Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
bert
fill-mask
sentence-similarity
mteb
custom_code
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use jinaai/jina-embeddings-v2-base-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jinaai/jina-embeddings-v2-base-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v2-base-code", trust_remote_code=True) 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] - Transformers
How to use jinaai/jina-embeddings-v2-base-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v2-base-code", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-code", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("jinaai/jina-embeddings-v2-base-code", trust_remote_code=True) - Transformers.js
How to use jinaai/jina-embeddings-v2-base-code with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'jinaai/jina-embeddings-v2-base-code'); - Notebooks
- Google Colab
- Kaggle
Update README.md (#8)
Browse files- Update README.md (55e5a5e8f73a773d71584222991e349f843e64ea)
Co-authored-by: Denis Sushentsev <Sushentsev@users.noreply.huggingface.co>
README.md
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The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
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This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search.
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This model has
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Additionally, we provide the following embedding models:
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- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
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The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
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This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search.
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This model has 161 million parameters, which enables fast and memory efficient inference, while delivering impressive performance.
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Additionally, we provide the following embedding models:
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- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
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