
# hate-speech-bert (base-uncased)

Fine-tuned hate speech detection model on English language using Toraman22 v2 dataset published at https://github.com/avaapm/hatespeech

Class labels:

"0": Neutral

"1": Offensive

"2": Hate

We split 80-20 train-test randomly.

5 epochs train_loss: 0.0948 

eval_f1 is 0.9426, eval_accuracy is 0.9430
    
### BibTeX entry and citation info
```bibtex
@InProceedings{toraman2022large,
  author    = {Toraman, Cagri  and  \c{S}ahinu\c{c}, Furkan and Yilmaz, Eyup Halit},
  title     = {Large-Scale Hate Speech Detection with Cross-Domain Transfer},
  booktitle = {Proceedings of the Language Resources and Evaluation Conference},
  month     = {June},
  year      = {2022},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2215--2225},
  url       = {https://aclanthology.org/2022.lrec-1.238}
}
```
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["bert", "hate-speech", "hate-speech-detection"], "task_categories": ["text-classification"], "task_ids": ["hate-speech-detection"]}
transformers pytorch bert text-classification hate-speech hate-speech-detection en license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #hate-speech #hate-speech-detection #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# hate-speech-bert (base-uncased)

Fine-tuned hate speech detection model on English language using Toraman22 v2 dataset published at URL

Class labels:

"0": Neutral

"1": Offensive

"2": Hate

We split 80-20 train-test randomly.

5 epochs train_loss: 0.0948 

eval_f1 is 0.9426, eval_accuracy is 0.9430
    
### BibTeX entry and citation info

# hate-speech-bert (base-uncased)

Fine-tuned hate speech detection model on English language using Toraman22 v2 dataset published at URL

Class labels:

"0": Neutral

"1": Offensive

"2": Hate

We split 80-20 train-test randomly.

5 epochs train_loss: 0.0948 

eval_f1 is 0.9426, eval_accuracy is 0.9430 ### BibTeX entry and citation info
TAGS
#transformers #pytorch #bert #text-classification #hate-speech #hate-speech-detection #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # hate-speech-bert (base-uncased)

Fine-tuned hate speech detection model on English language using Toraman22 v2 dataset published at URL

Class labels:

"0": Neutral

"1": Offensive

"2": Hate

We split 80-20 train-test randomly.

5 epochs train_loss: 0.0948 

eval_f1 is 0.9426, eval_accuracy is 0.9430 ### BibTeX entry and citation info