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
- Xet hash:
- 09b4ad542fba5be09d0cb14ed521bf9d15324a3b2cacd403c3a77c22dbdd14b9
- Size of remote file:
- 90.9 MB
- SHA256:
- 3ae17b87eda3d184502a821fddff43d82feb7c206f665a851c491ec715b497ed
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