Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use wnic00/hihu2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnic00/hihu2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wnic00/hihu2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wnic00/hihu2") model = AutoModelForSequenceClassification.from_pretrained("wnic00/hihu2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52cb93595b4b2f8244ca97771a4b5e52d4246daa9df35433a9eb9037e56b4b0d
- Size of remote file:
- 7.12 kB
- SHA256:
- ccf55f2040cbf1fe3f47b8407bf0cee97a6346d1a5340ed4319092832ffbe1dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.