Create README.md

README.md

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+# AToMiC Prebuilt Indexes
+
+## Example Usage:
+
+### Reproduction
+
+Toolkits:
+https://github.com/TREC-AToMiC/AToMiC/tree/main/examples/dense_retriever_baselines
+
+```bash
+# 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
+
+```python
+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)
+```