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
| { | |
| "0": { | |
| "precision": 0.7804878048780488, | |
| "recall": 0.9142857142857143, | |
| "f1-score": 0.8421052631578947, | |
| "support": 70.0 | |
| }, | |
| "1": { | |
| "precision": 0.8983050847457628, | |
| "recall": 0.7464788732394366, | |
| "f1-score": 0.8153846153846154, | |
| "support": 71.0 | |
| }, | |
| "accuracy": 0.8297872340425532, | |
| "macro avg": { | |
| "precision": 0.8393964448119058, | |
| "recall": 0.8303822937625754, | |
| "f1-score": 0.828744939271255, | |
| "support": 141.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8398142365844863, | |
| "recall": 0.8297872340425532, | |
| "f1-score": 0.8286501852011371, | |
| "support": 141.0 | |
| } | |
| } |