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": 22, | |
| "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_22/trainer_mb8_ga1/checkpoint-423", | |
| "best_metric": 0.8363526265327749, | |
| "best_model_dir": "/content/drive/MyDrive/EACL_2027_ViFN_Benchmark/runs/mbert/full/seed_22/best_model", | |
| "train_loss": 0.348644316337351, | |
| "wall_seconds": 100.6977710723877, | |
| "dev_accuracy": 0.8368794326241135, | |
| "dev_macro_precision": 0.8403540903540904, | |
| "dev_macro_recall": 0.8365191146881288, | |
| "dev_macro_f1": 0.8363526265327749, | |
| "dev_weighted_f1": 0.8364184772941922, | |
| "test_accuracy": 0.8156028368794326, | |
| "test_macro_precision": 0.8178137651821862, | |
| "test_macro_recall": 0.8158953722334004, | |
| "test_macro_f1": 0.8153706688154714, | |
| "test_weighted_f1": 0.8153242352026792, | |
| "completed_at_utc": "2026-07-18T16:09:02.936719+00:00" | |
| } |