Instructions to use arthrod/c3750-mdeberta_base-vanilla-1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use arthrod/c3750-mdeberta_base-vanilla-1-2 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("arthrod/c3750-mdeberta_base-vanilla-1-2") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
c3750-mdeberta_base-vanilla-1-2
Superseded. This is an early checkpoint, kept because it has a DOI. For current pt-BR PII detection use arthrod/gliner-mmbert-small-ptbr-pii-full-3x-v1 or the OpenAI Privacy Filter fine-tune arthrod/gliner-opf-ptbr-pii-v1; results for both are in arthrod/gliner-opf-ptbr-pii-bench-v1.
Legacy "nightmare" PII run on microsoft/mdeberta-v3-base with span/marker head. Multiple checkpoints up to step 3750.
- Version: checkpoints (250, 1000 … 3750)
- Backbone: mDeBERTa-v3-base
- GLiNER mode: vanilla / markerV0
- Weights per ckpt: ~1.15 GB
Training
- Dataset: legacy multi-PII experiment
Evaluation
Not benchmarked — legacy experimental run.
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