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")# pip install -U transformers accelerate # 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
Commit History
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Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
Verified signatureHello! ## Pull Request overview * Update metadata to set pipeline tag to the new `text-ranking` * Update metadata to set library name to `sentence-transformers` ## Changes This is an automated pull request to update the metadata of the model card. We recently introduced the [`text-ranking`](https://huggingface.co/models?pipeline_tag=text-ranking) pipeline tag for models that are used for ranking tasks, and we have a suspicion that this model is one of them. I also updated added metadata to specify that this model can be loaded with the `sentence-transformers` library, as it should be possible to load any model compatible with `transformers` `AutoModelForSequenceClassification`. Feel free to verify that it works with the following: ```bash pip install sentence-transformers ``` ```python from sentence_transformers import CrossEncoder model = CrossEncoder("n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2") scores = model.predict([ ("How many people live in Berlin?", "Berlin had a popu…c174a80
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