id stringlengths 22 22 | content stringclasses 1
value | vector dict |
|---|---|---|
projected-01550341-010 | {
"F1014": 6,
"F1038": 18,
"F1998": 38,
"F2001": 28,
"F2003": 13,
"F2013": 34,
"F2018": 7,
"F2020": 10,
"F2027": 5,
"F2031": 23,
"F2034": 34,
"F2050": 3,
"F2077": 25,
"F2079": 30,
"F2080": 64,
"F2087": 31,
"F2103": 59,
"F2110": 101,
"F2114": 50,
"F2116": 31,
"F2120": 71,
"F21... | |
projected-48082827-002 | {
"F1014": null,
"F1038": null,
"F1998": null,
"F2001": 44,
"F2003": 22,
"F2013": null,
"F2018": null,
"F2020": null,
"F2027": null,
"F2031": null,
"F2034": 60,
"F2050": null,
"F2077": null,
"F2079": null,
"F2080": null,
"F2087": null,
"F2103": null,
"F2110": null,
"F2114": null,
... | |
projected-00011315-001 | {"F1014":null,"F1038":null,"F1998":null,"F2001":null,"F2003":null,"F2013":null,"F2018":null,"F2020":(...TRUNCATED) | |
projected-22583399-000 | {"F1014":null,"F1038":null,"F1998":null,"F2001":59,"F2003":null,"F2013":null,"F2018":null,"F2020":nu(...TRUNCATED) | |
projected-00019961-005 | {"F1014":14,"F1038":null,"F1998":null,"F2001":38,"F2003":null,"F2013":42,"F2018":44,"F2020":28,"F202(...TRUNCATED) | |
projected-00019961-006 | {"F1014":null,"F1038":null,"F1998":null,"F2001":2,"F2003":null,"F2013":null,"F2018":42,"F2020":9,"F2(...TRUNCATED) | |
projected-00021888-013 | {"F1014":null,"F1038":null,"F1998":null,"F2001":42,"F2003":null,"F2013":null,"F2018":null,"F2020":nu(...TRUNCATED) | |
projected-02457603-002 | {"F1014":null,"F1038":null,"F1998":null,"F2001":65,"F2003":null,"F2013":17,"F2018":null,"F2020":59,"(...TRUNCATED) | |
projected-03426698-010 | {"F1014":21,"F1038":null,"F1998":null,"F2001":11,"F2003":null,"F2013":null,"F2018":59,"F2020":null,"(...TRUNCATED) | |
projected-00142196-002 | {"F1014":null,"F1038":null,"F1998":null,"F2001":null,"F2003":null,"F2013":null,"F2018":40,"F2020":nu(...TRUNCATED) |
End of preview. Expand in Data Studio
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
AToMiC Prebuilt Indexes
Example Usage:
Reproduction
Toolkits: https://github.com/TREC-AToMiC/AToMiC/tree/main/examples/dense_retriever_baselines
# Skip the encode and index steps, search with the prebuilt indexes and topics directly
python search.py \
--topics topics/openai.clip-vit-base-patch32.text.validation \
--index indexes/openai.clip-vit-base-patch32.image.faiss.flat \
--hits 1000 \
--output runs/run.openai.clip-vit-base-patch32.validation.t2i.large.trec
python search.py \
--topics topics/openai.clip-vit-base-patch32.image.validation \
--index indexes/openai.clip-vit-base-patch32.text.faiss.flat \
--hits 1000 \
--output runs/run.openai.clip-vit-base-patch32.validation.i2t.large.trec
Explore AToMiC datasets
import torch
from pathlib import Path
from datasets import load_dataset
from transformers import AutoModel, AutoProcessor
INDEX_DIR='indexes'
INDEX_NAME='openai.clip-vit-base-patch32.image.faiss.flat'
QUERY = 'Elizabeth II'
images = load_dataset('TREC-AToMiC/AToMiC-Images-v0.2', split='train')
images.load_faiss_index(index_name=INDEX_NAME, file=Path(INDEX_DIR, INDEX_NAME, 'index'))
model = AutoModel.from_pretrained('openai/clip-vit-base-patch32')
processor = AutoProcessor.from_pretrained('openai/clip-vit-base-patch32')
# prebuilt indexes contain L2-normalized vectors
with torch.no_grad():
q_embedding = model.get_text_features(**processor(text=query, return_tensors="pt"))
q_embedding = torch.nn.functional.normalize(q_embedding, dim=-1).detach().numpy()
scores, retrieved = images.get_nearest_examples(index_name, q_embedding, k=10)
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