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
| # Data and Third-Party Notice | |
| ## Scope | |
| The `urnova-95m` model was trained from scratch. It was not initialized from | |
| mBERT, XLM-R, DunbaaBERT, HPLT-BERT Urdu, or another third-party model | |
| checkpoint. | |
| The original urnova model weights and locally generated tokenizer artifacts | |
| are distributed under the Apache License, Version 2.0, to the extent of the | |
| rights held by the urnova publisher. | |
| This licence does not relicense the underlying pretraining documents. | |
| ## Training-data source | |
| The production corpus was derived from the Urdu Arabic-script portion of | |
| HPLT 3.0. | |
| HPLT states that: | |
| - it does not own the text from which the dataset was extracted; | |
| - it licenses the packaging of the dataset under Creative Commons CC0; | |
| - users remain responsible for compliance with applicable law. | |
| Official dataset identifier: | |
| `HPLT/HPLT3.0` | |
| No raw or reconstructed HPLT documents are included in this model repository. | |
| ## Recorded production provenance | |
| The frozen production lineage records: | |
| - source documents: 1,921,027; | |
| - source-token count: 1,246,973,570; | |
| - production training tokens processed: approximately 4.0 billion; | |
| - source corpus name in the deduplication manifest: `hplt3`; | |
| - source file SHA-256: | |
| `b4ede7d4548fc3db385666eddd796ae2ceb0d37788bbe85a13ff8abd81ebb991`; | |
| - tokenized output SHA-256: | |
| `b07ad6d48efe3e5147d875b09acf4a53ce675d078e3d868e8d778e15c8c863aa`; | |
| - upstream corpus identity SHA-256: | |
| `0ba4bf10ad09c3b8105f8ac20dd62953e8537664b8cda0ea4557a6ec275817d3`; | |
| - tokenizer vocabulary SHA-256: | |
| `3acb94ea6bca8b2a0256d9d46c545bb9c033977c71671f8bc52cb1aa6ada92bc`. | |
| Phases A, B, and C used the same tokenized production corpus with different | |
| packing and context-length policies. | |
| ## Provenance limitations | |
| The local production records do not preserve an immutable HPLT upstream | |
| revision or acquisition timestamp. | |
| The tokenizer-training manifest records 199,117 documents from the local file | |
| `dedup-200k.jsonl`, with SHA-256: | |
| `75d73d7d874525ab9c076f62ebbeb6bfd7bd0b63acac68bee2db657771855f34` | |
| That tokenizer manifest does not independently record the original dataset | |
| identifier or immutable upstream revision. | |
| These are known provenance limitations and must not be represented as fully | |
| resolved source-level provenance. | |
| ## Third-party rights | |
| Apache-2.0 applies only to rights held by the licensors in the distributed | |
| urnova artifacts. It does not grant permission to use: | |
| - copyrighted text that may be reproduced from training data; | |
| - names, logos, or trademarks appearing in source material; | |
| - personal information or content subject to privacy or publicity rights; | |
| - material restricted by website, publisher, contractual, or database terms. | |
| Users are responsible for determining whether their intended use complies | |
| with applicable law. | |
| ## Takedown and concerns | |
| For concerns relating to this model repository, open a discussion at: | |
| https://huggingface.co/mahwizzzz/urnova-95m/discussions | |
| Provide enough information to identify the affected material and explain the | |
| basis of the request. | |
| Valid requests will be investigated. Where appropriate, affected sources will | |
| be excluded from future training runs, and the model card will identify which | |
| previous model versions remain affected. | |
| HPLT publishes its own dataset notice-and-takedown procedure separately. | |