Text Classification
Transformers
TensorBoard
Safetensors
English
deberta-v2
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-DeBERTav3-DistillRankNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-DeBERTav3-DistillRankNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-DeBERTav3-DistillRankNET")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-DeBERTav3-DistillRankNET") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-DeBERTav3-DistillRankNET") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e7ada1fddd5a08a23d81b2c71719458b47f3ccf10252574222c90ea4112b127
- Size of remote file:
- 738 MB
- SHA256:
- bb85859d9cd57c6a81f8bb8f737dfd17c598f6072606e27b9d4de7e40d6948ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.