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alt.freeweights.vision / biomedical-ner-all
From: d4data <huggingface.co/d4data>Newsgroups: alt.freeweights.visionSubject:

biomedical-ner-all

Date: 2026-11-15Commit: 015a405Signed: 2DC28CC7B26A283B ✓

Token classification model with 66M parameters. Safetensors weights, 532 MB in all. Published by d4data under Apache-2.0.

66M params532MBbiomedical-ner-allsafetensorspermissive
FactsParams66MSize532 MBFormatssafetensorsTasktoken classificationLicenseApache-2.0From sourcepermissive
$ fw get d4data/biomedical-ner-all
# manifest sha256 a14bcf638964… · 532 MB · 9 files
$ fw verify ./biomedical-ner-all d4data/biomedical-ner-all
From sourceHosted (signed)d4data/biomedical-ner-allLast check: 2026-11-15 02:30 UTC
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Get help: custom fine-tune or private deploy. We review your setup before we quote.

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Runs with

Ranked by fit to this model (safetensors), then by GitHub stars.

TransformersLocalAItinygradTriton Inference ServerBentoMLCTranslate2ExLlamaV2LitServe
OverviewModel cardFilesUse itLicenseProvenanceModel tree

Overview

Size
532 MB
Weights
266 MB (safetensors)
Parameters
66M
Formats
safetensors
Task
token classification
License
Apache-2.0 (permissive)
Files
9, each with a sha256
Publisher
d4data
Added
2026-11-15
Section
alt.freeweights.vision
Lineage

Original release from the publisher.

Where these bytes come from
Publisher at the pinned commit→You

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Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 02:30 UTCpassed

Model card

From the publisher at commit 015a405. Shown as plain text with links; images open at the publisher.

About the Model#

An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased

Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18

Usage#

The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")

Author#

This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:

https://github.com/dreji18/Bio-Epidemiology-NER

You can support me here :)#

Buy Me A Coffee

Files

9 files · 532 MBWeights 266 MB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 9 files with their sha256
FileSizesha256
.gitattributes1.23 KB400b270bc8f5cb44fda65b1a4532ceca0fb452d0637425e861829560cf393ae2
README.md3.06 KBfb70f13f418fe0d2728982ba050df3daa47f595f19f6858221b64200a2120265
config.json5.00 KB8441374a5e33099b00da344bd19eb6ed67e697bd54c0720d0e97e714981e20ed
model.safetensors266 MBd744b846a71ce6ccdb49d7bfe5097eadc41e766ffd28481e1636ed796e820165
pytorch_model.bin266 MBb027673a3307002bc2c34795e627691e1a0b906ee3480036fb9a5b06d269f547
special_tokens_map.json125 Bb6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3
tokenizer.json711 KB435667fab0c06c165b1283ecb422497c37124f2d6a35b2ac73dc876332fc9518
tokenizer_config.json373 B7070b097c5c26dd42e3810db48bf58a9ed1785440cf3b9cf5cded238b7e6382e
vocab.txt232 KB07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
015a4050c9 pinned now2026-11-15 02:30 UTC9 files · 532 MB

Use it

$ fw get d4data/biomedical-ner-all
# manifest sha256 a14bcf638964… · 532 MB · 9 files
$ fw verify ./biomedical-ner-all d4data/biomedical-ner-all
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/d4data/biomedical-ner-all.json --dir ./biomedical-ner-all $ python3 fw.py verify ./biomedical-ner-all ./d4data__biomedical-ner-all.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download d4data/biomedical-ner-all --revision 015a4050c9ac99722e61c547aa9b4282bcbedc7f --local-dir ./biomedical-ner-all
Run at home

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
fb70f13f418fe0d2728982ba050df3daa47f595f19f6858221b64200a2120265
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: d4data <huggingface.co/d4data>Newsgroups: alt.freeweights.visionSubject: biomedical-ner-allDate: 2026-11-15Commit: 015a405Signed: 2DC28CC7B26A283B ✓
Upstream
d4data/biomedical-ner-all
Pinned commit
015a4050c9ac99722e61c547aa9b4282bcbedc7f
Manifest
biomedical-ner-all.json · .minisig
Manifest sha256
a14bcf638964b122e51e71134e4a6d30e61d505587b841f2a0a38e1fe221b8a2
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 02:30 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/d4data/biomedical-ner-all.json $ curl -O https://freeweights.allternit.com/manifests/d4data/biomedical-ner-all.json.minisig $ minisign -Vm biomedical-ner-all.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

1 model in the catalog name this one as their parent.

Quantizations
1
Quantizations 1
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download d4data/biomedical-ner-all
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. biomedical-ner-all.json. Provenance. Takedown history: none.
Source
huggingface.co/d4data/biomedical-ner-all

Variants

1 quantized or adapter build of biomedical-ner-all. Each has its own license check and page.

SubjectSizeParamsLicenseDate
biomedical-ner-all-ONNXHosted (signed) From: onnx-community in alt.freeweights.vision915 MB—Permissive2026-11-15

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