How to use BanhMiKepThit015/Deepshark-Paraphrase-MiniLM-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BanhMiKepThit015/Deepshark-Paraphrase-MiniLM-v2") sentences = [ "Làm thế nào để cải thiện độ chính xác của mô hình tự động học khi làm việc với dữ liệu có cấu trúc cây phức tạp, trong điều kiện tập dữ liệu huấn luyện ban đầu bị hạn chế và cần tối ưu quá trình gán nhãn?", "Nominal set plays a central role in a group-theoretic extension of finite automata to those over an infinite set of data values. Moerman et al. proposed an active learning algorithm for nominal word automata with the equality symmetry. In this paper, we introduce deterministic bottom-up nominal tree automata (DBNTA), which operate on trees whose nodes are labelled with elements of an orbit finite nominal set. We then prove a Myhill-Nerode theorem for the class of languages recognized by DBNTA and propose an active learning algorithm for DBNTA. The algorithm can deal with any data symmetry that admits least support, not restricted to the equality symmetry and/or the total order symmetry. To prove the termination of the algorithm, we define a partial order on nominal sets and show that there is no infinite chain of orbit finite nominal sets with respect to this partial order between any two orbit finite sets.", "We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset consists of 1.1k human-written natural language descriptions of 706 music recordings, all publicly accessible and released under Creative Common licenses. To showcase the use of our dataset, we benchmark popular models on three key music-and-language tasks (music captioning, text-to-music generation and music-language retrieval). Our experiments highlight the importance of cross-dataset evaluation and offer insights into how researchers can use SDD to gain a broader understanding of model performance.", "We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/family member. We describe an automated approach for determining the \\textit{degree of warmth}, a key component of EE, from acoustic and text features acquired from a sample of 37 recorded interviews. These recordings, collected over 20 years ago, are derived from a nationally representative birth cohort of 2,232 British twin children and were manually coded for EE. We outline the core steps of extracting usable information from recordings with highly variable audio quality and assess the efficacy of four machine learning approaches trained with different combinations of acoustic and text features. Despite the challenges of working with this legacy data, we demonstrated that the degree of warmth can be predicted with an $F_{1}$-score of \\textbf{61.5\\%}. In this paper, we summarise our learning and provide recommendations for future work using real-world speech samples." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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