# whatlies.language.FasttextLanguage¶

This object is used to lazily fetch Embeddings or EmbeddingSets from a fasttext language backend. This object is meant for retreival, not plotting.

Important

The vectors are not given by this library they must be downloaded upfront. You can find the download links here. Note: you'll want the bin file, not the text file. To train your own fasttext model see the guide here.

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

pip install whatlies[fasttext]
pip install whatlies[all]


Warning

You could theoretically use fasttext to train your own models with this code;

> import fasttext
> model = fasttext.train_unsupervised('data.txt',
model='cbow',
dim=10)
> model = fasttext.train_unsupervised('data.txt',
model='skipgram',
dim=20,
epoch=20,
lr=0.1,
min_count=1)
> lang = FasttextLanguage(model)
> lang['python']
> model.save_model("result/data-skipgram-20.bin")
> lang = FasttextLanguage("result/data-skipgram-20.bin")


But you need to be aware that the fasttext library from facebook has gone stale. Last update on pypi was June 2019. Our preferred usecase for it is to use the pretrained vectors. Note that you can also import these via spaCy but this requires a packaging step.

Parameters

Name Type Description Default
model name of the model to load, be sure that it's downloaded or trained beforehand required

Usage:

> from whatlies.language import FasttextLanguage
> 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/_fasttext_lang.py
  97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118  def __getitem__(self, query: Union[str, List[str]]): """ Retreive a single embedding or a set of embeddings. Depending on the spaCy model the strings can support multiple tokens of text but they can also use the Bert DSL. See the Language Options documentation: https://rasahq.github.io/whatlies/tutorial/languages/#bert-style. 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): self._input_str_legal(query) 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. Depending on the spaCy model the strings can support multiple tokens of text but they can also use the Bert DSL. See the Language Options documentation: https://rasahq.github.io/whatlies/tutorial/languages/#bert-style.

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/_fasttext_lang.py
 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165  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/_fasttext_lang.py
 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193  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/_fasttext_lang.py
 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233  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.