How to use zenml/finetuned-snowflake-arctic-embed-m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zenml/finetuned-snowflake-arctic-embed-m") sentences = [ "Where is the global configuration directory located in ZenML's default setup?", "'default' ...\n\nCreating default user 'default' ...Creating default stack for user 'default' in workspace default...\n\nActive workspace not set. Setting it to the default.\n\nThe active stack is not set. Setting the active stack to the default workspace stack.\n\nUsing the default store for the global config.\n\nUnable to find ZenML repository in your current working directory (/tmp/folder) or any parent directories. If you want to use an existing repository which is in a different location, set the environment variable 'ZENML_REPOSITORY_PATH'. If you want to create a new repository, run zenml init.\n\nRunning without an active repository root.\n\nUsing the default local database.\n\nRunning with active workspace: 'default' (global)\n\n┏━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━┓\n\n┃ ACTIVE │ STACK NAME │ SHARED │ OWNER │ ARTIFACT_STORE │ ORCHESTRATOR ┃\n\n┠────────┼────────────┼────────┼─────────┼────────────────┼──────────────┨\n\n┃ 👉 │ default │ ❌ │ default │ default │ default ┃\n\n┗━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━┛\n\nThe following is an example of the layout of the global config directory immediately after initialization:\n\n/home/stefan/.config/zenml <- Global Config Directory\n\n├── config.yaml <- Global Configuration Settings\n\n└── local_stores <- Every Stack component that stores information\n\n| locally will have its own subdirectory here.\n\n├── a1a0d3d0-d552-4a80-be09-67e5e29be8ee <- e.g. Local Store path for the\n\n| `default` local Artifact Store\n\n└── default_zen_store\n\n└── zenml.db <- SQLite database where ZenML data (stacks,\n\ncomponents, etc) are stored by default.\n\nAs shown above, the global config directory stores the following information:", "How do you configure the network settings on a Linux server?", "Reranking for better retrieval\n\nAdd reranking to your RAG inference for better retrieval performance.\n\nRerankers are a crucial component of retrieval systems that use LLMs. They help improve the quality of the retrieved documents by reordering them based on additional features or scores. In this section, we'll explore how to add a reranker to your RAG inference pipeline in ZenML.\n\nIn previous sections, we set up the overall workflow, from data ingestion and preprocessing to embeddings generation and retrieval. We then set up some basic evaluation metrics to assess the performance of our retrieval system. A reranker is a way to squeeze a bit of extra performance out of the system by reordering the retrieved documents based on additional features or scores.\n\nAs you can see, reranking is an optional addition we make to what we've already set up. It's not strictly necessary, but it can help improve the relevance and quality of the retrieved documents, which in turn can lead to better responses from the LLM. Let's dive in!\n\nPreviousEvaluation in practice\n\nNextUnderstanding reranking\n\nLast updated 1 month ago" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
