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: 784 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": 0.9311075781664017,
"recall": 0.9500720980533526,
"f1-score": 0.9404942456954233,
"support": 5548.0
},
"OFFENSIVE": {
"precision": 0.5398550724637681,
"recall": 0.3355855855855856,
"f1-score": 0.41388888888888886,
"support": 444.0
},
"HATE": {
"precision": 0.5921938088829072,
"recall": 0.6395348837209303,
"f1-score": 0.6149545772187281,
"support": 688.0
},
"accuracy": 0.8772455089820359,
"macro avg": {
"precision": 0.6877188198376923,
"recall": 0.6417308557866228,
"f1-score": 0.6564459039343468,
"support": 6680.0
},
"weighted avg": {
"precision": 0.8701960832863099,
"recall": 0.8772455089820359,
"f1-score": 0.8719636962442154,
"support": 6680.0
}
} |