alt.freeweights.vision / distilbert-multilingual-toxicity-classifier-ONNX
From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.visionSubject:

distilbert-multilingual-toxicity-classifier-ONNX

Date: 2026-11-15Commit: 4fbacceSigned: 2DC28CC7B26A283B ✓

Text classification model. Onnx weights, 2.23 GB in all. Published by onnx-community under OpenRAIL++-M (custom).

vision2.23GBdistilbert-multilingual-toxicity-classifier-ONNXonnxcustom terms
FactsParamsnot statedSize2.23 GBFormatsonnxTasktext classificationLicenseOpenRAIL++-M (custom)From sourcecustom termsread terms
# custom terms: read the license before you download
$ fw get onnx-community/distilbert-multilingual-toxicity-classifier-ONNX
# manifest sha256 6e6f4a1513c4… · 2.23 GB · 16 files
$ fw verify ./distilbert-multilingual-toxicity-classifier-ONNX onnx-community/distilbert-multilingual-toxicity-classifier-ONNX
From sourceHosted (signed)onnx-community/distilbert-multilingual-toxicity-classifier-ONNXLast check: 2026-11-15 00:25 UTC

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Quantized of distilbert-multilingual-toxicity-classifier.

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Overview

Size
2.23 GB
Weights
136 MB (smallest of 8 ONNX variants)
Parameters
not stated
Formats
onnx
Task
text classification
License
OpenRAIL++-M (custom) (custom)
Files
16, each with a sha256
Publisher
onnx-community
Added
2026-11-15
Section
alt.freeweights.vision
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Original release from the publisher.

Base model: gravitee-io/distilbert-multilingual-toxicity-classifier

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From sourcehuggingface.co2026-11-15 00:25 UTCpassed

Model card

From the publisher at commit 4fbacce. Shown as plain text with links; images open at the publisher.

distilbert-multilingual-toxicity-classifier (ONNX)#

This is an ONNX version of gravitee-io/distilbert-multilingual-toxicity-classifier. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js#

See the pipeline documentation for text-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline


distillbert-multilingual-toxicity-classifier#

This is a DistilBERT-based multilingual toxicity classifier fine-tuned using the gravitee-io/textdetox-multilingual-toxicity-dataset. The model supports a wide range of languages and is trained for toxicity classification ("not-toxic", "toxic").

We perform an 85/15 train-test split per language based on the textdetox dataset. All credits go to the authors of the original corpora.

Performance Overview#

Original model#
LanguageValidation F1Training F1ΔF1
Russian0.95720.9897-0.0324
English0.95280.9853-0.0325
Hindi0.92480.9599-0.0351
Armenian0.65130.6915-0.0402
French0.94460.9874-0.0428
Tatar0.92000.9682-0.0482
Ukrainian0.89970.9511-0.0514
Japanese0.86580.9253-0.0595
German0.89040.9547-0.0643
Spanish0.85640.9399-0.0835
Chinese0.68650.7807-0.0942
Arabic0.75630.8550-0.0987
Italian0.82230.9271-0.1048
Hinglish0.72340.8533-0.1299
Hebrew0.64550.8441-0.1987
Quantized model (ONNX)#
LanguageVal F1Quantized Val F1Δ Val F1Train F1Quantized Train F1Δ Train F1
Russian0.95720.9609+0.00370.98970.9875−0.0022
English0.95280.9495−0.00330.98530.9857+0.0004
German0.89040.8842−0.00620.95470.9369−0.0178
Hindi0.92480.8940−0.03000.95990.9335−0.0264
French0.94460.9351−0.00950.98740.9814−0.0060
Japanese0.86580.8584−0.00740.92530.9081−0.0172
Ukrainian0.89970.8988−0.00090.95110.9476−0.0035
Tatar0.92000.9148−0.00520.96820.9631−0.0051
Amharic0.65130.6377−0.01360.69150.6863−0.0052
Spanish0.85640.8439−0.01250.93990.9273−0.0126
Chinese0.68650.6697−0.01680.78070.7596−0.0211
Arabic0.75630.7535−0.00280.85500.8481−0.0069
Italian0.82230.8033−0.01900.92710.9193−0.0078
Hinglish0.72340.7260+0.00260.85330.8436−0.0097
Hebrew0.64550.6190−0.02650.84410.8204−0.0237

The quantized model maintains high accuracy and generalization on the evaluation set, with negligible performance loss for most languages.

🤗 Usage#

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
import numpy as np

# Load model and tokenizer using optimum
model = ORTModelForSequenceClassification.from_pretrained(
 "gravitee-io/distilbert-multilingual-toxicity-classifier",
 file_name="model.quant.onnx"
)
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/distilbert-multilingual-toxicity-classifier")

# Tokenize input
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Run inference
outputs = model(**inputs)
logits = outputs.logits

# Optional: convert to probabilities
probs = 1 / (1 + np.exp(-logits))
print(probs)

Github Repository#

You can check details on how the model was fine-tuned and evaluated on the Github Repository

License#

This model is licensed under OpenRAIL++

Citation#

@inproceedings{dementieva2024overview,
  title={Overview of the Multilingual Text Detoxification Task at PAN 2024},
  author={Dementieva, Daryna and Moskovskiy, Daniil and Babakov, Nikolay and Ayele, Abinew Ali and Rizwan, Naquee and Schneider, Frolian and Wang, Xintog and Yimam, Seid Muhie and Ustalov, Dmitry and Stakovskii, Elisei and Smirnova, Alisa and Elnagar, Ashraf and Mukherjee, Animesh and Panchenko, Alexander},
  booktitle={Working Notes of CLEF 2024 - Conference and Labs of the Evaluation Forum},
  editor={Guglielmo Faggioli and Nicola Ferro and Petra Galu{{s}}{{c}}{'a}kov{'a} and Alba Garc{'i}a Seco de Herrera},
  year={2024},
  organization={CEUR-WS.org}
}

@inproceedings{dementieva-etal-2024-toxicity,
  title = "Toxicity Classification in {U}krainian",
  author = "Dementieva, Daryna and Khylenko, Valeriia and Babakov, Nikolay and Groh, Georg",
  booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
  month = jun,
  year = "2024",
  address = "Mexico City, Mexico",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2024.woah-1.19/",
  doi = "10.18653/v1/2024.woah-1.19",
  pages = "244--255"
}

@inproceedings{DBLP:conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24,
  author = {Janek Bevendorff and et al.},
  title = {Overview of {PAN} 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative {AI} Authorship Verification - Extended Abstract},
  booktitle = {ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part {VI}},
  series = {Lecture Notes in Computer Science},
  volume = {14613},
  pages = {3--10},
  publisher = {Springer},
  year = {2024},
  doi = {10.1007/978-3-031-56072-9_1}
}

Files

16 files · 2.23 GBWeights 136 MB (smallest of 8 ONNX variants). Sizes and sha256 from the signed manifest.
Hide all 16 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
README.md7.17 KB6da7a4f7b7836578b41d6cf439e57d27ea8ff3afe20ba6592ac463cbe9bc394c
config.json829 Bb3b5b837f833d5b71cd9a89e54921c4e79ee93baf9ae971372faecfce151c80c
onnx/model.onnx541 MB392b7fd77dabad6a7398b364321e7a60fdefee2ac82f0f8944219d5c68ea38bd
onnx/model_bnb4.onnx395 MB853f1f16d6f799dbe898793f8bf8124b778bcc1b9dab0c69e13425ff91028e45
onnx/model_fp16.onnx271 MBa60f08212406af28ca7bbe1fd5e34bd8103106712e342db85e17f45baa5627d3
onnx/model_int8.onnx136 MB52e703cb3d8a3aac8c5d35a350dbc20fd5f0a54b00f01b4b88fc5bb2052f7700
onnx/model_q4.onnx398 MB33b82a1cc1303310ba9a5fd5eefbff0b4c72030e5376761d195621fe087b4edf
onnx/model_q4f16.onnx210 MBd5049bd2d61b4f7c7e28b3421dd8dc3ff5d0801e22d0c72f174300a79da7898e
onnx/model_quantized.onnx136 MB52e703cb3d8a3aac8c5d35a350dbc20fd5f0a54b00f01b4b88fc5bb2052f7700
onnx/model_uint8.onnx136 MB18f3577d9d56a064f1e0736a2b27b417b9572f0408e877a6e79a91d1e08277b2
quantize_config.json310 B9b60b5877b9a1687b5cddbc06124a974cb7536af9dbf5295279bf73c3823170c
special_tokens_map.json695 B5d5b662e421ea9fac075174bb0688ee0d9431699900b90662acd44b2a350503a
tokenizer.json2.92 MBc2c0e4d392f08c2c2eb950156eb26e4e5ddd15b557faa45ed6a0decaf45b5f98
tokenizer_config.json1.42 KB2707fd36aeb61e24db454421a597629d5663dd66e81c48a4f5421f0ae53ee7bf
vocab.txt996 KBfe0fda7c425b48c516fc8f160d594c8022a0808447475c1a7c6d6479763f310c
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
4fbaccee8c pinned now2026-11-15 00:25 UTC16 files · 2.23 GB

Use it

# custom terms: read the license before you download
$ fw get onnx-community/distilbert-multilingual-toxicity-classifier-ONNX
# manifest sha256 6e6f4a1513c4… · 2.23 GB · 16 files
$ fw verify ./distilbert-multilingual-toxicity-classifier-ONNX onnx-community/distilbert-multilingual-toxicity-classifier-ONNX
Terms that come with these files. OpenRAIL++-M: the use restrictions in Attachment A pass on to you and to anyone you share the model with. Keep a copy of the license with the files.
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$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/distilbert-multilingual-toxicity-classifier-ONNX --revision 4fbaccee8caaba02641b1757f7ef697e3fbffdb8 --local-dir ./distilbert-multilingual-toxicity-classifier-ONNX

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From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.visionSubject: distilbert-multilingual-toxicity-classifier-ONNXDate: 2026-11-15Commit: 4fbacceSigned: 2DC28CC7B26A283B ✓
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onnx-community/distilbert-multilingual-toxicity-classifier-ONNX
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distilbert-multilingual-toxicity-classifier-ONNX.json · .minisig
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6e6f4a1513c419c40dab81e0a870c951951831b4addd9a0f1222b5566b08719e
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Created
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gravitee-io/distilbert-multilingual-toxicity-classifier · this model is a quantization of it

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From: dima806 <huggingface.co/dima806>Subject: ai_vs_real_image_detection
From: legekka <huggingface.co/legekka>Subject: AI-Anime-Image-Detector-ViT
From: onnx-community <huggingface.co/onnx-community>Subject: AI-Anime-Image-Detector-ViT-ONNX
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