Feature Extraction
Transformers
Safetensors
PyTorch
English
gemma3_text
sentence-similarity
search
retrieval
ranking
embeddings
semantic-search
bi-encoder
rag
text-embeddings-inference
Instructions to use GorankLabs/Rank-Embed-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GorankLabs/Rank-Embed-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="GorankLabs/Rank-Embed-1B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("GorankLabs/Rank-Embed-1B") model = AutoModel.from_pretrained("GorankLabs/Rank-Embed-1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Rank-Embed-1B
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. It is designed to convert text into dense vector representations so systems can reason about semantic meaning rather than relying solely on keyword overlap.
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# Rank-Embed-1B
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Rank-Embed-1B is a specialized 1B-parameter **bi-encoder** model from GorankLabs, fine-tuned from [`google/gemma-3-1b-pt`](https://huggingface.co/google/gemma-3-1b-pt). It is designed to convert text into dense vector representations so systems can reason about semantic meaning rather than relying solely on keyword overlap.
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