Instructions to use daccuong2002/PhoBert-MultiLabel-KMeans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daccuong2002/PhoBert-MultiLabel-KMeans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daccuong2002/PhoBert-MultiLabel-KMeans")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daccuong2002/PhoBert-MultiLabel-KMeans") model = AutoModelForSequenceClassification.from_pretrained("daccuong2002/PhoBert-MultiLabel-KMeans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.3279833495616913
f1_macro: 0.876649943334319
f1_micro: 0.9077217308152044
f1_weighted: 0.9087694078887676
precision_macro: 0.8488349060784007
precision_micro: 0.9077217308152044
precision_weighted: 0.9121960695781595
recall_macro: 0.9108354827796887
recall_micro: 0.9077217308152044
recall_weighted: 0.9077217308152044
accuracy: 0.9077217308152044
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Model tree for daccuong2002/PhoBert-MultiLabel-KMeans
Base model
vinai/phobert-base-v2