Instructions to use BaoNhan/mbert-vifn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/mbert-vifn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/mbert-vifn")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/mbert-vifn") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/mbert-vifn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "status": "completed", | |
| "model_key": "mbert", | |
| "model_name": "mBERT", | |
| "model_id": "bert-base-multilingual-cased", | |
| "base_revision": "3f076fdb1ab68d5b2880cb87a0886f315b8146f8", | |
| "seed": 202, | |
| "split_seed": 42, | |
| "smoke_test": false, | |
| "epochs": 3, | |
| "max_length": 256, | |
| "micro_batch_size": 8, | |
| "gradient_accumulation_steps": 1, | |
| "effective_batch_size": 8, | |
| "per_device_eval_batch_size": 8, | |
| "text_mode_resolved": "raw", | |
| "tokenizer_class": "BertTokenizer", | |
| "model_class": "BertForSequenceClassification", | |
| "model_type": "bert", | |
| "best_checkpoint": "/content/drive/MyDrive/EACL_2027_ViFN_Benchmark/runs/mbert/full/seed_202/trainer_mb8_ga1/checkpoint-423", | |
| "best_metric": 0.8430161943319838, | |
| "best_model_dir": "/content/drive/MyDrive/EACL_2027_ViFN_Benchmark/runs/mbert/full/seed_202/best_model", | |
| "train_loss": 0.3795614107280758, | |
| "wall_seconds": 101.607901096344, | |
| "dev_accuracy": 0.8439716312056738, | |
| "dev_macro_precision": 0.8512345679012345, | |
| "dev_macro_recall": 0.8434607645875252, | |
| "dev_macro_f1": 0.8430161943319838, | |
| "dev_weighted_f1": 0.843103052229592, | |
| "test_accuracy": 0.8297872340425532, | |
| "test_macro_precision": 0.8393964448119058, | |
| "test_macro_recall": 0.8303822937625754, | |
| "test_macro_f1": 0.828744939271255, | |
| "test_weighted_f1": 0.8286501852011371, | |
| "completed_at_utc": "2026-07-18T16:11:04.259760+00:00" | |
| } |