How to use jaredmerlo/jared-adjusted with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jaredmerlo/jared-adjusted") sentences = [ "cy by precomputing and storing the likelihood\nof query terms, ranking documents based on their sum.\n2.3\nRetriever and Generator Fine-tuning\nFine-tuning within the RAG framework is crucial for optimizing both retrievers and generators. Some\nresearch focuses on fine-tuning the generator to better utilize retriever context [30–32], ensuring\nfaithful and robust generated content. Others fine-tune the retriever to learn to retrieve beneficial\npassages for the generator [33–35]. Holistic approaches treat RAG as an integrated system, fine-tuning\nboth retriever and generator together to enhance overall performance [36–38], despite increased\ncomplexity and integration challenges.\nSeveral surveys have extensively discussed current RAG systems, covering aspects like text genera-\ntion [7, 8], integration with LLMs [6, 39], multimodal [40], and AI-generated content [41]. While\nthese surveys provide comprehensive overviews of existing RAG methodologies, selecting the appro-\n3\nWhich city will the nex", " Small-sized blocks are used to match queries, and larger blocks that include contextual information are returned . We use the LLM-Embedder [20] model as an embedding model to demonstrate the effectiveness of advanced chunking techniques . Techniques like small-to-big and sliding window improve quality by maintaining context .", " Chunking balances preserving text semantics with simplicity and efficiency . Larger chunks provide more context, enhancing the process time but increasing process time . Smaller chunks improve retrieval recall and reduce time but may lack sufficient context . Faithfulness measures whether the response is hallucinated or matches the retrieved texts .", " Some research focuses on fine-tuning the generator to better utilize retriever context [30–32] Others fine-tune the retriever to learn to retrieve beneficial passages for the generator [33–35] Holistic approaches treat RAG as an integrated system ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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