sentence-transformers How to use chelleboyer/llm-mm-good-309e6f79-505b-4c23-8452-37cc854e67df with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("chelleboyer/llm-mm-good-309e6f79-505b-4c23-8452-37cc854e67df")
sentences = [
"How can the quality of reference data constrain outcomes?",
"Dong et al. (2024a)\n\nQingxiu Dong, Li Dong, Xingxing Zhang, Zhifang Sui, and Furu Wei. 2024a.\n\n\nSelf-Boosting Large Language Models with Synthetic Preference Data.\n\n\narXiv preprint arXiv:2410.06961 (2024).\n\n\n\n\n\n\nDong et al. (2022)\n\nQingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al. 2022.\n\n\nA survey on in-context learning.\n\n\narXiv preprint arXiv:2301.00234 (2022).\n\n\n\n\n\n\nDong et al. (2024b)\n\nYijiang River Dong, Tiancheng Hu, and Nigel Collier. 2024b.\n\n\nCan LLM be a Personalized Judge?\n\n\narXiv preprint arXiv:2406.11657 (2024).\n\n\n\n\n\n\nDorner et al. (2024)\n\nFlorian E. Dorner, Vivian Y. Nastl, and Moritz Hardt. 2024.",
"Journal of Natural Language Processing 30, 1 (2023), 243–249.\n\n\n\n\n\n\nChen et al. (2024e)\n\nJunjie Chen, Weihang Su, Zhumin Chu, Haitao Li, Qinyao Ai, Yiqun Liu, Min Zhang, and Shaoping Ma. 2024e.\n\n\nAn Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation.\n\n\n\n\narXiv:2410.12265 [cs.CL]\n\nhttps://arxiv.org/abs/2410.12265\n\n\n\nChen et al. (2023c)\n\nJiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, and Somesh Jha. 2023c.\n\n\nAdaptation with self-evaluation to improve selective prediction in llms.\n\n\narXiv preprint arXiv:2310.11689 (2023).\n\n\n\n\n\n\nChen et al. (2024d)",
"may be constrained by the quality and variety of the reference data."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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