Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
cross-encoder
rerank
text-embeddings-inference
Instructions to use namdp-ptit/ViRanker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namdp-ptit/ViRanker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namdp-ptit/ViRanker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namdp-ptit/ViRanker") model = AutoModelForSequenceClassification.from_pretrained("namdp-ptit/ViRanker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83182779330bf02f2e8bedc27659fcb1bf597a905d3060f0bbdbeb663b289b43
- Size of remote file:
- 17.1 MB
- SHA256:
- b74659c780d49afad7a7b9799868f75cbd3014fb6c34956e85a793028d38094a
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