Feature Extraction
Transformers
Safetensors
sentence-transformers
qwen2
text-generation
mteb
text-embeddings-inference
Instructions to use alvarobartt/jina-code-embeddings-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alvarobartt/jina-code-embeddings-1.5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="alvarobartt/jina-code-embeddings-1.5b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alvarobartt/jina-code-embeddings-1.5b") model = AutoModelForCausalLM.from_pretrained("alvarobartt/jina-code-embeddings-1.5b", device_map="auto") - sentence-transformers
How to use alvarobartt/jina-code-embeddings-1.5b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("alvarobartt/jina-code-embeddings-1.5b") 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
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library_name: transformers
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inference: false
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library_name: transformers
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pipeline_tag: feature-extraction
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