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docs: Update path to the classes
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roquelopez committed Jan 6, 2025
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Expand Up @@ -16,7 +16,7 @@ To see how to use these methods, please refer to the documentation of :py:func:`
- Class
- Description
* - ``gpt``
- :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.GPTValueMatcher`
- :class:`~bdikit.value_matching.gpt.GPTValueMatcher`
- | Leverages a large language model (GPT-4) to identify and select the most accurate value matches.

.. list-table:: Methods from other libraries
Expand All @@ -26,14 +26,14 @@ To see how to use these methods, please refer to the documentation of :py:func:`
- Class
- Description
* - ``tfidf``
- :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.TFIDFValueMatcher`
- :class:`~bdikit.value_matching.polyfuzzs.TFIDFValueMatcher`
- | Employs a character-based n-gram TF-IDF approach to approximate edit distance by capturing the frequency and contextual importance of n-gram patterns within strings. This method leverages the Term Frequency-Inverse Document Frequency (TF-IDF) weighting to quantify the similarity between strings based on their shared n-gram features.
* - ``edit_distance``
- :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.EditDistanceValueMatcher`
- :class:`~bdikit.value_matching.polyfuzz.EditDistanceValueMatcher`
- | Uses the edit distance between lists of strings using a customizable scorer that supports various distance and similarity metrics.
* - ``embedding``
- :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.EmbeddingValueMatcher`
- :class:`~bdikit.value_matching.polyfuzz.EmbeddingValueMatcher`
- | A value-matching algorithm that leverages the cosine similarity of value embeddings for precise comparisons. By default, it utilizes the `bert-base-multilingual-cased` model to generate contextualized embeddings, enabling effective multilingual matching.​.
* - ``fasttext``
- :class:`~bdikit.mapping_algorithms.value_mapping.algorithms.FastTextValueMatcher`
- :class:`~bdikit.value_matching.polyfuzz.FastTextValueMatcher`
- | This method uses the cosine similarity of FastText embeddings to accurately compare and align values, capturing both semantic and subword-level similarities..

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