Feature Extraction
Transformers
Safetensors
sentence-transformers
qwen2
text-generation
mteb
🇪🇺 Region: EU
Instructions to use jinaai/jina-code-embeddings-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-code-embeddings-0.5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-code-embeddings-0.5b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-code-embeddings-0.5b") model = AutoModelForCausalLM.from_pretrained("jinaai/jina-code-embeddings-0.5b") - sentence-transformers
How to use jinaai/jina-code-embeddings-0.5b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-code-embeddings-0.5b") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Create modules.json
Browse files- modules.json +20 -0
modules.json
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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