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--- |
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license: mit |
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language: |
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- en |
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--- |
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# bge-micro-v2-quant |
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This is the quantized (INT8) ONNX variant of the [bge-micro-v2](https://huggingface.co./TaylorAI/bge-micro-v2) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization. |
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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-micro-v2-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 further details regarding DeepSparse & Sentence Transformers integration, refer to the [DeepSparse README](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers). |
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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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