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
| { | |
| "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 | |
| } |