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--- |
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pipeline_tag: feature-extraction |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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language: en |
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license: apache-2.0 |
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--- |
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# ONNX Conversion of [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) |
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- ONNX model for CPU with O3 optimisation |
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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## Usage |
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```python |
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import torch |
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import torch.nn.functional as F |
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from optimum.onnxruntime import ORTModelForFeatureExtraction |
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from transformers import AutoTokenizer |
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sentences = [ |
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"The llama (/ˈlɑːmə/) (Lama glama) is a domesticated South American camelid.", |
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"The alpaca (Lama pacos) is a species of South American camelid mammal.", |
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"The vicuña (Lama vicugna) (/vɪˈkuːnjə/) is one of the two wild South American camelids.", |
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] |
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model_name = "EmbeddedLLM/paraphrase-MiniLM-L3-v2-onnx-o3-cpu" |
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device = "cpu" |
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provider = "CPUExecutionProvider" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = ORTModelForFeatureExtraction.from_pretrained( |
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model_name, use_io_binding=True, provider=provider, device_map=device |
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) |
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inputs = tokenizer( |
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sentences, |
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padding=True, |
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truncation=True, |
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return_tensors="pt", |
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max_length=model.config.max_position_embeddings, |
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) |
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inputs = inputs.to(device) |
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token_embeddings = model(**inputs).last_hidden_state |
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# Pool |
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att_mask = inputs["attention_mask"].unsqueeze(-1).expand(token_embeddings.size()).float() |
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embeddings = torch.sum(token_embeddings * att_mask, 1) / torch.clamp(att_mask.sum(1), min=1e-9) |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
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print(embeddings.cpu().numpy().shape) |
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``` |
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