Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use BAAI/bge-base-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/bge-base-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/bge-base-en")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-base-en") model = AutoModel.from_pretrained("BAAI/bge-base-en") - Inference
- Notebooks
- Google Colab
- Kaggle
update
Browse files- modules.json +6 -0
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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}
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]
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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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"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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}
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]
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