numb3r3 commited on
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391704c
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fix: add missing module

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  1. README.md +3 -35
README.md CHANGED
@@ -50,48 +50,16 @@ curl https://api.jina.ai/v1/rerank \
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  }'
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  ```
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- 2. Alternatively, you can use the latest version of the `sentence-transformers>=0.27.0` library. You can install it via pip:
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-
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- ```bash
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- pip install -U sentence-transformers
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- ```
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-
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- Then, you can use the following code to interact with the model:
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-
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- ```python
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- from sentence_transformers import CrossEncoder
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-
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- # Load the model, here we use our base sized model
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- model = CrossEncoder("jinaai/jina-reranker-v2-base-multilingual", num_labels=1, trust_remote_code=True)
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-
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-
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- # Example query and documents
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- query = "Organic skincare products for sensitive skin"
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- documents = [
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- "Eco-friendly kitchenware for modern homes",
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- "Biodegradable cleaning supplies for eco-conscious consumers",
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- "Organic cotton baby clothes for sensitive skin",
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- "Natural organic skincare range for sensitive skin",
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- "Tech gadgets for smart homes: 2024 edition",
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- "Sustainable gardening tools and compost solutions",
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- "Sensitive skin-friendly facial cleansers and toners",
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- "Organic food wraps and storage solutions",
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- "All-natural pet food for dogs with allergies",
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- "Yoga mats made from recycled materials"
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- ]
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-
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- results = model.rank(query, documents, return_documents=True, top_k=3)
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- ```
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-
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- 3. You can also use the `transformers` library to interact with the model programmatically.
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  ```python
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  !pip install transformers
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  from transformers import AutoModelForSequenceClassification
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  model = AutoModelForSequenceClassification.from_pretrained(
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- 'jinaai/jina-reranker-v2-base-multilingual', trust_remote_code=True
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  )
 
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  # Example query and documents
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  query = "Organic skincare products for sensitive skin"
 
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  }'
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  ```
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+ 2. You can also use the `transformers` library to interact with the model programmatically.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```python
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  !pip install transformers
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  from transformers import AutoModelForSequenceClassification
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  model = AutoModelForSequenceClassification.from_pretrained(
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+ 'jinaai/jina-reranker-v2-base-multilingual', trust_remote_code=True,
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  )
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+ model.to('cuda')
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  # Example query and documents
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  query = "Organic skincare products for sensitive skin"