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 flax_model.msgpack from n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/n2vec/cross-encoder_ms-marco-MiniLM-L-6-v2/resolve/main/flax_model.msgpack
90.9 MB
- Xet hash:
- 84694f228a6b0e9cea7869afafac020f8fbdd5fb6b6f1112e402edcadba39173
- Size of remote file:
- 90.9 MB
- SHA256:
- bc6033077e458cf98490d51883382806373325919e7b5dd12f4dd694dcd667cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.