Instructions to use daccuong2002/PhoBert-SingleLabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daccuong2002/PhoBert-SingleLabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daccuong2002/PhoBert-SingleLabel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daccuong2002/PhoBert-SingleLabel") model = AutoModelForSequenceClassification.from_pretrained("daccuong2002/PhoBert-SingleLabel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.30619508028030396
f1_macro: 0.9144546296168614
f1_micro: 0.9395170929954578
f1_weighted: 0.9393476038598597
precision_macro: 0.933137336891457
precision_micro: 0.9395170929954578
precision_weighted: 0.9396507724668284
recall_macro: 0.8988675495622459
recall_micro: 0.9395170929954578
recall_weighted: 0.9395170929954578
accuracy: 0.9395170929954578
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Model tree for daccuong2002/PhoBert-SingleLabel
Base model
vinai/phobert-base-v2