In your case, you should also create the model object and train it on the dataset using the SpaCy's pipeline and the provided feature extraction pipeline. Once you have the trained model, you can use it to extract features and keywords from the dataset by passing the text data (X) and the corresponding labels (y) to the texts and labels attributes of the model respectively.

Here is an example of how you can train the model on the dataset and use it to extract features and keywords from the dataset:

```
import spacy

load the data
nlp = spacy.load('encoreweb_sm')

fit the model on the data
model.fit(X, y)

Use the model to extract features and keywords
x = nlp.texts
y = nlp.labels
Xtrain = pd.DataFrame({'text': X[y==1], 'label': y[1]})
Xtest = pd.DataFrame({'text': X[y==0], 'label': y[0]})
Xtrain, Xtest = Xtrain.iloc[:10], Xtest.iloc[:10]

Xtrain["feature1"] = Xtrain["text"].apply(lambda x: nlp.vocab.vectors[x.text.split()[0]][0]['text'].split()[0])
Xtrain["feature2"] = Xtrain["text"].apply(lambda x: nlp.vocab.vectors[x.text.split()[0]][0]['text'].split()[1])
Xtrain["feature3"] = Xtrain["text"].apply(lambda x: nlp.vocab.vectors[x.text.split()[0]][1]['text'].split()[0])
Xtrain["feature4"] = Xtrain["text"].apply(lambda x: nlp.vocab

how do i integerate this with my code-
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler, LabelEncoder

# Load the dataset
file_path = "/Users/NAME_1