Mistral model generates the same embeddings for different input texts

🤗Transformers
1

Hi, I am using pre-trained LLM to get a representative embedding for an input text. But the results are wired. The embeddings are all the same regardless of input texts.

Codes:

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.")

The output tokenized inputs are different, but the generated embeddings are the same.

Detailed output:

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.

Does anyone know where the problem is? Many thanks!

2

It turns out that the embedding of the special beginning token in this model remains almost the same for different input texts. I guess that’s the reason. We can’t use the embedding of the beginning token to represent the whole sequence in this model. Got the answer from python - Mistral model generates the same embeddings for different input texts - Stack Overflow

3

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