Text Retrieval
sentence-transformers
Safetensors
English
llama_bidirec
dense-retrieval
information-retrieval
oblique-retrieval
authorship
stylometry
custom_code
Instructions to use DataScience-UIBK/OBLIQ-IR-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DataScience-UIBK/OBLIQ-IR-3B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DataScience-UIBK/OBLIQ-IR-3B", 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] - Notebooks
- Google Colab
- Kaggle
Commit History
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Update pipeline tag to text-retrieval and add paper link (#1)
- Update pipeline tag to text-retrieval and add paper link (d584f2172e8607c6f46ee75d2c8e430847929bf0) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>a9e00d9
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Fix GitHub link (org is DataScienceUIBK, no hyphen)
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Replace adapter with the merged full-weight OBLIQ-IR-3B model (EMNLP 2026)
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Correct usage: no task-instruction prefix (matches how the model was trained and evaluated)
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Add OBLIQ-IR-3B LoRA adapter (EMNLP 2026 paper-final checkpoint) + model card
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initial commit
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