Text Classification
Transformers
TensorBoard
Safetensors
English
modernbert
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-ettin-150m-DistillRankNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-ettin-150m-DistillRankNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-ettin-150m-DistillRankNET")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-ettin-150m-DistillRankNET") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-150m-DistillRankNET", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from xpmir/cross-encoder-ettin-150m-DistillRankNET: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/xpmir/cross-encoder-ettin-150m-DistillRankNET/resolve/main/model.safetensors
- Command line
-
hf download hf://xpmir/cross-encoder-ettin-150m-DistillRankNET/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/xpmir/cross-encoder-ettin-150m-DistillRankNET/resolve/main/model.safetensors
598 MB
- Xet hash:
- 881ffa765aa38723f8a5ec21b95d6ac601d0bef0c00828eb5b6b42b86c5188cf
- Size of remote file:
- 598 MB
- SHA256:
- 1b85fbd80f496f288f0de9be524aa31cb85386554f514aed678181852255c2ec
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