Instructions to use Jarbas/m2v-256-bge-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Jarbas/m2v-256-bge-reranker-base with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Jarbas/m2v-256-bge-reranker-base") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use Jarbas/m2v-256-bge-reranker-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jarbas/m2v-256-bge-reranker-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Jarbas/m2v-256-bge-reranker-base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Jarbas/m2v-256-bge-reranker-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://Jarbas/m2v-256-bge-reranker-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Jarbas/m2v-256-bge-reranker-base/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 8aeccd00191001d18d56567746e6865082a39451f1ad2157f95c0f4f3afe17dd
- Size of remote file:
- 17.1 MB
- SHA256:
- 9bce0521948cd86eb7fd4ce0b16c06615ac4f505595cdcb8be7eeb20df5ffbdb
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