I’m currently learning spaCy, and I have an exercise on word and sentence embeddings. Sentences are stored in a pandas DataFrame columns, and, we’re requested to train a classifier based on the vector of these sentences.
I have a dataframe that looks like this:
+---+---------------------------------------------------+ | | sentence | +---+---------------------------------------------------+ | 0 | "Whitey on the Moon" is a 1970 spoken word poe... | +---+---------------------------------------------------+ | 1 | St Anselm's Church is a Roman Catholic church ... | +---+---------------------------------------------------+ | 2 | Nymphargus grandisonae (common name: giant gla... | +---+---------------------------------------------------+
Next, I apply an NLP function to these sentences:
import en_core_web_md nlp = en_core_web_md.load() df['tokenized'] = df['sentence'].apply(nlp)
Now, if I understand correctly, each item in df[‘tokenized’] has an attribute that returns the vector of the sentence in a 2D array.
print(type(df['tokenized'][0].vector)) print(df['tokenized'][0].vector.shape)
yields
<class 'numpy.ndarray'> (300,)
How do I add the content of this array (300 rows) as columns to the df
dataframe for the corresponding sentence, ignoring stop words?
Thanks!
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Answer
Actually, using a single value averaging all vectors does yield good results in a classification model. What was needed was indeed a dataframe of 300 columns per sentence (since 300 is the standard length of spaCy word embeddings:
So, to continue @Sergey’s code:
sents = ["'Whitey on the Moon' is a 1970 spoken word" , "St Anselm's Church is a Roman Catholic church" , "Nymphargus grandisonae (common name: giant)"] df=pd.DataFrame({"sentence":sents}) df['tokenized'] = df['sentence'].apply(nlp) df['sent_vectors'] = df['tokenized'].apply(lambda x: x.vector) vectors = 0['sent_vector'].apply(pd.Series)
With this, vectors
contains the features of which a model can be trained. For instance, assuming each sentence has a sentiment attached to it:
from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split X = vectors y = df['sentiment'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) clf = LogisticRegression() clf.fit(X_train,y_train) y_pred = clf.predict(X_test)
What I couldn’t do is to remove stopwords from the DataFrame entries (i.e. remove each Token
object from the Doc
parent object stored in the dataframe where is_stop
is False
.