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whatlies.language.FloretLanguage

This object is used to lazily fetch Embeddings or EmbeddingSets from a floret language backend.

Important

The vectors are not given by this library they must be on disk upfront.

To train your own floret vectors see the guide here. In short, you can train your model via;

import floret

model = floret.train_unsupervised("data.txt")
model.save_model("vectors.bin")

This language backend might require you to manually install extra dependencies unless you installed via either;

pip install whatlies[floret]
pip install whatlies[all]

Parameters

Name	Type	Description	Default
path		path to the vectors on disk, be sure that it's on disk beforehand	required

Usage:

> from whatlies.language import FloretLanguage
> lang = FasttextLanguage("cc.en.300.bin")
> lang['python']
> lang = FasttextLanguage("cc.en.300.bin", size=10)
> lang[['python', 'snake', 'dog']]

__getitem__(self, query)

Show source code in language/_floret_lang.py

    def __getitem__(self, query: Union[str, List[str]]):
        """
        Retreive a single embedding or a set of embeddings.

        Arguments:
            query: single string or list of strings

        **Usage**
        ```python
        > lang = FasttextLanguage("cc.en.300.bin")
        > lang['python']
        > lang[['python'], ['snake']]
        > lang[['nobody expects'], ['the spanish inquisition']]
        ```
        """
        if isinstance(query, str):
            vec = self.model.get_word_vector(query)
            return Embedding(query, vec)
        return EmbeddingSet(*[self[tok] for tok in query])

Retreive a single embedding or a set of embeddings.

Parameters

Name	Type	Description	Default
query	Union[str, List[str]]	single string or list of strings	required

Usage

> lang = FasttextLanguage("cc.en.300.bin")
> lang['python']
> lang[['python'], ['snake']]
> lang[['nobody expects'], ['the spanish inquisition']]

embset_proximity(self, emb, max_proximity=0.1, top_n=20000, lower=True, metric='cosine')

Show source code in language/_floret_lang.py

    def embset_proximity(
        self,
        emb: Union[str, Embedding],
        max_proximity: float = 0.1,
        top_n=20_000,
        lower=True,
        metric="cosine",
    ):
        """
        Retreive an [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] or embeddings that are within a proximity.

        Arguments:
            emb: query to use
            max_proximity: the number of items you'd like to see returned
            top_n: likelihood limit that sets the subset of words to search
            metric: metric to use to calculate distance, must be scipy or sklearn compatible
            lower: only fetch lower case tokens

        Returns:
            An [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] containing the similar embeddings.
        """
        if isinstance(emb, str):
            emb = self[emb]

        queries = self._prepare_queries(top_n, lower)
        distances = self._calculate_distances(emb, queries, metric)
        return EmbeddingSet(
            {w: self[w] for w, d in zip(queries, distances) if d <= max_proximity}
        )

Retreive an EmbeddingSet or embeddings that are within a proximity.

Parameters

Name	Type	Description	Default
emb	Union[str, whatlies.embedding.Embedding]	query to use	required
max_proximity	float	the number of items you'd like to see returned	0.1
top_n		likelihood limit that sets the subset of words to search	20000
metric		metric to use to calculate distance, must be scipy or sklearn compatible	'cosine'
lower		only fetch lower case tokens	True

Returns

Type	Description
``	An EmbeddingSet containing the similar embeddings.

embset_similar(self, emb, n=10, top_n=20000, lower=False, metric='cosine')

Show source code in language/_floret_lang.py

    def embset_similar(
        self,
        emb: Union[str, Embedding],
        n: int = 10,
        top_n=20_000,
        lower=False,
        metric="cosine",
    ):
        """
        Retreive an [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] that are the most similar to the passed query.

        Arguments:
            emb: query to use
            n: the number of items you'd like to see returned
            top_n: likelihood limit that sets the subset of words to search
            metric: metric to use to calculate distance, must be scipy or sklearn compatible
            lower: only fetch lower case tokens, note that the official english model only has lower case tokens

        Important:
            This method is incredibly slow at the moment without a good `top_n` setting due to
            [this bug](https://github.com/facebookresearch/fastText/issues/1040).

        Returns:
            An [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] containing the similar embeddings.
        """
        embs = [w[0] for w in self.score_similar(emb, n, top_n, lower, metric)]
        return EmbeddingSet({w.name: w for w in embs})

Retreive an EmbeddingSet that are the most similar to the passed query.

Parameters

Name	Type	Description	Default
emb	Union[str, whatlies.embedding.Embedding]	query to use	required
n	int	the number of items you'd like to see returned	10
top_n		likelihood limit that sets the subset of words to search	20000
metric		metric to use to calculate distance, must be scipy or sklearn compatible	'cosine'
lower		only fetch lower case tokens, note that the official english model only has lower case tokens	False

Important

This method is incredibly slow at the moment without a good top_n setting due to this bug.

Returns

Type	Description
``	An EmbeddingSet containing the similar embeddings.

score_similar(self, emb, n=10, top_n=20000, lower=False, metric='cosine')

Show source code in language/_floret_lang.py

    def score_similar(
        self,
        emb: Union[str, Embedding],
        n: int = 10,
        top_n=20_000,
        lower=False,
        metric="cosine",
    ):
        """
        Retreive a list of (Embedding, score) tuples that are the most similar to the passed query.

        Arguments:
            emb: query to use
            n: the number of items you'd like to see returned
            top_n: likelihood limit that sets the subset of words to search, to ignore set to `None`
            metric: metric to use to calculate distance, must be scipy or sklearn compatible
            lower: only fetch lower case tokens, note that the official english model only has lower case tokens

        Important:
            This method is incredibly slow at the moment without a good `top_n` setting due
            to [this bug](https://github.com/facebookresearch/fastText/issues/1040).

        Returns:
            An list of ([Embedding][whatlies.embedding.Embedding], score) tuples.
        """
        if isinstance(emb, str):
            emb = self[emb]

        queries = self._prepare_queries(top_n, lower)
        distances = self._calculate_distances(emb, queries, metric)
        by_similarity = sorted(zip(queries, distances), key=lambda z: z[1])

        if len(queries) < n:
            warnings.warn(
                f"We could only find {len(queries)} feasible words. Consider changing `top_n` or `lower`",
                UserWarning,
            )

        return [(self[q], float(d)) for q, d in by_similarity[:n]]

Retreive a list of (Embedding, score) tuples that are the most similar to the passed query.

Parameters

Name	Type	Description	Default
emb	Union[str, whatlies.embedding.Embedding]	query to use	required
n	int	the number of items you'd like to see returned	10
top_n		likelihood limit that sets the subset of words to search, to ignore set to None	20000
metric		metric to use to calculate distance, must be scipy or sklearn compatible	'cosine'
lower		only fetch lower case tokens, note that the official english model only has lower case tokens	False

Important

This method is incredibly slow at the moment without a good top_n setting due to this bug.

Returns

Type	Description
``	An list of (Embedding, score) tuples.