小编deo*_*rio的帖子

我正在为已经训练好的 NER 模型编写推理脚本，但我在将编码标记（它们的 id）转换为原始单词时遇到了麻烦。

# example input
df = pd.DataFrame({'_id': [1], 'body': ['Amazon and Tesla are currently the best picks out there!']})

# calling method that handles inference:
ner_model = NER()
ner_model.recognize_from_df(df, 'body')

# here is only part of larger NER class that handles the inference:
def recognize_from_df(self, df: pd.DataFrame, input_col: str):
    predictions = []
    df = df[['_id', input_col]].copy()
    dataset = Dataset.from_pandas(df)
    # tokenization, padding, truncation:
    encoded_dataset = dataset.map(lambda examples: self.bert_tokenizer(examples[input_col], 
                                      padding='max_length', truncation=True, max_length=512), batched=True)
    encoded_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'], device=device)
    dataloader …