Feature Extraction
sentence-transformers
Safetensors
Transformers
mistral
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-en-icl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-en-icl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-en-icl") 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-en-icl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-en-icl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-en-icl") model = AutoModel.from_pretrained("BAAI/bge-en-icl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update modules.json
Browse files- modules.json +0 -6
modules.json
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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