Instructions to use cuongdk253/phobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cuongdk253/phobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cuongdk253/phobert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cuongdk253/phobert") model = AutoModelForSequenceClassification.from_pretrained("cuongdk253/phobert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf34f933f28cfbf49cdd7879cd0fc018fbca9bb61171739a94423de33bac6a5a
- Size of remote file:
- 5.78 kB
- SHA256:
- 8219b217d94a82f217a1d946357afee6b6ccea4ff7fbeab11f02b6f3bc38b09e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.