1--Model介绍

        Transformer的 model 一般可以分为：编码器类型（自编码）、解码器类型（自回归）和编码器解码器类型（序列到序列）；

        Model Head（任务头）是在base模型的基础上，根据不同任务而设置的模块；base模型只起到一个编码和建模特征的功能；

简单代码：

from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassificationif __name__ == "__main__":# 数据处理sen = "弱小的我也有大梦想！"tokenizer = AutoTokenizer.from_pretrained("hfl/rbt3")inputs = tokenizer(sen, return_tensors="pt")# 不带model head的模型调用model = AutoModel.from_pretrained("hfl/rbt3", output_attentions=True)output1 = model(**inputs)print(output1.last_hidden_state.size()) # [1, 12, 768]# 带model head的模型调用clz_model = AutoModelForSequenceClassification.from_pretrained("hfl/rbt3", num_labels=10)output2 = clz_model(**inputs)print(output2.logits.shape) # [1, 10]

2--AutoModel的使用

官方文档

        AutoModel 用于加载模型；

2-1--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