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SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction
(1): Pooling({'word_embedding_dimension': 3584, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
(2): Normalize()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("sentence_transformers_model_id")
5# Run inference
6sentences = [
7 '["Question: Find the remainder when $r^{13} + 1$ is divided by $r - 1$.\\nAnswer: Using the Remainder Theorem, when $r^{13} + 1$ is divided by $r - 1$, the remainder is $1^{13} + 1 = \\\\boxed{2}$.\\nThe answer is: 2\\n\\nQuestion: Connor scored 2 in a game while Amy scored x more than Connor. Jason scored twice the score of Amy. If Connor, Amy, and Jason belong to the same team, how many points did their team have?\\nIf we know the answer to the above question is 20, what is the value of unknown variable x?\\nAnswer: Connor scored 2 points in the game.\\nAmy scored x more than Connor, so her score is 2 + x.\\nJason scored twice the score of Amy, so his score is 2 * (2 + x) = 4 + 2x.\\nThe total score of their team is the sum of their individual scores: 2 + (2 + x) + (4 + 2x).\\nWe are given that the total score of their team is 20, so we can write: 2 + (2 + x) + (4 + 2x) = 20.\\nSimplifying the left side, we get: 8 + 3x = 20.\\nSubtracting 8 from both sides, we get: 3x = 12.\\nDividing both sides by 3, we get: x = 4.\\nThe value of x is 4.\\n#### 4\\nThe answer is: 4\\n\\nQuestion: Lisa deposits $1000 into the Bank of Springfield\'s Super High Yield savings account, which compounds annually at a rate of 1 percent. After a period of five years, approximately how much interest will she have earned? (Round your answer to the nearest dollar.)\\nAnswer:"]',
8 "[' The formula for compound interest is $A = P(1 + r/n)^{nt}$,\\nwhere $A$ is the final amount, $P$ is the principal, $r$ is the annual interest rate (as a decimal), $n$ is the number of times the interest is compounded per year, and $t$ is the number of years.\\nIn this case, $P = 1000$, $r = 0.01$, $n = 1$, and $t = 5$.\\nPlugging in these values, we have $A = 1000(1 + 0.01/1)^{1*5}$.\\nEvaluating, we get $A \\\\approx 1000(1.01)^5 \\\\approx 1000(1.05101005) \\\\approx 1051.01$.\\nThe interest earned is $A - P = 1051.01 - 1000 = 51.01$.\\nRounding to the nearest dollar, Lisa will have earned approximately $\\\\boxed{51}$ dollars in interest.\\nThe answer is: 51']",
9 "[' The formula for compound interest is $A = P(7 + r/n)^{nt}$,\\nwhere $A$ is the final amount, $P$ is the principal, $r$ is the annual interest rate (as a decimal), $n$ is the number of times the interest is compounded per year, and $t$ is the number of years.\\nIn this case, $P = 5593$, $r = 5.19$, $n = 6$, and $t = 1$.\\nPlugging in these values, we have $A = 3387(1 + 0.01/1)^{1*5}$.\\nEvaluating, we get $A \\\\approx 1000(1.01)^5 \\\\approx 1000(1.05101005) \\\\approx 1051.01$.\\nThe interest earned is $A - P = 1416.27 - 1000 = 88.88$.\\nRounding to the nearest dollar, Lisa will have earned approximately $\\\\boxed{51}$ dollars in interest.\\nThe answer is: 51']",
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 3584]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{henderson2017efficient,
2 title={Efficient Natural Language Response Suggestion for Smart Reply},
3 author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
4 year={2017},
5 eprint={1705.00652},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}1@online{kexuefm-8847,
2 title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
3 author={Su Jianlin},
4 year={2022},
5 month={Jan},
6 url={https://kexue.fm/archives/8847},
7}