

Sense2VecEncoder

embetter.text.Sense2VecEncoder

Create a Sense2Vec encoder, meant to help when encoding phrases as opposed to sentences.

Parameters

Name	Type	Description	Default
path	str	path to downloaded model	required

Usage

import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

from embetter.grab import ColumnGrabber
from embetter.text import Sense2VecEncoder

# Let's suppose this is the input dataframe
dataf = pd.DataFrame({
    "text": ["positive sentiment", "super negative"],
    "label_col": ["pos", "neg"]
})

# This pipeline grabs the `text` column from a dataframe
# which is then passed to the sense2vec model.
text_emb_pipeline = make_pipeline(
    ColumnGrabber("text"),
    Sense2VecEncoder("path/to/s2v")
)
X = text_emb_pipeline.fit_transform(dataf, dataf['label_col'])

transform(self, X, y=None)

Show source code in text/_s2v.py

    def transform(self, X, y=None):
        """Transforms the phrase text into a numeric representation."""
        return np.array([self.s2v[x] for x in X])

Transforms the phrase text into a numeric representation.