sentence-transformers How to use ronit01/golden_rag_tuned_minilm_10 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ronit01/golden_rag_tuned_minilm_10")
sentences = [
"How do the FiQA and SciFact RAG tutorial use cases differ in their choice of embedding models, vector store configurations, search strategies, generator models, and document templates?",
" :param search_cfg: The search algorithm type and its kwargs to use for retrieval of vectors/chunks, provided as a single dictionary. Must include a key :code:`\"type\"` with one of the following three options listed as value; default is :code:`\"similarity\"`.\n\n * :code:`\"similarity\"`: Standard cosine similarity search.\n * :code:`\"similarity_score_threshold\"`: Similarity search with minimum score threshold (SST).\n * :code:`\"mmr\"`: Maximum Marginal Relevance (MMR) search for diversity.\n\n Additional parameters for search configuration depend on the type; the keys can include the following:\n\n * :code:`\"k\"`: Number of documents to retrieve. Default is 5.\n * :code:`\"filter\"`: Optional filter criteria function for search results.\n * :code:`\"score_threshold\"`: Only for SST. Minimum similarity score threshold. \n * :code:`\"fetch_k\"`: Only for MMR. Number of documents to fetch before MMR reranking. Default is 20.\n * :code:`\"lambda_mult\"`: Only for MMR. Diversity parameter for MMR balancing relevance vs. diversity. Default is 0.5.\n :type search_cfg: dict, optional",
"Eval Compute Metrics Function\n-------------------------\n\nMandatory user-provided function to compute eval metrics on a given batch of (postprocessed) examples, \nwhich is injected by the system. \nIt should return metrics computed over the batch as a whole. \n\nIt is invoked for each batch during the evaluation process after generation and postprocessing (if applicable). \nPass it directly to the :code:`compute_metrics_fn` key in your eval config dictionary.\n\n\n.. py:function:: eval.compute_metrics_fn(batch: dict[str, list]) -> dict[str, dict[str, Any]]\n\n :param batch: Dictionary containing a batch of examples, including all preprocessed fields, generated outputs, and any postprocessed fields\n :type batch: dict[str, list]\n\n :return: Dictionary with a metric's name as key and a dictionary as value inside which a reserved key :code:`\"value\"` must exist with that corresponding metric's value over this batch of examples.\n :rtype: dict[str, dict[str, Any]]\n",
"Knob Set Generators\n-------\n\nTo create a multi-config specification, you need two things: **knob set generators** for \nknob values and **config group generators** that take a config with set-valued knobs \nto generate groups of full configs.\n\nWe currently support two common knob set generators: :func:`List()` for a discrete \nset of values and :func:`Range()` for sampling from a continuous value interval.\n\n\n.. py:function:: List(values: List[Any])\n\n\t:param values: List of discrete values for a knob; all values must be the same python data type.\n\t:type values: List[Any]\n\n\n.. py:function:: Range(start: int | float, end: int | float, dtype: str = \"int\" | \"float\")\n\n\t:param start: Lower bound of range interval.\n\t:type start: int | float\n\n\t:param end: Upper bound of range interval.\n\t:type end: int | float\n\n\t:param dtype: Data type of value to be sampled, either :code:`\"int\"` or :code:`\"float\"`.\n\t:type dtype: str\n\n\n**Notes:**\n\nAs of this writing, :func:`Range()` performs uniform sampling within the given interval. \nWe plan to continue expanding this API and add more functionality on this front based on feedback.\n\nNote that the return types of the knob set generators are internal to RapidFire AI and \nthey are usable only within the context of the config group generators below.\n\n\n\nConfig Group Generators\n-----\n\nWe currently support two common config group generators: :func:`RFGridSearch()` for grid search \nand :func:`RFRandomSearch()` for random search. \n\nMore support for AutoML heuristics such as SHA, HyperOpt, as well as an integration with \nthe popular AutoML library Optuna are coming soon. \nLikewise for RAG/context engineering, we also plan to support the AutoML heuristic syftr.\n\n\n.. py:function:: RFGridSearch(configs: Dict[str, Any] | List[Dict[str, Any]], trainer_type: str = \"SFT\" | \"DPO\" | \"GRPO\" | None)\n\n\t:param configs: A config dictionary with :func:`List()` for at least one knob; can be a list of such config dictionaries too.\n\t:type configs: Dict[str, Any] | List[Dict[str, Any]]\n\n\t:param trainer_type: The fine-tuning/post-training control flow to use: \"SFT\", \"DPO\", or \"GRPO\". Skip this argument for :func:`run_evals()`.\n\t:type trainer_type: str, optional \n\n\n.. py:function:: RFRandomSearch(configs: Dict[str, Any], trainer_type: str = \"SFT\" | \"DPO\" | \"GRPO\" | None, num_runs: int, seed: int = 42)\n\n\t:param configs: A config dictionary with :func:`List()` or :func:`Range()` for at least one knob.\n\t:type configs: Dict[str, Any]\n\n\t:param trainer_type: The fine-tuning/post-training control flow to use: \"SFT\", \"DPO\", or \"GRPO\". Skip this argument for :func:`run_evals()`.\n\t:type trainer_type: str, optional\n\n\t:param num_runs: Number of runs (full combinations of knob values) to sample in total.\n\t:type num_runs: int\n\n\t:param seed: Seed for random sampling of knob values to construct combinations. Default is 42.\n\t:type seed: int, optional \n\n\n**Notes:**\n\nFor :func:`RFGridSearch()`, each knob can have either a single value or a :func:`List()` of values but no knob \nshould have :func:`Range()` of values; otherwise, it will error out.\n\nFor :func:`RFRandomSearch()`, each knob can have either a single value, or a :func:`List()` of values, or a \n :func:`Range()` of values. The semantics of sampling are independently-identically-distributed (IID), i.e.,\nwe uniformly randomly pick a value from each discrete set and from each continuous set to construct the \nknob combination for one run. \nThen we repeat that sampling process in an IID way to accumulate :code:`num_runs` distinct combinations.\n\nNote that the return types of the config group generators are internal to RapidFire AI and they are usable only \nwithin the context of :func:`run_fit()` or :func:`run_evals()` in the :class:`Experiment` class."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
