Instructions to use whr778/scaling-mmbert-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use whr778/scaling-mmbert-40k with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("whr778/scaling-mmbert-40k") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
scaling-mmbert-40k (Stage A, warmed base)
Research checkpoint from the mmBERT head-init data-scaling curve (private).
mmBERT-base with fresh GLiNER2 heads (from_encoder) warmed on ~40,000
structure/argument-dense event records (multilingual, assembled from public event
corpora; RAMS/WikiEvents held out to avoid leakage), 5 epochs.
This is the N=40k point's warmed base — the reusable head-init artifact.
It is not fine-tuned on any single downstream task; fine-tune it (e.g. on RAMS)
to obtain a usable event model. The paired fine-tune is whr778/scaling-mmbert-40k-rams.
Purpose
Measures how much structure/argument data is needed to warm mmBERT's fresh
extraction heads before downstream fine-tuning (head-initialization thesis). See
the experiment spec SCALING_CURVE_EXPERIMENT.md.
- Encoder:
jhu-clsp/mmBERT-base(native long-context, 2048 training window) - Stage-A corpus size: ~40,000 records
- Loss:
bce_posweight(pos_weight 4.0); differential-style LR (encoder 2e-5 / heads 5e-4)
Caveats
Research artifact, private, not benchmarked as a general model. Label quality is bounded by the source event corpora. One point on a scaling curve, not a release.
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