'utf-8' 编解码器无法解码位置 3131 中的字节 0x80：无效的起始字节

Question

我的信息检索代码有问题。

我只是想从我之前添加到语料库文件的 txt 文件中获取信息。一开始，一切都还好，检索到的信息并不惊人，而是一个好的开始。

但是，现在每次运行代码时，我都会得到这个错误：

UnicodeDecodeError：“utf-8”编解码器无法解码位置 3131 中的字节 0x80：起始字节无效

import nltk
import sys
import os
import math
from nltk.tokenize import wordpunct_tokenize
import string
import path

FILE_MATCHES = 1
SENTENCE_MATCHES = 1


def main():

    # Check command-line arguments
    if len(sys.argv) != 2:
        sys.exit("Usage: python questions.py corpus")
    else:
        print("Hi! I hope you are having a good time. Thank you for contacting Appen today.")

    # Calculate IDF values across files
    files = load_files(sys.argv[1])

   
    file_words = {
        filename: tokenize(files[filename])
        for filename in files
    }
    file_idfs = compute_idfs(file_words)

    # Prompt user for query
    
    query = set(tokenize(input( "What can I help you with today?: ")))

    # Determine top file matches according to TF-IDF
    filenames = top_files(query, file_words, file_idfs, n=FILE_MATCHES)

    # Extract sentences from top files
    sentences = dict()
    for filename in filenames:
        for passage in files[filename].split("\n"):
            for sentence in nltk.sent_tokenize(passage):
                tokens = tokenize(sentence)
                if tokens:
                    sentences[sentence] = tokens

    # Compute IDF values across sentences
    idfs = compute_idfs(sentences)

    # Determine top sentence matches
    matches = top_sentences(query, sentences, idfs, n=SENTENCE_MATCHES)
    for match in matches:
        print(match)

def load_files(directory):
    """
    Given a directory name, return a dictionary mapping the filename of each
    `.txt` file inside that directory to the file's contents as a string.
    """
    corpus = {}
    abad = os.path.join(os.getcwd(), directory)

    allfiles = os.listdir(directory)

    for filename in allfiles:

        with open((os.path.join(abad, filename)), mode='r', encoding="utf-8") as f:
            doc = f.read().rstrip("\n")
            corpus[filename] = doc

    return corpus


def tokenize(document):
    """
    Given a document (represented as a string), return a list of all of the
    words in that document, in order.
    Process document by coverting all words to lowercase, and removing any
    punctuation or English stopwords.
    """
    token = nltk.tokenize.word_tokenize(document.lower())
    
    document = [x for x in token if x not in string.punctuation and x not in nltk.corpus.stopwords.words("english")]

    return document


def compute_idfs(documents):
    """
    Given a dictionary of `documents` that maps names of documents to a list
    of words, return a dictionary that maps words to their IDF values.
    Any word that appears in at least one of the documents should be in the
    resulting dictionary.
    """
    words = set()
    for filename in documents:
        words.update(documents[filename])

    # Calculate IDFs
    idfs = dict()
    for word in words:
        f = sum(word in documents[filename] for filename in documents)
        idf = math.log(len(documents) / f)
        idfs[word] = idf

    return idfs


def top_files(query, files, idfs, n):
    """
    Given a `query` (a set of words), `files` (a dictionary mapping names of
    files to a list of their words), and `idfs` (a dictionary mapping words
    to their IDF values), return a list of the filenames of the the `n` top
    files that match the query, ranked according to tf-idf.
    """
    #set up tfids dictionary
    tfidfs = {}


    #for the name and content in the files items check if the word is in them and if so add to score
    for filename, filecon in files.items():
        score = 0
        for word in query:
            if word in filecon:
                score += filecon.count(word)* idfs[word]
        if score != 0:
            tfidfs[filename] = score

    # Sort and get top n TF-IDFs for each file
    print("Give me a moment to check that for you.")
    sort = [k for k, v in sorted(tfidfs.items(), key=lambda y: y[1], reverse=True)]

    return sort[:n]


def top_sentences(query, sentences, idfs, n):
    """
    Given a `query` (a set of words), `sentences` (a dictionary mapping
    sentences to a list of their words), and `idfs` (a dictionary mapping words
    to their IDF values), return a list of the `n` top sentences that match
    the query, ranked according to idf. If there are ties, preference should
    be given to sentences that have a higher query term density.
    """
    #set up tfids dictionary
    tfidfs = [] 

    #for the name and content in the sentences check if the word is in them and if so add to score
    for sentence in sentences:
        idf = 0
        match = 0
        for s in query:
            if s in sentences[sentence]:  # if query is in the sentence, add IDFS and record a match
                idf += idfs[s]
                match += 1

        density = float(match)/len(sentences[sentence])  # calculate 'matching word measure'

        tfidfs.append((sentence, idf, density))
    # Sort and get top n TF-IDFs for sentence
    tfidfs.sort(key=lambda x: (x[1], x[2]), reverse=True)
    sort = [x[0] for x in tfidfs]

    return sort [:n]

if __name__ == "__main__":
    main()

我在网上查了这个问题，找到了几个答案，我试过了，但没有用。

Answer 1

此错误表明此文件不是 UTF-8 编码的。包含 0x80 的最常见情况是 Windows-1252（通常称为“ANSI”）和字符

€

.