Text Classification
Transformers
TensorBoard
Safetensors
English
bert
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-MiniLM-L12-DistillRankNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-MiniLM-L12-DistillRankNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-MiniLM-L12-DistillRankNET")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-MiniLM-L12-DistillRankNET") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-MiniLM-L12-DistillRankNET") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bf3909b1f18fcfb9b421f4b841713d31a81b20bc7a76cc1d5832f0df229998d
- Size of remote file:
- 133 MB
- SHA256:
- 850818e4aae575316880f0ee8c674a55fa18dbf48552115ca447158e61d6ea64
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