Python Mistral模型为不同的输入文本生成相同的嵌入

我正在使用预先训练的LLM来为输入文本生成代表性嵌入.但无论输入文本如何，输出嵌入都是相同的.

代码:

from transformers import pipeline, AutoTokenizer, AutoModel
import numpy as np
PRETRAIN_MODEL = 'mistralai/Mistral-7B-Instruct-v0.2'
tokenizer = AutoTokenizer.from_pretrained(PRETRAIN_MODEL)
model = AutoModel.from_pretrained(PRETRAIN_MODEL)

def generate_embedding(document):
    inputs = tokenizer(document, return_tensors='pt')
    print("Tokenized inputs:", inputs)
    with torch.no_grad():
        outputs = model(**inputs)
    embedding = outputs.last_hidden_state[0, 0, :].numpy()
    print("Generated embedding:", embedding)
    return embedding

text1 = "this is a test"
text2 = "this is another test"
text3 = "there are other tests"

embedding1 = generate_embedding(text1)
embedding2 = generate_embedding(text2)
embedding3 = generate_embedding(text3)

are_equal = np.array_equal(embedding1, embedding2) and np.array_equal(embedding2, embedding3)

if are_equal:
    print("The embeddings are the same.")
else:
    print("The embeddings are not the same.")

打印的 token 不同，但打印的嵌入是相同的.输出:

Tokenized inputs: {'input_ids': tensor([[   1,  456,  349,  264, 1369]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}
Generated embedding: [-1.7762679  1.9293272 -2.2413437 ...  2.6379988 -3.104867   4.806004 ]
Tokenized inputs: {'input_ids': tensor([[   1,  456,  349, 1698, 1369]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}
Generated embedding: [-1.7762679  1.9293272 -2.2413437 ...  2.6379988 -3.104867   4.806004 ]
Tokenized inputs: {'input_ids': tensor([[   1,  736,  460,  799, 8079]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}
Generated embedding: [-1.7762679  1.9293272 -2.2413437 ...  2.6379988 -3.104867   4.806004 ]
The embeddings are the same.

有人知道问题出在哪里吗？谢谢！