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
Update config.json
Browse files- config.json +1 -1
config.json
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings":
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 4096,
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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