incorrect nomic embeddings

[racinmat]

I was comparing the nomic embeddings and they are very different from the original version.

import pandas as pd
from more_itertools import chunked
from typing import List
import numpy as np
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
import torch
import os
from tqdm.notebook import tqdm
import json
from fastembed import SparseTextEmbedding, TextEmbedding

assert torch.cuda.is_available()
SEED = 25

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)

def embed(texts: List[str]):
    embeddings = model.encode(["clustering: " + t for t in texts], convert_to_tensor=True)
    embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
    embeddings = F.normalize(embeddings, p=2, dim=1)
    return embeddings.cpu().numpy()
    

import types
embedding_model = TextEmbedding(model_name="nomic-ai/nomic-embed-text-v1.5")

def embed_fast(texts: List[str]):
    embeddings = embedding_model.embed(["clustering: " + t for t in texts])
    # Force computation if embed_func returns a generator
    if isinstance(embeddings, types.GeneratorType):
        embeddings = np.array(list(embeddings))
    return embeddings

res1 = embed(data)
res1
array([[ 0.0478127 ,  0.07791077, -0.16337295, ..., -0.09588917,
        -0.01815554, -0.0391101 ],
       [ 0.00486873,  0.05552602, -0.17271836, ..., -0.06137123,
        -0.01570066,  0.00191791],
       [ 0.06523891,  0.0010743 , -0.18216555, ..., -0.07239737,
        -0.04038522, -0.01559034],
       ...,
       [ 0.00218753,  0.07514183, -0.20761742, ..., -0.06362353,
        -0.01809935, -0.05537352],
       [ 0.04343107,  0.09780316, -0.18920647, ..., -0.04333348,
        -0.00627593, -0.05098606],
       [ 0.07419404, -0.00313356, -0.17786372, ..., -0.12185868,
         0.00074316, -0.03453541]], dtype=float32)
         
res2 = embed_fast(data)
res2
array([[ 0.02929905,  0.04431156, -0.15961514, ..., -0.06745377,
        -0.05103005, -0.03928511],
       [ 0.01270135,  0.05443013, -0.17877947, ..., -0.07663167,
        -0.02374521, -0.01003957],
       [ 0.04591728,  0.03366161, -0.22145797, ..., -0.06816148,
        -0.04856569, -0.00438772],
       ...,
       [ 0.00895302,  0.06434014, -0.22018844, ..., -0.06492045,
        -0.02143181, -0.06246027],
       [ 0.02247173,  0.06561402, -0.19789155, ..., -0.05780106,
        -0.01238827, -0.0473954 ],
       [ 0.05192114,  0.00191367, -0.1852103 , ..., -0.10932592,
        -0.01199157, -0.03163441]], dtype=float32)

# compute cosine similarity between nth element in res1 and res2
from sklearn.metrics.pairwise import cosine_similarity
cosine_similarity(res1[0].reshape(1, -1), res2[0].reshape(1, -1))
array([[0.85182214]], dtype=float32)

so we can see that the cosine similarity is only 0.85, the vectors are completely different.

The fastembed normalize https://github.com/qdrant/fastembed/blob/main/fastembed/common/models.py#L49-L54 does not follow the normalization in https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#sentence-transformers it seems

[NirantK]

Ohh, we missed this completely in our tests — I'll look into this. Thanks a ton for reporting this!

[k4u5h1k]

Same for nomic embed v1. Any planned resolution on this?

[joein]

Hey @k4u5h1k, sorry for the late response, yes, we're working on it, we'll fix it soon

[twellck]

HI, I just added on comment on the PR that was merged (See #280 (comment))

The addition of the mean_pool solved cosine similarity issue but there are still inconsistencies about the normalization (especially when comparing to Nomic's documentation), I think this issue should be reopened in the mean time.