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": 0.7894736842105263, | |
| "recall": 0.8571428571428571, | |
| "f1-score": 0.821917808219178, | |
| "support": 70.0 | |
| }, | |
| "1": { | |
| "precision": 0.8461538461538461, | |
| "recall": 0.7746478873239436, | |
| "f1-score": 0.8088235294117647, | |
| "support": 71.0 | |
| }, | |
| "accuracy": 0.8156028368794326, | |
| "macro avg": { | |
| "precision": 0.8178137651821862, | |
| "recall": 0.8158953722334004, | |
| "f1-score": 0.8153706688154714, | |
| "support": 141.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8180147586642548, | |
| "recall": 0.8156028368794326, | |
| "f1-score": 0.8153242352026792, | |
| "support": 141.0 | |
| } | |
| } |