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README.md
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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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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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| [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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```
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```python
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from deepsparse.sentence_transformers import SentenceTransformer
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model = SentenceTransformer('zeroshot/bge-small-en-v1.5-quant', 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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'Sentences are passed as a list of string.',
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'The quick brown fox jumps over the lazy dog.']
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# Sentences are encoded by calling model.encode()
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embeddings = model.encode(sentences)
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# Print the embeddings
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for sentence, embedding in zip(sentences, embeddings):
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print("Sentence:", sentence)
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print("Embedding:", embedding.shape)
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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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