pii-ner-nemotron safetensors download | Hugging Bay

pii-ner-nemotron

scanpatch/pii-ner-nemotron

Model details

token classificationLicense: apache-2.0SHA-256 recorded for 6 of 9 filessafetensors · BIN

Content controls: not assessed.

The catalog lists this model for labeling parts of text. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

View as JSON →

Your next step

Use it from code

This Python snippet downloads the selected file. Running the model needs its own library and setup.

from urllib.request import urlretrieve

urlretrieve(
    "https://huggingbay.xyz/api/downloads/hf-model-scanpatch-pii-ner-nemotron/model.safetensors",
    "model.safetensors",
)

Other ways

This file cannot open in the app. The app supports one GGUF file at a time.

See app requirements

5 hosted of 9 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors2.24 GB · 1 file Download · 2.24 GB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

Compare your file’s SHA-256 with the recorded hash.

Model facts

pii-ner-nemotron is an open-weights language model for token classification. The primary file is model.safetensors (2.24 GB). The primary file needs about 4 GB free for the selected file (file plus room to run it). License: Apache 2.0. The SHA-256 prefix is 5c20b6ccba7d. The primary Download is available now. No tested run commands for this file yet. See Files for current download availability. The size and hash details refer to the selected file, not every file in the repository. The memory figure is estimated from recorded file size with working room included.

What's included

5 hosted of 9 listed

Weights

Tokenizer & config

README, LICENSE & .gitattributes

About this model

External Hugging Face catalog record for scanpatch/pii-ner-nemotron. Task: token classification. Library: transformers. Languages: en, ru, uk. Base model: xlm-roberta-large. Includes 1 model-card evaluation signal. Popularity signal at import: 84 downloads and 1 likes.

Quick facts

Hosted here: 5 hosted of 9 listed
Full repo upstream: 2.25 GB
Recommended file: 2.24 GB · model.safetensors
Languages: en, ru, uk
Format: safetensors · BIN
Downloads on Hugging Face: 159
File hashes: Hashes recorded. Date not recorded.

License & source

License: apache-2.0

5 hosted of 9 listed

Check the license text and keep the required notices when you use or share the files.

Upstream source →

See the JSON record →

License: Apache 2.0. Free to use and share, keep the credit notice
Task: token classification
Hosted here: at least 5 listed files; full count and size unknown
Full repo upstream: 2.25 GB
Recommended file: 2.24 GB · model.safetensors
Files: 5 hosted of 9 listed
Download: Download here (5 hosted files)
Added to Hugging Bay: Jun 30, 2026
Updated at source: Sep 24, 2026
Source: scanpatch/pii-ner-nemotron

FAQ

What is this model?

scanpatch/pii-ner-nemotron is an open-weights language model for token classification published by scanpatch, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for token classification: scanpatch/pii-ner-nemotron · transformers

Can I use it commercially?

It is released under the apache-2.0 license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

How big is it?

Hosted here: at least 5 listed files; full count and size unknown. Full repo upstream: 2.25 GB. The recommended download, model.safetensors, is 2.24 GB.

What can I download here?

Hosted here: at least 5 listed files; full count and size unknown. Full repo upstream: 2.25 GB. The original release is at https://huggingface.co/scanpatch/pii-ner-nemotron.

File checks and computer fit

File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 5 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-scanpatch-pii-ner-nemotron/safety on Hugging Bay.

Will scanpatch/pii-ner-nemotron run on my computer?

We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.