文本处理流水线简介

使用 PyTorch 的文本深度学习

Shubham Jain

Data Scientist

回顾：预处理

预处理流水线

预处理：
- 分词
- 去停用词
- 词干提取
- 罕见词移除

文本处理流水线

预处理流水线

编码：
- 独热编码
- 词袋模型
- TF-IDF
嵌入

文本处理流水线

数据处理流水线

Dataset：已处理与编码文本的容器
DataLoader：批处理、打乱、多进程

回顾：实现 Dataset 与 DataLoader

# Import libraries
from torch.utils.data import Dataset, DataLoader

# Create a class
class TextDataset(Dataset):

    def __init__(self, text):
        self.text = text

    def __len__(self):
        return len(self.text)

    def __getitem__(self, idx):
        return self.text[idx]

回顾：整合 Dataset 与 DataLoader

dataset = TextDataset(encoded_text)

dataloader = DataLoader(dataset, batch_size=2, shuffle=True)

使用辅助函数

def preprocess_sentences(sentences):
    processed_sentences = []
    for sentence in sentences:
        sentence = sentence.lower()
        tokens = tokenizer(sentence)
        tokens = [token for token in tokens 
                  if token not in stop_words]
        tokens = [stemmer.stem(token) 
                  for token in tokens]
        freq_dist = FreqDist(tokens)
        threshold = 2
        tokens = [token for token in tokens if 
        freq_dist[token] > threshold]
        processed_sentences.append(
                   ' '.join(tokens))
    return processed_sentences

def encode_sentences(sentences):
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(sentences)
    encoded_sentences = X.toarray()
    return encoded_sentences, vectorizer

def extract_sentences(data):
    sentences = re.findall(r'[A-Z][^.!?]*[.!?]', 
                           data)
    return sentences

构建文本处理流水线

def text_processing_pipeline(text):

    tokens = preprocess_sentences(text)

    encoded_sentences, vectorizer = encode_sentences(tokens)

    dataset = TextDataset(encoded_sentences)

    dataloader = DataLoader(dataset, batch_size=2, shuffle=True)

    return dataloader, vectorizer

应用文本处理流水线

text_data = "This is the first text data. And here is another one."

sentences = extract_sentences(text_data)
dataloaders, vectorizer = [text_processing_pipeline(text) for text in sentences]

print(next(iter(dataloader))[0, :10])

[[1, 1, 1, 1, 1], [0, 0, 0, 1, 1]]

文本处理流水线：总结

完整流水线

Vamos praticar!

使用 PyTorch 的文本深度学习