Instructions to use tdro-llm/s2-tdro-Qwen1.5-1.8B-curr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tdro-llm/s2-tdro-Qwen1.5-1.8B-curr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tdro-llm/s2-tdro-Qwen1.5-1.8B-curr") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
metadata
datasets:
- tdro-llm/finetune_data
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
base_model:
- Qwen/Qwen1.5-1.8B
s2-tdro-Qwen1.5-1.8B-curr
tDRO: Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval. Guangyuan Ma, Yongliang Ma, Xing Wu, Zhenpeng Su, Ming Zhou and Songlin Hu.
This is a fine-tuned tDRO optimized retriever with Sample Ratio Reweighting of tdro-llm/finetune_data.