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
| { | |
| "dataset": "ViFN", | |
| "dataset_rows": 1406, | |
| "split_counts": { | |
| "train": 1124, | |
| "validation": 141, | |
| "test": 141 | |
| }, | |
| "split_seed": 42, | |
| "source_sha256": "80ca7eab9383bb0487e64c857b81956f3abd5147aaa19888d4ca8cdff7fd3a50", | |
| "canonical_data_sha256": "dcf674944d5a4c323f1817ff33c088ff41e427b26b9892e31cae70c077f6e5f0", | |
| "base_model": "bert-base-multilingual-cased", | |
| "base_revision": "3f076fdb1ab68d5b2880cb87a0886f315b8146f8", | |
| "fine_tuning_seeds": [ | |
| 42, | |
| 22, | |
| 202 | |
| ], | |
| "representative_seed": 42, | |
| "selection_rule": "highest development Macro-F1; test metrics are not used for selection", | |
| "aggregate_metrics": { | |
| "dev_accuracy": { | |
| "mean": 0.8486997635933807, | |
| "std": 0.014763588648695997, | |
| "values": [ | |
| 0.8368794326241135, | |
| 0.8652482269503546, | |
| 0.8439716312056738 | |
| ] | |
| }, | |
| "dev_macro_precision": { | |
| "mean": 0.8529317119960395, | |
| "std": 0.013506402205727543, | |
| "values": [ | |
| 0.8403540903540904, | |
| 0.8672064777327935, | |
| 0.8512345679012345 | |
| ] | |
| }, | |
| "dev_macro_recall": { | |
| "mean": 0.8483232729711604, | |
| "std": 0.014845199869479062, | |
| "values": [ | |
| 0.8365191146881288, | |
| 0.864989939637827, | |
| 0.8434607645875252 | |
| ] | |
| }, | |
| "dev_macro_f1": { | |
| "mean": 0.8481242000514174, | |
| "std": 0.014993029393505908, | |
| "values": [ | |
| 0.8363526265327749, | |
| 0.8650037792894936, | |
| 0.8430161943319838 | |
| ] | |
| }, | |
| "dev_weighted_f1": { | |
| "mean": 0.8481886833633626, | |
| "std": 0.014975323748613392, | |
| "values": [ | |
| 0.8364184772941922, | |
| 0.8650445205663038, | |
| 0.843103052229592 | |
| ] | |
| }, | |
| "test_accuracy": { | |
| "mean": 0.8250591016548463, | |
| "std": 0.008189365520420235, | |
| "values": [ | |
| 0.8156028368794326, | |
| 0.8297872340425532, | |
| 0.8297872340425532 | |
| ] | |
| }, | |
| "test_macro_precision": { | |
| "mean": 0.829765076745669, | |
| "std": 0.010976776319693734, | |
| "values": [ | |
| 0.8178137651821862, | |
| 0.8320850202429151, | |
| 0.8393964448119058 | |
| ] | |
| }, | |
| "test_macro_recall": { | |
| "mean": 0.8254527162977867, | |
| "std": 0.008278278300672315, | |
| "values": [ | |
| 0.8158953722334004, | |
| 0.8300804828973842, | |
| 0.8303822937625754 | |
| ] | |
| }, | |
| "test_macro_f1": { | |
| "mean": 0.8245628443823872, | |
| "std": 0.007971415110036854, | |
| "values": [ | |
| 0.8153706688154714, | |
| 0.8295729250604351, | |
| 0.828744939271255 | |
| ] | |
| }, | |
| "test_weighted_f1": { | |
| "mean": 0.8245014945559426, | |
| "std": 0.007959906612465843, | |
| "values": [ | |
| 0.8153242352026792, | |
| 0.8295300632640115, | |
| 0.8286501852011371 | |
| ] | |
| } | |
| }, | |
| "training": { | |
| "epochs": 3, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "effective_batch_size": 8, | |
| "max_length": 256, | |
| "text_mode": "raw", | |
| "class_weighting": false, | |
| "resampling": false | |
| }, | |
| "environment": { | |
| "python": "3.12.13", | |
| "execution_mode": "full" | |
| }, | |
| "created_at_utc": "2026-07-18T18:01:08.422406+00:00" | |
| } |