asigalov61/coyo-hd-11m-llavanext-all-MiniLM-L6-v2

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Sentence transformer (all_MiniLM_L6_v2) embeddings for all long llava summaries in coyo-hd-11m-llavanext dataset (07-03-2024 version)

Sentence Transformers

coyo-hd-11m-llavanext

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Instructions

PLEASE NOTE: You will need at least 40GB GPU to use the embeddings

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Depencencies

!pip install huggingface_hub -U
!pip install datasets -U
!pip install sentence-transformers -U

Imports

from huggingface_hub import hf_hub_download

from datasets import load_dataset

from sentence_transformers import SentenceTransformer
from sentence_transformers import util

import torch
import numpy as np

import tqdm

coyo dataset and coyo dataset embeddings

coyo_dataset = load_dataset("CaptionEmporium/coyo-hd-11m-llavanext")

hf_hub_download(repo_id="asigalov61/coyo-hd-11m-llavanext-all-MiniLM-L6-v2", 
                repo_type='dataset', 
                filename="coyo_hd_11m_llavanext_all_MiniLM_L6_v2_llava_captions_embeddings_07_03_24.npz",
                local_dir='.'
                )

Loading code

coyo_embeddings_cpu = np.load('coyo_hd_11m_llavanext_all_MiniLM_L6_v2_llava_captions_embeddings_07_03_24.npz')['data']

coyo_embeddings_cpu = torch.from_numpy(coyo_embeddings_cpu).cuda()
coyo_embeddings_cpu = util.normalize_embeddings(coyo_embeddings_cpu)

model = SentenceTransformer('all-MiniLM-L6-v2', device='cuda')

Inference code

torch.cuda.empty_cache()

queries_corpus = ['Capital of France',
                  'Love, peace and happiness',
                  'Cute cats in tacky suits :)'
                  ]

queries_embeddings = model.encode(queries_corpus, device='cuda', show_progress_bar=True, convert_to_tensor=True)
queries_embeddings = util.normalize_embeddings(queries_embeddings)

results = util.semantic_search(queries_embeddings, coyo_embeddings_cpu, score_function=util.dot_score)

closest_index = results[0][0]['corpus_id']

print('=' * 70)
print('Best match index:', closest_index)
print('=' * 70)
print('Best match corpus entry:', coyo_dataset['train'][closest_index])
print('=' * 70)

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