Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use thanhcong2001/Multiple_Labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thanhcong2001/Multiple_Labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thanhcong2001/Multiple_Labels")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thanhcong2001/Multiple_Labels") model = AutoModelForSequenceClassification.from_pretrained("thanhcong2001/Multiple_Labels") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 522569c5e65ab211d2d0e6894c777b419f56652eac3bf9674e11cf793199956e
- Size of remote file:
- 268 MB
- SHA256:
- 15ded923ce310614dda13e92c68c8ba61d3d45e66e8450ac3c1e98f6fa228cfe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.