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") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -8927,7 +8927,7 @@ print(scores.tolist())
|
|
| 8927 |
|
| 8928 |
## Evaluation
|
| 8929 |
|
| 8930 |
-
`bge-multilingual-
|
| 8931 |
|
| 8932 |
- [**MIRACL**](https://github.com/project-miracl/miracl)
|
| 8933 |
|
|
|
|
| 8927 |
|
| 8928 |
## Evaluation
|
| 8929 |
|
| 8930 |
+
`bge-multilingual-gemma2` exhibits **state-of-the-art (SOTA) results on benchmarks like MIRACL, MTEB-pl, and MTEB-fr**. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench.
|
| 8931 |
|
| 8932 |
- [**MIRACL**](https://github.com/project-miracl/miracl)
|
| 8933 |
|