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Instructions to use cross-encoder/ms-marco-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/ms-marco-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use cross-encoder/ms-marco-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/ms-marco-MiniLM-L6-v2") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/ms-marco-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
Issue of NaN values when running predict on CPU
#6
by himj98 - opened
When forcing the model to run on cpu on a MacBook M2, we get NaN values when running the predict function. Note that when we use the default device = 'mps' we get the required response correctly. Also this happens only for the ms-marco-miniLM-L-6-v2, note that for ms-marco-miniLM-L-12-v2 we get the right responses too.
i second that
Same here, same MacBook Air M2
same problem here. to be 100% sure it couldn't be avoidable i tried with both sentence-transformers and transformers but it doesn't work. Anyone found a fix?

