Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
vihsd
transfer
eacl-2027
hate-speech-detection
offensive-language
social-media
Instructions to use BaoNhan/cafebert-ViHSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViHSD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViHSD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViHSD") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViHSD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 785 Bytes
0cd47bb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"CLEAN": {
"precision": 0.9320148331273177,
"recall": 0.9513338139870223,
"f1-score": 0.9415752386049415,
"support": 5548.0
},
"OFFENSIVE": {
"precision": 0.49473684210526314,
"recall": 0.31756756756756754,
"f1-score": 0.3868312757201646,
"support": 444.0
},
"HATE": {
"precision": 0.6092896174863388,
"recall": 0.6482558139534884,
"f1-score": 0.6281690140845071,
"support": 688.0
},
"accuracy": 0.8779940119760479,
"macro avg": {
"precision": 0.6786804309063066,
"recall": 0.6390523985026927,
"f1-score": 0.6521918428032044,
"support": 6680.0
},
"weighted avg": {
"precision": 0.8697114833706134,
"recall": 0.8779940119760479,
"f1-score": 0.8724240706422319,
"support": 6680.0
}
} |