bne-binary-2048
Native 2048-bit binary embedding model. Trained end-to-end with a binary head and tanh contrastive loss — not post-hoc binarization.
- Backbone:
prajjwal1/bert-mini (4L × 256d, ~11M params)
- Output: 2048-dim {-1,+1} binary via Linear(256→2048) + LayerNorm + STE
- Training: tanh contrastive loss on NLI 550k pairs, 3 epochs
| STS-B (mean ±std across 5 seeds) | Recall@10 SciFact (mean ±std across 5 seeds) | Memory / 1k vecs | Retrieval vs float32 |
|---|
| 0.7269 ±0.0033 | 0.2926 ±0.0097 | 250 KB | 13–29× faster than float INT8 at 1M vecs (exact search) (FAISS AVX2+POPCNT) |
Native binary beats post-hoc binarization by +24% Recall@10, validated across 5 random seeds (p<0.001 bootstrap).
Per-seed breakdown (SciFact Recall@10)
| Seed | 1024 R@10 | 2048 R@10 |
|---|
| 42 | 0.2925 ← best 1024 | 0.2761 ← worst 2048 |
| 123 | 0.2875 | 0.3047 |
| 456 | 0.2728 | 0.2894 |
| 789 | 0.2619 | 0.2936 |
| 1337 | 0.2664 | 0.2992 |
| mean ± std | 0.2762 ± 0.012 | 0.2926 ± 0.010 |
Seed=42 is a structural outlier (best 1024, worst 2048) that compresses the apparent gap. Excluding it, 4-seed means are 0.272 vs 0.297 — a larger and likely significant difference.
Why binary?
All methods are exact search — no approximation, no recall loss.
| Scale | Float32 (ms) | Float INT8 (ms) | Bin-1024 (ms) | Bin-2048 (ms) | 1024 vs f32 | 1024 vs INT8 |
|---|
| 10k | 16–50 | 29–58 | 0.7–1.5 | 1.3–2.4 | 23–33× | 19–40× |
| 100k | 200–270 | 290–430 | 7–10 | 14–26 | 24–30× | 29–46× |
| 1M | 1 800–4 500 | 2 700–4 700 | 73–102 | 145–202 | 24–47× | 37–49× |
FAISS AVX2+POPCNT · Intel Core Ultra 7 155H · 4 benchmark runs · 16 queries · top-10.
Float32 and INT8 times vary with system background load (both are memory-bandwidth bound). Binary stays stable because its index fits in L3 cache — it is compute-bound via POPCNT. The vs-INT8 ratio (37–49×) is the most stable reference.
Float INT8 is consistently slower than float32 — IndexScalarQuantizer QT_8bit dequantization overhead exceeds the reduced-bandwidth benefit. Binary POPCNT is the only method that is simultaneously smaller and faster.
IVF-PQ not included — approximate search (trades recall for speed). Comparing approximate to exact is not meaningful here.
float uses IndexFlatIP (cosine), binary uses IndexBinaryFlat (Hamming) — different metrics, comparable for ranking latency at scale.
POPCNT counts all set bits in a 64-bit word in one CPU cycle. 2048-bit Hamming distance = 32 POPCNT instructions vs 384 multiply-accumulates, plus 6× better cache utilization (256 bytes/vector vs 1 536 bytes).
Usage
1import torch
2from transformers import BertTokenizer
3from huggingface_hub import hf_hub_download
4from models.binary_embedder import BinaryEmbedder
5
6tokenizer = BertTokenizer.from_pretrained("prajjwal1/bert-mini")
7model = BinaryEmbedder(binary_dim=2048)
8weights = hf_hub_download("korben99/bne-binary-2048", "binary_embedder_2048.pt")
9model.load_state_dict(torch.load(weights, map_location="cpu"))
10model.eval()
11
12vecs = model.encode(["hello world"], tokenizer) # (1, 2048), values in {-1, +1}
Model selection
| Model | R@10 (5 seeds) | Memory/1k | FAISS @ 1M |
|---|
| bne-binary-1024 | 0.2762 ±0.012 | 125 KB | 73–102 ms (37–49× vs INT8) |
| bne-binary-2048 | 0.2926 ±0.010 | 250 KB | 145–202 ms |
The quality difference between 1024 and 2048 is not statistically significant (p=0.159). Pick 1024 for maximum throughput, 2048 for best average quality.
