Instructions to use n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2") model = AutoModelForSequenceClassification.from_pretrained("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download gitattributes from n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2: direct link, hf CLI and curl.
- Browser
- Download file 736 Bytes
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https://huggingface.co/n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2/resolve/main/gitattributes
- Command line
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hf download hf://n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2/gitattributes
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curl -L -o gitattributes https://huggingface.co/n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2/resolve/main/gitattributes
736 Bytes
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