Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. filelists/train.txt ----start emotion extract------- Traceback (most recent call last): File "emotion_extract.py", line 133, in <module> for idx, line in enumerate(f.readlines()): UnicodeDecodeError: 'gbk' codec can't decode byte 0xaa in position 52: illegal multibyte sequence

这个错误是因为你的代码试图使用 gbk 编码打开一个文件，但是文件中包含了非法的多字节序列。你可以试着将文件编码转换为 UTF-8 或者使用其他适当的编码方式打开文件。如果你使用的是 Python 3，可以使用 `open` 函数的 `encoding` 参数指定正确的编码方式，例如：

with open('filelists/train.txt', 'r', encoding='utf-8') as f:
    for idx, line in enumerate(f.readlines()):
        # your code here

如果你的文件确实是 gbk 编码，你可以尝试使用 `errors='ignore'` 参数来忽略非法字符，但这可能会导致读取到的文本内容不完整。

相关问题

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

In natural language processing (NLP) models, special tokens serve as placeholders or indicators for specific types of inputs or tasks that require unique handling. These tokens are usually added to the vocabulary during model initialization or can be dynamically incorporated during training. They help the model understand and process:

1. **Padding** and **masking**: `[PAD]` and `[MASK]` tokens are used to handle variable-length sequences and indicate missing or padding elements.
2. **Segmentation**: `[SEP]` or `[CLS]` tokens separate sentences or segments in multi-token inputs, like in BERT or RoBERTa.
3. **Classification and tagging**: `[CLS]` is often used as the first token in a sequence for classification tasks, while `[MASK]` might be used for masked language modeling.
4. **Positional information**: `[unused1]`, `[unused2]`, etc., denote positions in the sequence.

When these special tokens are present, their word embeddings must be fine-tuned or trained along with the rest of the model parameters. Fine-tuning ensures that the model adapts to the specific task at hand, while training from scratch would initialize them anew without prior knowledge. This adaptation helps the model capture the context-specific meaning of these tokens and improve overall performance.