple passes of the model, you can use the `--num_passes` flag.
### Run end to end - RankGPT4-o
We can run the RankGPT4-o model with the following command:
```bash
python src/rank_llm/scripts/run_rank_llm.py --model_path=gpt-4o --top_k_candidates=100 --dataset=dl20 \
--retrieval_method=bm25 --prompt_mode=rank_GPT_APEER --context_size=4096 --use_azure_openai
```
Note that the `--prompt_mode` is set to `rank_GPT_APEER` to use the LLM refined prompt from [APEER](https://arxiv.org/abs/2406.14449).
This can be changed to `rank_GPT` to use the original prompt.
### Run end to end - LiT5
We can run the LiT5-Distill V2 model (which could rerank 100 documents in a single pass) with the following command:
```bash
python src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/LiT5-Distill-large-v2 --top_k_candidates=100 --dataset=dl19 \
--retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=4 \
--variable_passages --window_size=100
```
We can run the LiT5-Distill original model (which works with a window size of 20) with the following command:
```bash
python src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/LiT5-Distill-large --top_k_candidates=100 --dataset=dl19 \
--retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=32 \
--variable_passages
```
We can run the LiT5-Score model with the following command:
```bash
python src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/LiT5-Score-large --top_k_candidates=100 --dataset=dl19 \
--retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=8 \
--window_size=100 --variable_passages
```
### Run end to end - monoT5
The following runs the 3B variant of monoT5 trained for 10K steps:
```
python src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/monot5-3b-msmarco-10k --top_k_candidates=1000 --dataset=dl19 \
--retrieval_method=bm25 --prompt_mode=monot5 --context_size=512
```
Note that we usually rerank 1K candidates with monoT5.
If you would like to contribute to the project, please refer to the [contribution guidelines](CONTRIBUTING.md).
## 🦙🐧 Model Zoo
The following is a table of the listwise models our repository was primarily built to handle (with the models hosted on HuggingFace):
| Model Name | Hugging Face Identifier/Link |
|-------------------|---------------------------------------------|
| RankZephyr 7B V1 - Full - BF16 | [castorini/rank_zephyr_7b_v1_full](https://huggingface.co/castorini/rank_zephyr_7b_v1_full) |
| RankVicuna 7B - V1 | [castorini/rank_vicuna_7b_v1](https://huggingface.co/castorini/rank_vicuna_7b_v1) |
| RankVicuna 7B - V1 - No Data Augmentation | [castorini/rank_vicuna_7b_v1_noda](https://huggingface.co/castorini/rank_vicuna_7b_v1_noda) |
| RankVicuna 7B - V1 - FP16 | [castorini/rank_vicuna_7b_v1_fp16](https://huggingface.co/castorini/rank_vicuna_7b_v1_fp16) |
| RankVicuna 7B - V1 - No Data Augmentation - FP16 | [castorini/rank_vicuna_7b_v1_noda_fp16](https://huggingface.co/castorini/rank_vicuna_7b_v1_noda_fp16) |
We also officially support the following rerankers built by our group:
### LiT5 Suite
The following is a table specifically for our LiT5 suite of models hosted on HuggingFace:
| Model Name | Hugging Face Identifier/Link |
|-----------------------|---------------------------------------------|
| LiT5 Distill base | [castorini/LiT5-Distill-base](https://huggingface.co/castorini/LiT5-Distill-base) |
| LiT5 Distill large | [castorini/LiT5-Distill-large](https://huggingface.co/castorini/LiT5-Distill-large) |
| LiT5 Distill xl | [castorini/LiT5-Distill-xl](https://huggingface.co/castorini/LiT5-Distill-xl) |
| LiT5 Distill base v2 | [castorini/LiT5-Distill-base-v2](https://huggingface.co/castorini/LiT5-Distill-base-v2) |
| LiT5 Distill large v2 | [castorini/LiT5-Distill-large-v2](https://huggingface.co/castorini/LiT5-Distill-large-v2) |
| LiT5 Distill xl v2 | [castorini/LiT5-Distill-xl-v2](https://huggingface.co/castorini/LiT5-Distill-xl-v2) |
| LiT5 Score base | [castorini/LiT5-Score-base](https://huggingface.co/castorini/LiT5-Score-base) |
| LiT5 Score large | [castorini/LiT5-Score-large](https://huggingface.co/castorini/LiT5-Score-large) |
| LiT5 Score xl | [castorini/LiT5-Score-xl](https://huggingface.co/castorini/LiT5-Score-xl) |
Now you can run top-100 reranking with the v2 model in a single pass while maintaining efficiency!
### monoT5 Suite - Pointwise Rerankers
The following is a table specifically for our monoT5 suite of models hosted on HuggingFace:
| Model Name | Hugging Face Identifier/Link |
|-----------------------------------|--------------------------------------------------------|
| monoT5 Small MSMARCO 10K | [castorini/monot5-small-msmarco-10k](https://huggingface.co/castorini/monot5-small-msmarco-10k) |
| monoT5 Small MSMARCO 100K | [castorini/monot5-small-msmarco-100k](https://huggingface.co/castorini/monot5-small-msmarco-100k) |
| monoT5 Base MSMARCO | [castorini/monot5-base-msmarco](https://huggingface.co/castorini/monot5-base-msmarco) |
| monoT5 Base MSMARCO 10K | [castorini/monot5-base-msmarco-10k](https://huggingface.co/castorini/monot5-base-msmarco-10k) |
| monoT5 Large MSMARCO 10K | [castorini/monot5-large-msmarco-10k](https://huggingface.co/castorini/monot5-large-msmarco-10k) |
| monoT5 Large MSMARCO | [castorini/monot5-large-msmarco](https://huggingface.co/castorini/monot5-large-msmarco) |
| monoT5 3B MSMARCO 10K | [castorini/monot5-3b-msmarco-10k](https://huggingface.co/castorini/monot5-3b-msmarco-10k) |
| monoT5 3B MSMARCO | [castorini/monot5-3b-msmarco](https://huggingface.co/castorini/monot5-3b-msmarco) |
| monoT5 Base Med MSMARCO | [castorini/monot5-base-med-msmarco](https://huggingface.co/castorini/monot5-base-med-msmarco) |
| monoT5 3B Med MSMARCO | [castorini/monot5-3b-med-msmarco](https://huggingface.co/castorini/monot5-3b-med-msmarco) |
We recommend the Med models for biomedical retrieval. We also provide both 10K (generally better OOD effectiveness) and 100K checkpoints (better in-domain).
## ✨ References
If you use RankLLM, please cite the following relevant papers:
[[2309.15088] RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models](https://arxiv.org/abs/2309.15088)
<!-- {% raw %} -->
```
@ARTICLE{pradeep2023rankvicuna,
title = {{RankVicuna}: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models},
author = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin},
year = {2023},
journal = {arXiv:2309.15088}
}
```
<!-- {% endraw %} -->
[[2312.02724] RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://arxiv.org/abs/2312.02724)
<!-- {% raw %} -->
```
@ARTICLE{pradeep2023rankzephyr,
title = {{RankZephyr}: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!},
author = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin},
year = {2023},
journal = {arXiv:2312.02724}
}
```
<!-- {% endraw %} -->
If you use one of the LiT5 models please cite the following relevant paper:
[[2312.16098] Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models](https://arxiv.org/abs/2312.16098)
```
@ARTICLE{tamber2023scaling,
title = {Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models},
author = {Manveer Singh Tamber and Ronak Pradeep and Jimmy Lin},
year = {2023},
journal = {arXiv:2312.16098}
}
```
If you use one of the monoT5 models please cite the following relevant paper:
[[2101.05667] The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv.org/abs/2101.05667)
```
@ARTICLE{pradeep2021emd,
title = {The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},
author = {Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},
year = {2021},
journal = {arXiv:2101.05667},
}
```
## 🙏 Acknowledgments
This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
Raw data
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"keywords": "prompt-decoder, RankVicuna, RankZephyr, RankLLM, information retrieval, neural ranking, LLM, listwise, pointwise",
"author": null,
"author_email": "Sahel Sharifymoghaddam <sahel.sharifymoghaddam@uwaterloo.ca>, Ronak Pradeep <rpradeep@uwaterloo.ca>, Jimmy Lin <jimmylin@uwaterloo.ca>",
"download_url": "https://files.pythonhosted.org/packages/bf/de/4f85a81d2151081c625340e470d363362833277e7fa4e9e8b76195cc50cc/rank_llm-0.20.2.tar.gz",
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"description": "# RankLLM\n\n[](https://pypi.org/project/rank-llm/)\n[](https://pepy.tech/project/rank-llm)\n[](https://pepy.tech/project/rank-llm)\n[](https://arxiv.org/abs/2309.15088)\n[](https://www.apache.org/licenses/LICENSE-2.0)\n\n\nWe offer a suite of rerankers - pointwise models like monoT5 and listwise models with a focus on open source LLMs compatible with [FastChat](https://github.com/lm-sys/FastChat?tab=readme-ov-file#supported-models) (e.g., Vicuna, Zephyr, etc.) or [vLLM](https://https://github.com/vllm-project/vllm). We also support RankGPT variants, which are proprietary listwise rerankers. Some of the code in this repository is borrowed from [RankGPT](https://github.com/sunnweiwei/RankGPT), [PyGaggle](https://github.com/castorini/pygaggle), and [LiT5](https://github.com/castorini/LiT5)!\n\n# Releases\ncurrent_version = 0.20.2\n\n## \ud83d\udcdf Instructions\n\n### Create Conda Environment\n\n```bash\nconda create -n rankllm python=3.10\nconda activate rankllm\n```\n\n### Install Pytorch with CUDA\n```bash\npip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n```\n\n### Install openjdk with maven if you want to use the retriever\n```bash\nconda install -c conda-forge openjdk=21 maven -y\n```\n\n### Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### Run end to end - RankZephyr\n\nWe can run the RankZephyr model with the following command:\n```bash\npython src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/rank_zephyr_7b_v1_full --top_k_candidates=100 --dataset=dl20 \\\n--retrieval_method=SPLADE++_EnsembleDistil_ONNX --prompt_mode=rank_GPT --context_size=4096 --variable_passages\n```\n\nIncluding the `--vllm_batched` flag will allow you to run the model in batched mode using the `vllm` library.\nIf you want to run multiple passes of the model, you can use the `--num_passes` flag.\n\n### Run end to end - RankGPT4-o\n\nWe can run the RankGPT4-o model with the following command:\n```bash\npython src/rank_llm/scripts/run_rank_llm.py --model_path=gpt-4o --top_k_candidates=100 --dataset=dl20 \\\n --retrieval_method=bm25 --prompt_mode=rank_GPT_APEER --context_size=4096 --use_azure_openai\n```\nNote that the `--prompt_mode` is set to `rank_GPT_APEER` to use the LLM refined prompt from [APEER](https://arxiv.org/abs/2406.14449).\nThis can be changed to `rank_GPT` to use the original prompt.\n\n### Run end to end - LiT5\n\nWe can run the LiT5-Distill V2 model (which could rerank 100 documents in a single pass) with the following command:\n\n```bash\npython src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/LiT5-Distill-large-v2 --top_k_candidates=100 --dataset=dl19 \\\n --retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=4 \\\n --variable_passages --window_size=100\n```\n\nWe can run the LiT5-Distill original model (which works with a window size of 20) with the following command:\n\n```bash\npython src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/LiT5-Distill-large --top_k_candidates=100 --dataset=dl19 \\\n --retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=32 \\\n --variable_passages\n```\n\nWe can run the LiT5-Score model with the following command:\n\n```bash\npython src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/LiT5-Score-large --top_k_candidates=100 --dataset=dl19 \\\n --retrieval_method=bm25 --prompt_mode=LiT5 --context_size=150 --vllm_batched --batch_size=8 \\\n --window_size=100 --variable_passages\n```\n\n### Run end to end - monoT5\n\nThe following runs the 3B variant of monoT5 trained for 10K steps:\n\n```\npython src/rank_llm/scripts/run_rank_llm.py --model_path=castorini/monot5-3b-msmarco-10k --top_k_candidates=1000 --dataset=dl19 \\\n --retrieval_method=bm25 --prompt_mode=monot5 --context_size=512\n```\n\nNote that we usually rerank 1K candidates with monoT5.\n\nIf you would like to contribute to the project, please refer to the [contribution guidelines](CONTRIBUTING.md).\n\n## \ud83e\udd99\ud83d\udc27 Model Zoo\n\nThe following is a table of the listwise models our repository was primarily built to handle (with the models hosted on HuggingFace):\n\n| Model Name | Hugging Face Identifier/Link |\n|-------------------|---------------------------------------------|\n| RankZephyr 7B V1 - Full - BF16 | [castorini/rank_zephyr_7b_v1_full](https://huggingface.co/castorini/rank_zephyr_7b_v1_full) |\n| RankVicuna 7B - V1 | [castorini/rank_vicuna_7b_v1](https://huggingface.co/castorini/rank_vicuna_7b_v1) |\n| RankVicuna 7B - V1 - No Data Augmentation | [castorini/rank_vicuna_7b_v1_noda](https://huggingface.co/castorini/rank_vicuna_7b_v1_noda) |\n| RankVicuna 7B - V1 - FP16 | [castorini/rank_vicuna_7b_v1_fp16](https://huggingface.co/castorini/rank_vicuna_7b_v1_fp16) |\n| RankVicuna 7B - V1 - No Data Augmentation - FP16 | [castorini/rank_vicuna_7b_v1_noda_fp16](https://huggingface.co/castorini/rank_vicuna_7b_v1_noda_fp16) |\n\nWe also officially support the following rerankers built by our group:\n\n### LiT5 Suite\n\nThe following is a table specifically for our LiT5 suite of models hosted on HuggingFace:\n\n| Model Name | Hugging Face Identifier/Link |\n|-----------------------|---------------------------------------------|\n| LiT5 Distill base | [castorini/LiT5-Distill-base](https://huggingface.co/castorini/LiT5-Distill-base) |\n| LiT5 Distill large | [castorini/LiT5-Distill-large](https://huggingface.co/castorini/LiT5-Distill-large) |\n| LiT5 Distill xl | [castorini/LiT5-Distill-xl](https://huggingface.co/castorini/LiT5-Distill-xl) |\n| LiT5 Distill base v2 | [castorini/LiT5-Distill-base-v2](https://huggingface.co/castorini/LiT5-Distill-base-v2) |\n| LiT5 Distill large v2 | [castorini/LiT5-Distill-large-v2](https://huggingface.co/castorini/LiT5-Distill-large-v2) |\n| LiT5 Distill xl v2 | [castorini/LiT5-Distill-xl-v2](https://huggingface.co/castorini/LiT5-Distill-xl-v2) |\n| LiT5 Score base | [castorini/LiT5-Score-base](https://huggingface.co/castorini/LiT5-Score-base) |\n| LiT5 Score large | [castorini/LiT5-Score-large](https://huggingface.co/castorini/LiT5-Score-large) |\n| LiT5 Score xl | [castorini/LiT5-Score-xl](https://huggingface.co/castorini/LiT5-Score-xl) |\n\nNow you can run top-100 reranking with the v2 model in a single pass while maintaining efficiency!\n\n### monoT5 Suite - Pointwise Rerankers\n\nThe following is a table specifically for our monoT5 suite of models hosted on HuggingFace:\n\n| Model Name | Hugging Face Identifier/Link |\n|-----------------------------------|--------------------------------------------------------|\n| monoT5 Small MSMARCO 10K | [castorini/monot5-small-msmarco-10k](https://huggingface.co/castorini/monot5-small-msmarco-10k) |\n| monoT5 Small MSMARCO 100K | [castorini/monot5-small-msmarco-100k](https://huggingface.co/castorini/monot5-small-msmarco-100k) |\n| monoT5 Base MSMARCO | [castorini/monot5-base-msmarco](https://huggingface.co/castorini/monot5-base-msmarco) |\n| monoT5 Base MSMARCO 10K | [castorini/monot5-base-msmarco-10k](https://huggingface.co/castorini/monot5-base-msmarco-10k) |\n| monoT5 Large MSMARCO 10K | [castorini/monot5-large-msmarco-10k](https://huggingface.co/castorini/monot5-large-msmarco-10k) |\n| monoT5 Large MSMARCO | [castorini/monot5-large-msmarco](https://huggingface.co/castorini/monot5-large-msmarco) |\n| monoT5 3B MSMARCO 10K | [castorini/monot5-3b-msmarco-10k](https://huggingface.co/castorini/monot5-3b-msmarco-10k) |\n| monoT5 3B MSMARCO | [castorini/monot5-3b-msmarco](https://huggingface.co/castorini/monot5-3b-msmarco) |\n| monoT5 Base Med MSMARCO | [castorini/monot5-base-med-msmarco](https://huggingface.co/castorini/monot5-base-med-msmarco) |\n| monoT5 3B Med MSMARCO | [castorini/monot5-3b-med-msmarco](https://huggingface.co/castorini/monot5-3b-med-msmarco) |\n\nWe recommend the Med models for biomedical retrieval. We also provide both 10K (generally better OOD effectiveness) and 100K checkpoints (better in-domain).\n\n## \u2728 References\n\nIf you use RankLLM, please cite the following relevant papers: \n\n[[2309.15088] RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models](https://arxiv.org/abs/2309.15088)\n\n<!-- {% raw %} -->\n```\n@ARTICLE{pradeep2023rankvicuna,\n title = {{RankVicuna}: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models},\n author = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin},\n year = {2023},\n journal = {arXiv:2309.15088}\n}\n```\n<!-- {% endraw %} -->\n\n\n[[2312.02724] RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://arxiv.org/abs/2312.02724)\n\n<!-- {% raw %} -->\n```\n@ARTICLE{pradeep2023rankzephyr,\n title = {{RankZephyr}: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!},\n author = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin},\n year = {2023},\n journal = {arXiv:2312.02724}\n}\n```\n<!-- {% endraw %} -->\n\nIf you use one of the LiT5 models please cite the following relevant paper:\n\n[[2312.16098] Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models](https://arxiv.org/abs/2312.16098)\n\n```\n@ARTICLE{tamber2023scaling,\n title = {Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models},\n author = {Manveer Singh Tamber and Ronak Pradeep and Jimmy Lin},\n year = {2023},\n journal = {arXiv:2312.16098}\n}\n```\n\nIf you use one of the monoT5 models please cite the following relevant paper:\n\n[[2101.05667] The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv.org/abs/2101.05667)\n\n```\n@ARTICLE{pradeep2021emd,\n title = {The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},\n author = {Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},\n year = {2021},\n journal = {arXiv:2101.05667}, \n}\n```\n## \ud83d\ude4f Acknowledgments\n\nThis research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.\n",
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