jupyterjazz
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update readme
Browse filesSigned-off-by: [email protected] <[email protected]>
README.md
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## Performance
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```
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Furthermore, you can use ONNX for efficient inference with `jina-embeddings-v3`:
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```python
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import onnxruntime
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import numpy as np
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from transformers import AutoTokenizer, PretrainedConfig
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# Load tokenizer and model config
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tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
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config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')
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# Tokenize input
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input_text = tokenizer('sample text', return_tensors='np')
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# ONNX session
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model_path = 'jina-embeddings-v3/onnx/model.onnx'
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session = onnxruntime.InferenceSession(model_path)
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# Prepare inputs for ONNX model
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task_type = 'text-matching'
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task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
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inputs = {
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'input_ids': input_text['input_ids'],
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'attention_mask': input_text['attention_mask'],
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'task_id': task_id
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}
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# Run model
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outputs = session.run(None, inputs)
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```
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## Performance
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