Text Classification
Transformers
TensorBoard
Safetensors
English
electra
cross-encoder
sequence-classification
Instructions to use xpmir/cross-encoder-ELECTRA-DistillRankNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-ELECTRA-DistillRankNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-ELECTRA-DistillRankNET")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-ELECTRA-DistillRankNET") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ELECTRA-DistillRankNET") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1af403d05028b3861dbf8763e33e1b4cd1ed5a7ab5c833d2f8e6158e554fafc
- Size of remote file:
- 438 MB
- SHA256:
- 73552b734ea4b627f3a25381c2d93b8f6f1a642c4f1df12b9f042adfedbe14d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.