Instructions to use Quym5124050/vihealthvn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Quym5124050/vihealthvn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Quym5124050/vihealthvn")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Quym5124050/vihealthvn") model = AutoModelForSequenceClassification.from_pretrained("Quym5124050/vihealthvn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c4e6a6ee8977c543f1f51c3f7a71b95bc3896858f64f8504435bed80c186cb4
- Size of remote file:
- 5.84 kB
- SHA256:
- 50eb621ea7d84e98d8f3e6dbd28a87b9c1ecf61dd1b4f670fd74d562830f9457
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.