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
| { | |
| "status": "completed", | |
| "task": "ViHSD", | |
| "dataset": "ViHSD", | |
| "model_key": "cafebert", | |
| "model_name": "CafeBERT", | |
| "model_id": "uitnlp/CafeBERT", | |
| "base_revision": "af76fcf2a04096b2b54b348a3e4eb48253c93c5d", | |
| "seed": 22, | |
| "split_seed": -1, | |
| "smoke_test": false, | |
| "epochs": 3, | |
| "num_labels": 3, | |
| "max_length": 256, | |
| "micro_batch_size": 256, | |
| "gradient_accumulation_steps": 1, | |
| "effective_batch_size": 256, | |
| "per_device_eval_batch_size": 512, | |
| "bf16": true, | |
| "tf32": true, | |
| "text_mode_resolved": "raw", | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "model_class": "XLMRobertaForSequenceClassification", | |
| "model_type": "xlm-roberta", | |
| "best_checkpoint": "/content/EACL_2027_ViHSD/runs_ml256_bs256_a10080_fast3/vihsd/cafebert/seed-22/trainer_mb256_ga1/checkpoint-282", | |
| "best_metric": 0.6662261779079418, | |
| "best_model_dir": "/content/EACL_2027_ViHSD/runs_ml256_bs256_a10080_fast3/vihsd/cafebert/seed-22/best_model", | |
| "train_loss": 0.43061161041259766, | |
| "wall_seconds": 123.9551351070404, | |
| "dev_accuracy": 0.8735029940119761, | |
| "dev_macro_precision": 0.7091333850267234, | |
| "dev_macro_recall": 0.6544894683604082, | |
| "dev_macro_f1": 0.6662261779079418, | |
| "dev_weighted_f1": 0.8673236110494446, | |
| "test_accuracy": 0.8772455089820359, | |
| "test_macro_precision": 0.6877188198376923, | |
| "test_macro_recall": 0.6417308557866228, | |
| "test_macro_f1": 0.6564459039343468, | |
| "test_weighted_f1": 0.8719636962442154, | |
| "completed_at_utc": "2026-07-20T15:13:10.074390+00:00" | |
| } |