AbstractPhil/captionbert-8192-v2-b

captionbert-8192-b

A 58.3M standalone sentence encoder distilled from the geometric consensus of five BERT-family teachers. No expert models at inference: tokenizer + this model, 768-d L2-normalized output.

12 layers, 512-d, 8 heads, FFN 2048, 8192 position capacity. 0.53x bert-base.

This is the complete-corpus build: all 66 CC12M chunks, 31.9M rows. Its sibling captionbert-8192-v2 trained on 54 chunks because ModernBERT was missing from 10 of them; those were repaired and gate-verified before this run.

from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2-B", trust_remote_code=True)
tok   = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")

emb = model.encode(["a cat on a windowsill", "a feline by the window"])   # (2, 768)
(emb[0] @ emb[1]).item()

model.attach_amoe()          # this repo's NATIVE arms -- see the warning below
emb = model.encode(["a cat on a windowsill"])

Benchmark

model	params	STS-B	SICK-R	STS12	STS13	STS14	STS15	STS16	BIOSSES	mean
bert-base	109.5M	0.4729	0.5865	0.3087	0.5988	0.4773	0.6029	0.6373	0.5469	0.5289
ModernBERT-base	149.0M	0.4215	0.5479	0.3527	0.4247	0.3795	0.5349	0.4174	0.5630	0.4552
roberta-base	124.6M	0.5436	0.6296	0.3211	0.5631	0.4522	0.6134	0.6198	0.5777	0.5401
albert-base-v2	11.7M	0.4784	0.5364	0.3101	0.4831	0.3809	0.5542	0.5491	0.4863	0.4723
distilbert	66.4M	0.5717	0.6424	0.4344	0.6490	0.5410	0.6663	0.6854	0.5162	0.5883
captionbert-8192-b	58.3M	0.5752	0.6548	0.5012	0.6037	0.5470	0.7146	0.6782	0.5500	0.6031
captionbert-8192-b + arms	63.2M	0.7675	0.7374	0.6706	0.7381	0.6945	0.8109	0.7695	0.6472	0.7295
captionbert-8192-v2	58.3M	0.5747	0.6526	0.5051	0.5995	0.5452	0.7136	0.6776	0.5933	0.6077
all-MiniLM-L6-v2	22.7M	0.8203	0.7758	0.7237	0.8058	0.7559	0.8539	0.7899	0.8144	0.7925

All ten models measured in one harness, same eight tasks, mean-pooled and L2-normalized, no task tuning. Spearman correlation; mean is the unweighted average over the eight.

all-MiniLM-L6-v2 was contrastively trained on 1B+ curated sentence pairs. It is listed for scale, not as a peer -- nothing here saw a similarity label.

The trunk beats every teacher it was distilled from, and the best of them (distilbert, .5883) by +.0194 -- at 13% of their combined 461M parameters, having never seen a similarity label. The margin comes mostly from STS12, where every teacher collapses to .31-.43 and the trunk holds .50.

With arms it clears the best teacher by +.14 and closes to within .063 of a model trained on a billion curated pairs.

Mean-pooled BERT-family encoders are known-weak sentence encoders -- that is the reason Sentence-BERT exists -- so beating them is an efficiency result rather than a state-of-the-art one. The MiniLM row is in the table to keep that honest.

Geometry

model	self_cos	erank
bert-base	+0.6071	32.8
ModernBERT-base	+0.9001	26.1
roberta-base	+0.9594	19.8
albert-base-v2	+0.7473	20.9
distilbert	+0.6920	31.1
captionbert-8192-b	+0.1411	36.1
captionbert-8192-b + arms	+0.0984	55.5
captionbert-8192-v2	+0.1396	36.6
all-MiniLM-L6-v2	+0.0251	86.7

self_cos is the isotropy gauge: the mean cosine between unrelated sentences. Mean-pooled BERT-family embeddings sit in a narrow cone (+.61 to +.96), where cosine cannot discriminate. erank is the participation ratio -- how many of the 768 directions carry variance.

Both track capability almost perfectly across all ten models, and isotropy is the mechanism: no isotropy objective appears anywhere in the training stack. The arms then lift erank 36.6 -> 57.6, the first evidence in this line that adaptation adds usable directions rather than only rotating them.

More data bought nothing (and that is the finding)

-b trained on 19% more rows for 19% more steps than -v2. Head to head:

	v2 (54ch, 26.9M)	b (66ch, 31.9M)	delta
8-task mean, bare	.6077	.6031	-.0046
7 tasks excluding BIOSSES	--	--	+.0009
erank (STS-B)	36.6	36.1	-0.5
self_cos (STS-B)	+.1396	+.1411	+.0015
8-task mean, native arms	.7287	.7295	+.0008

The entire -.0046 comes from BIOSSES, which is 100 rows -- a 0.4-sigma move. Everything else is a dead heat.

The ceiling is TEACHER AGREEMENT, not corpus size. The consensus target uses 28.7 of 768 directions: five BERT-family encoders only agree on ~29, and no amount of the same distribution raises that. The trunk reaches erank ~103 in domain but ~36 out of it -- the structure it builds on captions does not transfer. The next lever is heterogeneous teachers, measurable at the consensus stage before a single training step.

AMOE arms are TRUNK-BOUND -- use this repo's

Three 1.6M-parameter anchors on the frozen trunk, under a trained dispatch. Anchors toggle bit-exact, so one artifact serves both the unsupervised baseline and the adapted model.

mask	STS-B	SICK-R	mean (8 tasks)
OFF (bare trunk)	.5752	.6548	.6031
equiv only	.7219	.7200	.6842
simplify only	.5995	.6603	.6254
paraphrase only	.6137	.6612	.6295
all three	.7675	.7374	.7295

An -only row is that arm as damped by the dispatch -- masking never renormalizes, so it reads lower than the same anchor trained alone.

Do not attach captionbert-8192-v2's arms to this trunk. Measured:

configuration	mean
v2 arms on v2	.7287
v2 arms on -b	.6863
+ re-aligned routing keys	.6987
-b native anchors	.7295

Transferring the arms costs 31% of their gain. Re-training only the 1,536 routing keys recovers 29% of that; retraining the anchors recovers all of it. 71% of the loss is in the anchors themselves.

These two trunks are indistinguishable on eight STS tasks and on geometry, yet 1.6M adapter parameters tell them apart -- adapters read the residual stream and the task gauges read the pooled output, and the stream carries trunk identity the output does not. Budget one anchor set per trunk (~18 min).

attach_amoe() resolves this repo's own arms by default. Files are under amoe/b-collective/. See amoe-lora.

How it was built

1. Five teachers embedded 33M CC12M llava-next captions (mean-pooled, 768-d).
2. One global whitened Procrustes map per teacher into bert-base's frame, fit on a stratified random sample and reported out-of-sample (worst arm retains 95% of its in-sample R@1 at 1,833x chance).
3. Consensus = normalized centroid of the aligned teachers, per chunk.
4. Student trained from scratch: InfoNCE(T=0.07) + per-sample MSE against the consensus. Pure Adam, no weight decay. 31.9M rows, 62,312 steps at batch 2048, ~6.4 h on one RTX 6000 Pro.

The alignment maps in maps/ are the same maps v2 used -- refitting them would put the consensus targets in a different frame with no signal in the loss.

Known limits

• Consensus rank ~28.7 of 768. The model's ceiling, and a property of teacher agreement rather than of this model.
• Alignment quality varies by teacher. Out-of-sample cosine into the bert frame: distil .625, roberta .372, albert .331, modern .327 -- the ordering tracks architectural distance from bert-base.
• Single seed. The AMOE results carry a measured seed spread of .003-.005; the trunk does not have one.
• Trained on image captions; expect caption-like text to be its strongest domain.
• BIOSSES is 100 rows. Treat any single-task delta there as noise.

Files

model.safetensors            the trunk, HF format
config.json                  AutoModel config (auto_map -> modeling_captionbert)
modeling_captionbert.py      CaptionBertV2Model + attach_amoe/detach_amoe
checkpoints/                 training checkpoints (final_model.pt is the ship)
maps/                        alignment maps -- SHARED with v2, do not refit
amoe/b-collective/           native anchors + dispatch + metrics

Output convention

field	shape
last_hidden_state	(B, L, 512)	token states
pooler_output	(B, 768)	the embedding, L2-normalized
embedding	(B, 768)	alias

geolip-captionbert-8192 (v1) returned the pooled embedding as last_hidden_state. If porting v1 code, use pooler_output.

Citation

@misc{abstractphil2026captionbertb,
  title  = {captionbert-8192-b: consensus distillation on the complete CC12M corpus},
  author = {AbstractPhil},
  year   = {2026},
  url    = {https://huggingface.co/AbstractPhil/captionbert-8192-v2-B}
}

MIT.