t2.train(train_sents)Traceback (most recent call last): File "<input>", line 1, in <module> AttributeError: 'BigramTagger' object has no attribute 'train'如何改正 完整代码如下：import nltk import random from nltk.corpus import brown from nltk import pos_tag, word_tokenize from nltk.tag import DefaultTagger, UnigramTagger, BigramTagger brown_tagged_sents = brown.tagged_sents(categories='news') size = int(len(brown_tagged_sents) * 0.9) train_sents = brown_tagged_sents[:size] test_sents = brown_tagged_sents[size:] t0 = DefaultTagger('NN') t1 = UnigramTagger(train_sents, backoff=t0) t2 = BigramTagger(train_sents, backoff=t1) t2.train(train_sents) Traceback (most recent call last): File "<input>", line 1, in <module> AttributeError: 'BigramTagger' object has no attribute 'train'

The error message indicates that the `BigramTagger` object does not have the `train` method. This is because `BigramTagger` is already trained during initialization.

To fix this error, you can remove the `t2.train(train_sents)` line and directly use the `t2` tagger to tag new sentences. For example:

sent = "This is a test sentence"
tokens = word_tokenize(sent)
tags = t2.tag(tokens)
print(tags)

相关问题

解释下面这段代码 def post_process(self, output): """语音后处理，将模型推理结果映射到文本""" encoder_out, encoder_out_lens, ctc_log_probs, \ beam_log_probs, beam_log_probs_idx = output batch_size = beam_log_probs.shape[0] num_processes = batch_size log_probs_idx = beam_log_probs_idx[:, :, 0] batch_sents = [] for idx, seq in enumerate(log_probs_idx): batch_sents.append(seq[:encoder_out_lens[idx]].tolist()) txt = map_batch(batch_sents, self.vocabulary, num_processes, True, 0)[0] return txt

这是一个语音识别模型的后处理函数，用于将模型的输出结果转换成文本。函数的输入参数output包含了模型的多个输出结果，包括encoder_out, encoder_out_lens, ctc_log_probs, beam_log_probs, beam_log_probs_idx。其中beam_log_probs和beam_log_probs_idx是集束搜索算法得到的结果，表示概率最大的若干个文本序列和它们对应的概率值和索引。

函数首先获取batch_size和num_processes，其中batch_size表示输入的音频序列个数，num_processes表示处理的并行进程数。然后从beam_log_probs_idx中获取每个音频序列对应的最佳文本序列的索引log_probs_idx，再根据encoder_out_lens获取每个音频序列的有效长度，将log_probs_idx中多余的部分截取掉，得到batch_sents，表示每个音频序列对应的最佳文本序列。最后调用map_batch函数将batch_sents映射到文本，并返回文本结果。map_batch函数是一个自定义的函数，用于将输入的文本序列映射到具体的文本内容，具体实现可能涉及到一个词表vocabulary，以及多进程并行处理的技巧。

for fileid in nltk.corpus.gutenberg.fileids(): num_chars=len(nltk.corpus.gutenberg.raw(fileid)) #统计字符数 num_words=len(nltk.corpus.gutenberg.words(fileid)) #统计单词数 num_sent=len(nltk.corpus.gutenberg.sents(fileid)) #统计句子数 num_vocab=len(set([w.lower() for w in nltk.corpus.gutenberg.words(fileid)])) print(int(num_chars / num_words), int(num_words / num_sent),int(num_words / num_vocab), fileid) File "<input>", line 6 print(int(num_chars / num_words), int(num_words / num_sent),int(num_words / num_vocab), fileid) ^ IndentationError: unindent does not match any outer indentation level

这个错误提示是缩进错误，可能是你的代码缩进不正确。你可以检查一下代码中的缩进，确保缩进正确。正确的代码应该是这样的：

for fileid in nltk.corpus.gutenberg.fileids():
    num_chars = len(nltk.corpus.gutenberg.raw(fileid))  # 统计字符数 
    num_words = len(nltk.corpus.gutenberg.words(fileid))  # 统计单词数 
    num_sent = len(nltk.corpus.gutenberg.sents(fileid))  # 统计句子数 
    num_vocab = len(set([w.lower() for w in nltk.corpus.gutenberg.words(fileid)])) 
    print(int(num_chars / num_words), int(num_words / num_sent), int(num_words / num_vocab), fileid)