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--- |
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license: cc-by-nc-sa-4.0 |
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size_categories: |
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- 10M<n<100M |
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tags: |
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- coyo |
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- coyo-hd-11m-llavanext |
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- sentence-transformers |
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- images |
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- image captions |
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- embeddings |
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--- |
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*** |
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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) |
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[Sentence Transformers](https://github.com/UKPLab/sentence-transformers) |
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[coyo-hd-11m-llavanext](https://huggingface.co/datasets/CaptionEmporium/coyo-hd-11m-llavanext) |
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*** |
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# Instructions |
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## PLEASE NOTE: You will need at least 40GB GPU to use the embeddings |
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*** |
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## Depencencies |
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```python |
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!pip install huggingface_hub -U |
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!pip install datasets -U |
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!pip install sentence-transformers -U |
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``` |
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## Imports |
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```python |
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from huggingface_hub import hf_hub_download |
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from datasets import load_dataset |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers import util |
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import torch |
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import numpy as np |
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import tqdm |
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``` |
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## coyo dataset and coyo dataset embeddings |
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```python |
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coyo_dataset = load_dataset("CaptionEmporium/coyo-hd-11m-llavanext") |
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hf_hub_download(repo_id="asigalov61/coyo-hd-11m-llavanext-all-MiniLM-L6-v2", |
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repo_type='dataset', |
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filename="coyo_hd_11m_llavanext_all_MiniLM_L6_v2_llava_captions_embeddings_07_03_24.npz", |
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local_dir='.' |
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) |
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``` |
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## Loading code |
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```python |
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coyo_embeddings_cpu = np.load('coyo_hd_11m_llavanext_all_MiniLM_L6_v2_llava_captions_embeddings_07_03_24.npz')['data'] |
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coyo_embeddings_cpu = torch.from_numpy(coyo_embeddings_cpu).cuda() |
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coyo_embeddings_cpu = util.normalize_embeddings(coyo_embeddings_cpu) |
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model = SentenceTransformer('all-MiniLM-L6-v2', device='cuda') |
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``` |
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## Inference code |
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```python |
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torch.cuda.empty_cache() |
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queries_corpus = ['Capital of France', |
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'Love, peace and happiness', |
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'Cute cats in tacky suits :)' |
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] |
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queries_embeddings = model.encode(queries_corpus, device='cuda', show_progress_bar=True, convert_to_tensor=True) |
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queries_embeddings = util.normalize_embeddings(queries_embeddings) |
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results = util.semantic_search(queries_embeddings, coyo_embeddings_cpu, score_function=util.dot_score) |
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closest_index = results[0][0]['corpus_id'] |
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print('=' * 70) |
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print('Best match index:', closest_index) |
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print('=' * 70) |
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print('Best match corpus entry:', coyo_dataset['train'][closest_index]) |
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print('=' * 70) |
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``` |
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*** |
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### Project Los Angeles |
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### Tegridy Code 2024 |
