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alt.freeweights.vision / bert-base-multilingual-uncased-sentiment
From: nlptown <huggingface.co/nlptown>Newsgroups: alt.freeweights.visionSubject:

bert-base-multilingual-uncased-sentiment

Date: 2026-11-15Commit: 8f6f4e3Signed: 2DC28CC7B26A283B ✓

Text classification model with 167M parameters. Safetensors weights, 2.68 GB in all. Published by nlptown under MIT.

167M params2.68GBbert-base-multilingual-uncased-sentimentsafetensorspermissive
FactsParams167MSize2.68 GBFormatssafetensorsTasktext classificationLicenseMITFrom sourcepermissive
$ fw get nlptown/bert-base-multilingual-uncased-sentiment
# manifest sha256 1da539278942… · 2.68 GB · 10 files
$ fw verify ./bert-base-multilingual-uncased-sentiment nlptown/bert-base-multilingual-uncased-sentiment
From sourceHosted (signed)nlptown/bert-base-multilingual-uncased-sentimentLast check: 2026-11-15 02:26 UTC
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Ranked by fit to this model (safetensors), then by GitHub stars.

TransformersLocalAItinygradTriton Inference ServerBentoMLCTranslate2ExLlamaV2LitServe
OverviewModel cardFilesUse itLicenseProvenanceModel tree

Overview

Size
2.68 GB
Weights
669 MB (safetensors)
Parameters
167M
Formats
safetensors
Task
text classification
License
MIT (permissive)
Files
10, each with a sha256
Publisher
nlptown
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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BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 02:26 UTCpassed

Model card

From the publisher at commit 8f6f4e3. Shown as plain text with links; images open at the publisher.

bert-base-multilingual-uncased-sentiment#

Visit the NLP Town website for an updated version of this model, with a 40% error reduction on product reviews.

This is a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish, and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).

This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks.

Training data#

Here is the number of product reviews we used for finetuning the model:

LanguageNumber of reviews
English150k
Dutch80k
German137k
French140k
Italian72k
Spanish50k

Accuracy#

The fine-tuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:

  • Accuracy (exact) is the exact match for the number of stars.
  • Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer.
LanguageAccuracy (exact)Accuracy (off-by-1)
English67%95%
Dutch57%93%
German61%94%
French59%94%
Italian59%95%
Spanish58%95%

Contact#

In addition to this model, NLP Town offers custom models for many languages and NLP tasks.

If you found this model useful, you can buy us a coffee.

Feel free to contact us for questions, feedback and/or requests for similar models.

Files

10 files · 2.68 GBWeights 669 MB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 10 files with their sha256
FileSizesha256
.gitattributes445 B98c91dabef4df27b2fc7c8454d220aba47476f73593cbd034d961fe4927304c1
README.md2.04 KBf2719fbf85d3bda03ecd11407429e4b1aa57aace5ddfa1edea668aad50503880
config.json953 Be5387cfa72d4b8f301b7df84365195d25711212b6c3d377cace2b18c3a911a74
flax_model.msgpack669 MB7988c7c3610880f76a734e6f7d46a8a8456ad459ac4910e9fd43b0bd3dc03583
model.safetensors669 MB0435c6c12c80a0a2206137c9fa8f6478fce4f4de88ac3d43d0f0e0da3c8c767b
pytorch_model.bin669 MBe72c9084bf53cee1f722b1acc6f7439cce27e9beabddce447653a1af45405183
special_tokens_map.json112 B303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3
tf_model.h5670 MBcb7ef0d2a4879de37b6a8824b2c6e213526360d4d89b653d674ff58e220161cb
tokenizer_config.json39 Bbc33e3cc4de965153393572f1a5cd5d2bf18872f8c0a0096ae547beac01cec9c
vocab.txt872 KB87b44292b452f6c05afa49b2e488e7eedf79ea4f4c39db6f2f4b37764228ef3f
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
8f6f4e3a8f pinned now2026-11-15 02:26 UTC10 files · 2.68 GB

Use it

$ fw get nlptown/bert-base-multilingual-uncased-sentiment
# manifest sha256 1da539278942… · 2.68 GB · 10 files
$ fw verify ./bert-base-multilingual-uncased-sentiment nlptown/bert-base-multilingual-uncased-sentiment
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/nlptown/bert-base-multilingual-uncased-sentiment.json --dir ./bert-base-multilingual-uncased-sentiment $ python3 fw.py verify ./bert-base-multilingual-uncased-sentiment ./nlptown__bert-base-multilingual-uncased-sentiment.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download nlptown/bert-base-multilingual-uncased-sentiment --revision 8f6f4e3a8f70be4b65d3a4a8762b6d781cda240d --local-dir ./bert-base-multilingual-uncased-sentiment
Run at home

License

Name
MIT License
Id
MIT
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
f2719fbf85d3bda03ecd11407429e4b1aa57aace5ddfa1edea668aad50503880
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: nlptown <huggingface.co/nlptown>Newsgroups: alt.freeweights.visionSubject: bert-base-multilingual-uncased-sentimentDate: 2026-11-15Commit: 8f6f4e3Signed: 2DC28CC7B26A283B ✓
Upstream
nlptown/bert-base-multilingual-uncased-sentiment
Pinned commit
8f6f4e3a8f70be4b65d3a4a8762b6d781cda240d
Manifest
bert-base-multilingual-uncased-sentiment.json · .minisig
Manifest sha256
1da5392789420d73c9146145ae792e35b430edf082776dfb5105b590b5b196b9
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 02:26 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/nlptown/bert-base-multilingual-uncased-sentiment.json $ curl -O https://freeweights.allternit.com/manifests/nlptown/bert-base-multilingual-uncased-sentiment.json.minisig $ minisign -Vm bert-base-multilingual-uncased-sentiment.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 nlptown/bert-base-multilingual-uncased-sentiment
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. bert-base-multilingual-uncased-sentiment.json. Provenance. Takedown history: none.
Source
huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment

Variants

1 quantized or adapter build of bert-base-multilingual-uncased-sentiment. Each has its own license check and page.

SubjectSizeParamsLicenseDate
bert-base-multilingual-uncased-sentiment-ONNXHosted (signed) From: onnx-community in alt.freeweights.vision2.49 GB—Permissive2026-11-15

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