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gravitee-io/bert-tiny-toxicity

sourceHugging Faceopenrail++updated 5mo agoView on Hugging Face
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Model Card

bert-tiny-toxicity

This is a 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

While the model performance differs from gravitee-io/distilbert-multilingual-toxicity-classifier Some languages still make the cut, even with the base model being lightweight and trained on English as per the model card.

Original model

Languageeval f1train f1Δ f1
en0.9421050.975587-0.033482
fr0.8767830.943089-0.066306
de0.8727740.919155-0.046381
hi0.8451780.885335-0.040157
it0.8055560.857527-0.051971
es0.7841190.856389-0.072270
ja0.7455920.758249-0.012657
uk0.6890950.686985+0.002110
hin0.6881720.806429-0.118257
ru0.6883720.724231-0.035858
am0.6488160.691555-0.042739
tt0.6446080.695892-0.051284
ar0.6444710.670118-0.025647
zh0.6403710.660996-0.020625
he0.5148510.524138-0.009286

Quantized model (ONNX)

Languageeval f1train f1Δ F1
en0.9422570.974907-0.032650
fr0.8767830.942214-0.065431
de0.8726360.918535-0.045900
hi0.8429120.884449-0.041538
it0.8065740.858737-0.052163
es0.7826090.856392-0.073784
ja0.7503170.756441-0.006124
hin0.6970510.806604-0.109553
ru0.6932080.722626-0.029418
uk0.6890950.684864+0.004232
am0.6473630.689944-0.042581
ar0.6444710.669856-0.025386
tt0.6420660.695060-0.052993
zh0.6404620.661274-0.020811
he0.5074630.521815-0.014352

🤗 Usage

python
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/bert-tiny-toxicity",
 file_name="model.quant.onnx"
)
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/bert-tiny-toxicity")
# 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

bibtex
@misc{bhargava2021generalization,
      title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, 
      author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
      year={2021},
      eprint={2110.01518},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{DBLP:journals/corr/abs-1908-08962,
  author    = {Iulia Turc and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {Well-Read Students Learn Better: The Impact of Student Initialization
               on Knowledge Distillation},
  journal   = {CoRR},
  volume    = {abs/1908.08962},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.08962},
  eprinttype = {arXiv},
  eprint    = {1908.08962},
  timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
bibtex
@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}
}