Instructions to use BaoNhan/phobert-base-vifn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/phobert-base-vifn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/phobert-base-vifn")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/phobert-base-vifn") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/phobert-base-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": "vinai/phobert-base", | |
| "base_revision": "c1e37c5c86f918761049cef6fa216b4779d0d01d", | |
| "fine_tuning_seeds": [ | |
| 42, | |
| 22, | |
| 202 | |
| ], | |
| "representative_seed": 22, | |
| "selection_rule": "highest development Macro-F1; test metrics are not used for selection", | |
| "aggregate_metrics": { | |
| "dev_accuracy": { | |
| "mean": 0.8770685579196217, | |
| "std": 0.008189365520420171, | |
| "values": [ | |
| 0.8865248226950354, | |
| 0.8723404255319149, | |
| 0.8723404255319149 | |
| ] | |
| }, | |
| "dev_macro_precision": { | |
| "mean": 0.878471904660285, | |
| "std": 0.008271926112746478, | |
| "values": [ | |
| 0.8878787878787879, | |
| 0.8723340040241448, | |
| 0.875202922077922 | |
| ] | |
| }, | |
| "dev_macro_recall": { | |
| "mean": 0.8768947015425889, | |
| "std": 0.008162130821931303, | |
| "values": [ | |
| 0.886317907444668, | |
| 0.8723340040241448, | |
| 0.8720321931589538 | |
| ] | |
| }, | |
| "dev_macro_f1": { | |
| "mean": 0.8769136547163822, | |
| "std": 0.008201239636128432, | |
| "values": [ | |
| 0.8863819500402901, | |
| 0.8723340040241448, | |
| 0.8720250100847116 | |
| ] | |
| }, | |
| "dev_weighted_f1": { | |
| "mean": 0.876940339845822, | |
| "std": 0.008202534495814187, | |
| "values": [ | |
| 0.8864105245712393, | |
| 0.8723404255319149, | |
| 0.872070069434312 | |
| ] | |
| }, | |
| "test_accuracy": { | |
| "mean": 0.8605200945626477, | |
| "std": 0.004094682760210118, | |
| "values": [ | |
| 0.8652482269503546, | |
| 0.8581560283687943, | |
| 0.8581560283687943 | |
| ] | |
| }, | |
| "test_macro_precision": { | |
| "mean": 0.8635432700730278, | |
| "std": 0.00454023255846262, | |
| "values": [ | |
| 0.866969696969697, | |
| 0.8583937198067633, | |
| 0.865266393442623 | |
| ] | |
| }, | |
| "test_macro_recall": { | |
| "mean": 0.8607981220657277, | |
| "std": 0.004070822509857444, | |
| "values": [ | |
| 0.8654929577464789, | |
| 0.8582494969818913, | |
| 0.8586519114688129 | |
| ] | |
| }, | |
| "test_macro_f1": { | |
| "mean": 0.8602881146210591, | |
| "std": 0.004211351351138073, | |
| "values": [ | |
| 0.865139692927259, | |
| 0.8581488933601609, | |
| 0.8575757575757577 | |
| ] | |
| }, | |
| "test_weighted_f1": { | |
| "mean": 0.8602552002720701, | |
| "std": 0.004218391661333309, | |
| "values": [ | |
| 0.865112559421485, | |
| 0.8581417583515276, | |
| 0.8575112830431979 | |
| ] | |
| } | |
| }, | |
| "training": { | |
| "epochs": 3, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "effective_batch_size": 8, | |
| "max_length": 256, | |
| "text_mode": "segmented", | |
| "class_weighting": false, | |
| "resampling": false | |
| }, | |
| "environment": { | |
| "python": "3.12.13", | |
| "execution_mode": "full" | |
| }, | |
| "created_at_utc": "2026-07-18T17:59:52.912002+00:00" | |
| } |