ModernBERT-Small (Factorized Linear Embeddings) — BabyLM 2026 Strict-Small
This model is a ModernBERT-Small masked language model (RoPE, GeGLU, alternating local/global
attention, 384 hidden size, 16 layers, 6 attention heads) trained from scratch on the
BabyLM 2026 Strict-Small 10M-word corpus, as part of an ablation study on
parameter-efficient token embedding layers for developmentally-plausible pretraining under the
BabyLM Challenge's strict-small compute and data budget.
Embedding design
Standard transformer token embedding tables scale as vocab_size x hidden_size, which for this
model's 30,522-token vocabulary and 384 hidden size would be an 11.7M-parameter dense lookup
table -- a large fraction of the model's total parameter budget under the strict-small
constraint. This checkpoint instead uses an ALBERT-style linear factorization: tokens are
first embedded into a smaller 128-dimensional bottleneck space, then linearly projected up to the
model's 384-dimensional hidden size, replacing one large vocab_size x hidden_size matrix with
two much smaller ones (vocab_size x 128 and 128 x 384). The output projection (tie_word_ embeddings=false) uses its own separate decoder rather than sharing the input embedding weights.
This is one variant in a broader comparison of embedding-layer parameterizations (dense, linear and MLP-style factorization, tensor-train decomposition, deterministic Fourier expansion, and compositional/frequency-adaptive variants) evaluated under identical data, tokenizer, optimizer, and training budget, to isolate the effect of the embedding layer's parameterization on downstream BabyLM evaluation performance.
Training data
BabyLM 2026 Strict-Small corpus (~10M words), tokenized with a byte-level BPE tokenizer trained on the same corpus (vocab size 30,522). No external data, synthetic augmentation, or human annotation beyond the corpus as officially released.
Usage
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained(
"remg1997/modernbert-small-modernbert-small-factorized-linear-babylm2026",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
"remg1997/modernbert-small-modernbert-small-factorized-linear-babylm2026"
)
Each chck_{N}M branch of this repository corresponds to a BabyLM-Challenge compliance
checkpoint (one per N million words of training data seen); main points at the final,
fully-trained checkpoint.
Evaluation
Evaluated with the official babylm-eval harness
(BLiMP, EWoK, entity tracking, COMPS, Global PIQA, reading-time correlation, GLUE/SuperGLUE
fine-tuning, and Age-of-Acquisition word-surprisal correlation) under the strict-small track.