问题描述
我正在研究HuggingFace的转移学习功能（特别是用于命名实体识别的功能）。首先，我对变压器体系结构有些陌生。我在他们的网站上简要介绍了他们的示例：
from transformers import pipeline
nlp = pipeline("ner")
sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO,therefore very" \
"close to the Manhattan Bridge which is visible from the window."
print(nlp(sequence))
我想做的是保存并在本地运行，而不必每次都下载“ ner”模型（大小超过1 GB）。在他们的文档中，我看到您可以使用“ pipeline.save_pretrained（）”函数将管道保存到本地文件夹。结果是将各种文件存储到特定的文件夹中。
我的问题是保存后如何将该模型重新加载到脚本中以继续进行分类？ “ pipeline.save_pretrained（）”的输出是多个文件。
这是我到目前为止尝试过的：
1：遵循有关管道的文档
pipe = transformers.TokenClassificationPipeline(model="pytorch_model.bin",tokenizer='tokenizer_config.json')
2：在ner上遵循HuggingFace示例：
from transformers import AutoModelForTokenClassification,AutoTokenizer
import torch
model = AutoModelForTokenClassification.from_pretrained("path to folder following .save_pretrained()")
tokenizer = AutoTokenizer.from_pretrained("path to folder following .save_pretrained()")
label_list = [
"O",# Outside of a named entity
"B-MISC",# Beginning of a miscellaneous entity right after another miscellaneous entity
"I-MISC",# Miscellaneous entity
"B-PER",# Beginning of a person's name right after another person's name
"I-PER",# Person's name
"B-ORG",# Beginning of an organisation right after another organisation
"I-ORG",# Organisation
"B-LOC",# Beginning of a location right after another location
"I-LOC" # Location
]
sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO,therefore very" \
"close to the Manhattan Bridge."
# Bit of a hack to get the tokens with the special tokens
tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
inputs = tokenizer.encode(sequence,return_tensors="pt")
outputs = model(inputs)[0]
predictions = torch.argmax(outputs,dim=2)
print([(token,label_list[prediction]) for token,prediction in zip(tokens,predictions[0].tolist())])
这会产生错误：列表索引超出范围
我还尝试仅打印出不返回令牌及其实体的文本格式的预测。
任何帮助将不胜感激！
解决方法
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