ModernBERT-Small (Compositional Byte-N-Gram 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. This checkpoint replaces that table entirely with a compositional byte-n-gram
embedding, inspired by fastText's subword hashing trick: each vocabulary token is decomposed
into its raw UTF-8 byte sequence, every byte n-gram (n = 1 to 4) is extracted from it and hashed
into one of 8,192 shared buckets, and the token's representation is the mean of its buckets'
embeddings (a 128-dimensional EmbeddingBag table, ~1.0M parameters total) rather than a
dedicated per-token row. This composed vector is then projected up to the model's 384-dimensional
hidden size. The same shared n-gram bucket table is used to produce the MLM output head's logits
(tie_word_embeddings=true is required for this embedding type), so the entire vocabulary's
token representations are generated algorithmically from ~1M shared subword-hash parameters
instead of ~11.7M independent per-token ones. This particular checkpoint uses no token-specific
residual on top of the composed representation (compositional_residual_mode=none) -- it is the
"composition-only" control in a broader sweep that also tests frequency-gated residual variants.
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-phase6-composition-babylm2026",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
"remg1997/modernbert-small-phase6-composition-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.