如何在 sklearn 中使用 BERT 和 Elmo 嵌入

问题描述

我使用 sklearn 创建了一个使用 Tf-Idf 的文本分类器，我想使用 BERT 和 Elmo 嵌入而不是 Tf-Idf。

人们会怎么做？

我正在使用以下代码嵌入 Bert：

from flair.data import Sentence
from flair.embeddings import TransformerWordEmbeddings

# init embedding
embedding = TransformerWordEmbeddings('bert-base-uncased')

# create a sentence
sentence = Sentence('The grass is green .')

# embed words in sentence
embedding.embed(sentence)

import pandas as pd
import numpy as np

from sklearn.compose import ColumnTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression

column_trans = ColumnTransformer([
    ('tfidf',TfidfVectorizer(),'text'),('number_scaler',MinMaxScaler(),['number'])
])

# Initialize data
data = [
    ['This process,however,afforded me no means of.',20,1],['another long description',21,['It never once occurred to me that the fumbling',19,0],['How lovely is spring As we looked from Windsor',18,0]
]

# Create DataFrame
df = pd.DataFrame(data,columns=['text','number','target'])

X = column_trans.fit_transform(df)
X = X.toarray()
y = df.loc[:,"target"].values

# Perform classification

classifier = LogisticRegression(random_state=0)
classifier.fit(X,y)

解决方法

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