Feature Extraction
sentence-transformers
Safetensors
Transformers
gemma2
sentence-similarity
mteb
Eval Results (legacy)
Instructions to use BAAI/bge-multilingual-gemma2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-multilingual-gemma2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-multilingual-gemma2") 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] - Transformers
How to use BAAI/bge-multilingual-gemma2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-multilingual-gemma2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-multilingual-gemma2") model = AutoModel.from_pretrained("BAAI/bge-multilingual-gemma2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from BAAI/bge-multilingual-gemma2: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/BAAI/bge-multilingual-gemma2/resolve/main/tokenizer.json
- Command line
-
hf download hf://BAAI/bge-multilingual-gemma2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BAAI/bge-multilingual-gemma2/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- 9a6d31863c7a07a16ff1811c6a38a6979cf84e76bb0144bba71c6f2e4fb8a49b
- Size of remote file:
- 17.5 MB
- SHA256:
- 2eaeb76aefb50cfe531bc6e56f81debaf81ccbf1ec1e7fcbc667f360338b74b6
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