HuggingFace学习笔记--AutoModel的使用

1--AutoModel的使用

官方文档

        AutoModel 用于加载模型；

1-1--简单Demo

测试代码：

from transformers import AutoTokenizer, AutoModelif __name__ == "__main__":checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"tokenlizer = AutoTokenizer.from_pretrained(checkpoint) raw_input = ["I love kobe bryant.", "Me too."]inputs = tokenlizer(raw_input, padding = "longest", truncation = True, max_length = 512, return_tensors = "pt")# 加载指定的模型model = AutoModel.from_pretrained(checkpoint)print("model: \n", model)outputs = model(**inputs)print("last_hidden_state: \n", outputs.last_hidden_state.shape) # 打印最后一个隐层的输出维度# [2 7 768] batch_size为2，7个token，每个token的维度为768

输出结果：

last_hidden_state: torch.Size([2, 7, 768])# 最后一个隐层的输出
# batchsize为2，表示两个句子
# 7表示token数，每一个句子有7个token
# 768表示特征大小，每一个token的维度为768

测试代码：

from transformers import AutoTokenizer, AutoModelForSequenceClassificationif __name__ == "__main__":checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"tokenlizer = AutoTokenizer.from_pretrained(checkpoint) raw_input = ["I love kobe bryant.", "Me too."]inputs = tokenlizer(raw_input, padding = "longest", truncation = True, max_length = 512, return_tensors = "pt")model2 = AutoModelForSequenceClassification.from_pretrained(checkpoint) # 二分类任务print(model2)outputs2 = model2(**inputs)print(outputs2.logits.shape)

运行结果：

torch.Size([2, 2])
# 两个句子，每个句子二分类的概率