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: 1,120 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 | {
"test_macro_f1_text": "0.6430 ± 0.0197",
"test_macro_f1_mean": 0.6430199124399391,
"test_macro_f1_std": 0.019685624532663235,
"test_accuracy_text": "0.8739 ± 0.0064",
"test_accuracy_mean": 0.8739021956087824,
"test_accuracy_std": 0.006449878283872728,
"test_macro_precision_text": "0.6718 ± 0.0203",
"test_macro_precision_mean": 0.6717973464330855,
"test_macro_precision_std": 0.02025978809418166,
"test_macro_recall_text": "0.6312 ± 0.0160",
"test_macro_recall_mean": 0.6311801103764685,
"test_macro_recall_std": 0.01601092304857379,
"dev_macro_f1_text": "0.6530 ± 0.0118",
"dev_macro_f1_mean": 0.6530095360792209,
"dev_macro_f1_std": 0.011759357387290862,
"task": "ViHSD",
"dataset": "ViHSD",
"model_key": "cafebert",
"model_name": "CafeBERT",
"base_model": "uitnlp/CafeBERT",
"seeds": [
22,
42,
202
],
"representative_seed": 22,
"selection_rule": "maximum development Macro-F1 when available; otherwise test Macro-F1 or smallest available seed",
"split_protocol": "official-published-splits",
"max_length": 256,
"epochs": 3,
"batch_size": 8
} |