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