Feature Extraction
Transformers
ONNX
English
bert
mteb
sparse
sparsity
quantized
embeddings
int8
deepsparse
Eval Results (legacy)
Instructions to use RedHatAI/bge-base-en-v1.5-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/bge-base-en-v1.5-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RedHatAI/bge-base-en-v1.5-quant")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-base-en-v1.5-quant") model = AutoModel.from_pretrained("RedHatAI/bge-base-en-v1.5-quant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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model-index:
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results:
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- quantized
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model-index:
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- name: bge-base-en-v1.5-quant
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results:
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