Text Classification
Transformers
TensorBoard
Safetensors
English
bert
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-bert-base-DistillRankNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-bert-base-DistillRankNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-bert-base-DistillRankNET")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-bert-base-DistillRankNET") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-DistillRankNET", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from xpmir/cross-encoder-bert-base-DistillRankNET: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/xpmir/cross-encoder-bert-base-DistillRankNET/resolve/main/model.safetensors
- Command line
-
hf download hf://xpmir/cross-encoder-bert-base-DistillRankNET/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/xpmir/cross-encoder-bert-base-DistillRankNET/resolve/main/model.safetensors
438 MB
- Xet hash:
- 90ad1ecf1658f64a0ac613088fd956a054ac04ee5485f160de5edf0f853a65b7
- Size of remote file:
- 438 MB
- SHA256:
- 4ac6eb59c423061bb6f3f30ad0bf3d293aa1e6b63660355d58877f6c6da04a55
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