So many tools and functions to use? Try Pre:Ranker !
Pre:Ranker is designed to optimize the function calling process of modern LLMs by narrowing down the corpus of available tools based on a given query.
Check 🐱GITHUB for details!

Exapmle Usage

python

from sentence_transformers.cross_encoder import CrossEncoder

model = CrossEncoder('yjoonjang/preranker-v1')
model.eval()
pairs = [
    ["Is 'https://www.apple.com' available in the Wayback Machine on September 9, 2015?", "{'name': 'availability', 'description': 'Checks if a given URL is archived and currently accessible in the Wayback Machine.', 'parameters': {'url': {'description': 'The URL to check for availability in the Wayback Machine.', 'type': 'str', 'default': 'http://mashape.com'}, 'timestamp': {'description': \"The timestamp to look up in Wayback. If not specified, the most recent available capture is returned. The format of the timestamp is 1-14 digits (YYYYMMDDhhmmss). Defaults to '20090101'.\", 'type': 'str, optional', 'default': '20090101'}, 'callback': {'description': 'An optional callback to produce a JSONP response. Defaults to None.', 'type': 'str, optional', 'default': ''}}}"],
    ["Is 'https://www.apple.com' available in the Wayback Machine on September 9, 2015?", "{'name': 'top_grossing_mac_apps', 'description': 'Fetches a list of the top-grossing Mac apps from the App Store.', 'parameters': {'category': {'description': \"The category ID for the apps to be fetched. Defaults to '6016' (general category).\", 'type': 'str', 'default': '6016'}, 'country': {'description': \"The country code for the App Store. Defaults to 'us'.\", 'type': 'str', 'default': 'us'}, 'lang': {'description': \"The language code for the results. Defaults to 'en'.\", 'type': 'str', 'default': 'en'}, 'num': {'description': 'The number of results to return. Defaults to 100. Maximum allowed value is 200.', 'type': 'int', 'default': '100'}}}"],
    ["Is 'https://www.apple.com' available in the Wayback Machine on September 9, 2015?", "{'name': 'top_paid_mac_apps', 'description': 'Retrieves a list of the top paid Mac apps from the App Store.', 'parameters': {'category': {'description': \"Category of the apps to retrieve. Default is '6016'.\", 'type': 'str', 'default': '6016'}, 'country': {'description': \"Country code to filter the app results. Default is 'us'.\", 'type': 'str', 'default': 'us'}, 'lang': {'description': \"Language code for the results. Default is 'en'.\", 'type': 'str', 'default': 'en'}, 'num': {'description': 'Number of results to return. Default is 100. Maximum is 200.', 'type': 'int', 'default': '100'}}}",]
]

scores = model.predict(pairs)
print(scores) # [0.91427845 0.7625548  0.7656321]

MTEB-ToolRet

Changed ToolRet Benchmark into BEIR format to make it MTEB compatible. check make_toolret_to_beir_format.ipynb for more details.

Evaluation Code

bash

git clone https://github.com/yjoonjang/PreRanker.git
cd PreRanker/toolret_eval
python run_mteb.py

Evaluation Results

Model Name	Model Parameter	Recall@10	MAP@10	MRR@10	Precision@10	NDCG@10
yjoonjang/preranker-v1	150M	0.540	0.361	0.462	0.088	0.428
Alibaba-NLP/gte-reranker-modernbert-base	150M	0.524	0.356	0.454	0.086	0.422
jinaai/jina-reranker-v2-base-multilingual	278M	0.502	0.331	0.414	0.083	0.395
Alibaba-NLP/gte-multilingual-reranker-base	306M	0.474	0.299	0.383	0.078	0.363
BAAI/bge-reranker-v2-m3	568M	0.461	0.293	0.370	0.076	0.355

Training Details

preranker-v1 is a fine-tuned model based on Alibaba-NLP/gte-reranker-modernbert-base, via sentence-transformers
Training data will soon be released.

Training Procedure

loss: ListNetLoss
batch size: 4
learning rate: 2e-5
epoch: 1

License

apache-2.0

Citation

bibtex

@misc{Pre:Ranker,
  publisher = {Youngjoon Jang, Seongtae Hong},
  year = {2025},
  url = {https://github.com/yjoonjang/preranker}
},

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

ListNetLoss

bibtex

@inproceedings{cao2007learning,
    title={Learning to Rank: From Pairwise Approach to Listwise Approach},
    author={Cao, Zhe and Qin, Tao and Liu, Tie-Yan and Tsai, Ming-Feng and Li, Hang},
    booktitle={Proceedings of the 24th international conference on Machine learning},
    pages={129--136},
    year={2007}
}