Fill-Mask
Transformers
Safetensors
Urdu
modernbert
urdu
encoder
masked-language-modeling
long-context
8k-context
urblimp
zero-shot
benchmark
Instructions to use ProximaAI/urnova-95m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProximaAI/urnova-95m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ProximaAI/urnova-95m")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ProximaAI/urnova-95m") model = AutoModelForMaskedLM.from_pretrained("ProximaAI/urnova-95m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 881 Bytes
d36bc6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | urnova-95m
Copyright 2026 The urnova authors
The original urnova model weights, tokenizer artifacts, code, documentation,
benchmark reports, and visualizations included in this repository are made
available under the Apache License, Version 2.0, to the extent of the rights
held by their respective copyright holders.
The model was trained from scratch using an Urdu corpus derived from HPLT 3.0.
HPLT licenses the packaging of its dataset under CC0 but states that it does
not own the underlying extracted web text.
The Apache License does not grant rights in the underlying training text,
third-party content, trademarks, privacy rights, publicity rights, database
rights, or text that a model may reproduce.
No raw HPLT training documents are distributed in this model repository.
See DATA_AND_THIRD_PARTY_NOTICE.md for provenance, limitations, and the
takedown procedure.
|