class_docstr: TextSim class for computing textual similarity.
  
  TextSim leverages the RETSim embedding model to compute the syntactic
  similarity between texts, retrieve similar documents, and deduplicate
  datasets. The model is a highly-optimized, small model that runs
  efficiently on both GPU and CPU, which makes TextSim significantly faster
  than traditional edit distance-based techniques and other neural-based
  embeddings.
  
  For more details on the RETSim model, please see TODO (marinazh) [paper link].
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docstr: Find the closest matches for queries in a list of targets and
  return the result as a pandas DataFrame.
  
  Args:
      queries: Input query texts to search for.
  
      targets: Targets to search in, e.g. for each query, find the nearest
          match in `targets`. If None, then `queries` is used as the
          targets as well and matches are computed within a single list.
  
      similarity_threshold: Similarity threshold for near-duplicate
          match, where a query and a search result are considered
          near-duplicate matches if their similarity is higher than
          `similarity_threshold`. 0.9 is typically a good threshold
          for detecting near-duplicate matches.
  
  Returns:
      Returns a pandas DataFrame with ["Query", "Match", "Similarity"]
      columns, representing each query, nearest match in `targets`, and
      their similarity value.
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code: def match(
      self,
      queries: Sequence[str],
      targets: Sequence[Any] | None = None,
      similarity_threshold: float = 0.9,
  ) -> DataFrame:
      """Find the closest matches for queries in a list of targets and
      return the result as a pandas DataFrame.
  
      Args:
          queries: Input query texts to search for.
  
          targets: Targets to search in, e.g. for each query, find the nearest
              match in `targets`. If None, then `queries` is used as the
              targets as well and matches are computed within a single list.
  
          similarity_threshold: Similarity threshold for near-duplicate
              match, where a query and a search result are considered
              near-duplicate matches if their similarity is higher than
              `similarity_threshold`. 0.9 is typically a good threshold
              for detecting near-duplicate matches.
  
      Returns:
          Returns a pandas DataFrame with ["Query", "Match", "Similarity"]
          columns, representing each query, nearest match in `targets`, and
          their similarity value.
      """
      return super().match(
          queries=queries,
          targets=targets,
          similarity_threshold=similarity_threshold,
      )
  
file_path: unisim/unisim/textsim.py
imports: from typing import Any, Dict, List, Sequence
  from pandas import DataFrame
  from tabulate import tabulate
  from .dataclass import Result, ResultCollection
  from .embedder import TextEmbedder
  from .enums import IndexerType
  from .types import BatchEmbeddings
  from .unisim import UniSim
label_desc: Use image node_id 3 for calling the TextSim obj's underlying member method code with example usage: obj.match(queries, targets, similarity_threshold) and returns: super