remg1997/modernbert-small-phase6-composition-frequency-shuffled-babylm2026

ModernBERT-Small (Compositional + Shuffled-Frequency Residual, Control) — 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's base representation is the same compositional byte-n-gram embedding used across this ablation sweep: each vocabulary token is decomposed into its raw UTF-8 byte sequence, every byte n-gram (n = 1 to 4) is extracted 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) rather than a dedicated per-token row.

On top of that shared composed representation, this variant adds a frequency-gated per-token residual -- a learned vocab_size x 128 embedding added to the composed vector, scaled per token by frequency / (frequency + tau) (tau = 100) so common tokens lean more on their own residual and rare tokens rely mostly on the composition. Critically, this checkpoint is the shuffled-frequency control: the frequency values used to compute each token's gating weight are randomly permuted across the vocabulary (fixed seed 42) before being applied, so a token's residual is gated by an unrelated token's frequency rather than its own. This isolates whether any benefit from the frequency-gated residual comes from the correct frequency signal, or merely from having some per-token residual-plus-gating mechanism -- a null-effect control against the true frequency-gated variant in this sweep.

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-frequency-shuffled-babylm2026",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "remg1997/modernbert-small-phase6-composition-frequency-shuffled-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.