Text Classification
Transformers
PyTorch
Romanian
bert
hate speech
offensive language
romanian
classification
nlp
Eval Results (legacy)
text-embeddings-inference
Instructions to use readerbench/ro-offense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/ro-offense with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="readerbench/ro-offense")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("readerbench/ro-offense") model = AutoModelForSequenceClassification.from_pretrained("readerbench/ro-offense") - Notebooks
- Google Colab
- Kaggle
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README.md
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- F1 Micro: 0.8232
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- F1 Weighted: 0.8210
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## Model description
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Finetuned Romanian BERT model for offensive classification.
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- F1 Micro: 0.8232
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- F1 Weighted: 0.8210
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Output labels:
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- LABEL_0 = No offensive language
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- LABEL_1 = Profanity (no directed insults)
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- LABEL_2 = Insults (directed offensive language, lower level of offensiveness)
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- LABEL_3 = Abuse (directed hate speech, racial slurs, sexist speech, threat with violence, death wishes, ..)
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## Model description
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Finetuned Romanian BERT model for offensive classification.
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