Instructions to use whr778/scaling-mmbert-100k-rams with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use whr778/scaling-mmbert-100k-rams with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("whr778/scaling-mmbert-100k-rams") # 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-100k-rams (Stage B, RAMS fine-tune)
Research checkpoint from the mmBERT head-init data-scaling curve (private).
The warmed base whr778/scaling-mmbert-100k (mmBERT-base
from_encoder, warmed on ~100,000 structure/argument records) fine-tuned on RAMS
under the fixed mmbert-base-rams recipe (15 epochs, bce_posweight 4.0, native
long-context, argument-strict checkpoint selection).
RAMS blind-test (871 docs), strict micro-F1
| metric | score |
|---|---|
| event_argument_strict | 0.158 |
| event_trigger_strict | 0.732 |
This is the N=100k point's y-value on the curve: RAMS argument-strict F1 vs Stage-A corpus size. Curve context: N=0 (fresh heads → RAMS) = 0.050; a DeBERTa-v3 fastino warm-start (254K) reaches 0.462 as a cross-encoder reference.
Purpose
Quantifies whether warming mmBERT's heads on ~100,000 records lifts downstream RAMS
argument extraction. See SCALING_CURVE_EXPERIMENT.md and PAPER.md §10.
Caveats
Research artifact, private. One point on a scaling curve, not a general release.
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