README.md

metadata

base_model: tarudesu/ViHateT5-base
tags:
  - generated_from_trainer
model-index:
  - name: ViHateT5-base-HSD
    results: []
datasets:
  - tarudesu/ViCTSD
  - tarudesu/ViHOS
  - tarudesu/ViHSD
language:
  - vi
metrics:
  - f1
  - accuracy
pipeline_tag: text2text-generation
widget:
  - text: 'toxic-speech-detection: Nhìn bà không thể không nhớ đến các phim phù thủy'
  - text: 'hate-speech-detection: thằng đó trông đần vcl ấy nhỉ'
  - text: 'hate-spans-detection: trông như cl'

ViHateT5: Enhancing Hate Speech Detection in Vietnamese with A Unified Text-to-Text Transformer Model | ACL'2024 (Findings)

Disclaimer: This paper contains examples from actual content on social media platforms that could be considered toxic and offensive.

ViHateT5-HSD is the fine-tuned model of ViHateT5 on multiple Vietnamese hate speech detection benchmark datasets.

The architecture and experimental results of ViHateT5 can be found in the paper:

@inproceedings{thanh-nguyen-2024-vihatet5,
    title = "{V}i{H}ate{T}5: Enhancing Hate Speech Detection in {V}ietnamese With a Unified Text-to-Text Transformer Model",
    author = "Thanh Nguyen, Luan",
    editor = "Ku, Lun-Wei  and Martins, Andre  and Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.355",
    pages = "5948--5961"
    }

The pre-training dataset named VOZ-HSD is available at HERE.

Kindly CITE our paper if you use ViHateT5-HSD to generate published results or integrate it into other software.

Example usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("tarudesu/ViHateT5-base-HSD")
model = AutoModelForSeq2SeqLM.from_pretrained("tarudesu/ViHateT5-base-HSD")

def generate_output(input_text, prefix):
    # Add prefix
    prefixed_input_text = prefix + ': ' + input_text

    # Tokenize input text
    input_ids = tokenizer.encode(prefixed_input_text, return_tensors="pt")

    # Generate output
    output_ids = model.generate(input_ids, max_length=256)

    # Decode the generated output
    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

    return output_text

sample = 'Tôi ghét bạn vl luôn!'
prefix = 'hate-spans-detection' # Choose 1 from 3 prefixes ['hate-speech-detection', 'toxic-speech-detection', 'hate-spans-detection']

result = generate_output(sample, prefix)
print('Result: ', result)

Please feel free to contact us by email [email protected] if you have any further information!