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# bge-base-en-v1.5-sparse
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| Links | Sparsification Method |
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| --------------------------------------------------------------------------------------------------- | ---------------------- |
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| [zeroshot/bge-large-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-large-en-v1.5-sparse) | Quantization (INT8) & 50% Pruning |
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| [zeroshot/bge-large-en-v1.5-quant](https://huggingface.co/zeroshot/bge-large-en-v1.5-quant) | Quantization (INT8) |
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| [zeroshot/bge-base-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-base-en-v1.5-sparse) | Quantization (INT8) & 50% Pruning |
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| [zeroshot/bge-base-en-v1.5-quant](https://huggingface.co/zeroshot/bge-base-en-v1.5-quant) | Quantization (INT8) |
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| [zeroshot/bge-small-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-small-en-v1.5-sparse) | Quantization (INT8) & 50% Pruning |
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| [zeroshot/bge-small-en-v1.5-quant](https://huggingface.co/zeroshot/bge-small-en-v1.5-quant) | Quantization (INT8) |
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```bash
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pip install -U deepsparse-nightly[sentence_transformers]
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```python
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from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
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model = DeepSparseSentenceTransformer('
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# Our sentences we like to encode
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sentences = ['This framework generates embeddings for each input sentence',
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print("")
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```
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For
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For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
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![;)](https://media.giphy.com/media/bYg33GbNbNIVzSrr84/giphy-downsized-large.gif)
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# bge-base-en-v1.5-sparse
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## Usage
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This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization/pruning and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
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```bash
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pip install -U deepsparse-nightly[sentence_transformers]
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```python
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from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
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model = DeepSparseSentenceTransformer('neuralmagic/bge-base-en-v1.5-sparse', export=False)
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# Our sentences we like to encode
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sentences = ['This framework generates embeddings for each input sentence',
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print("")
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```
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For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
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