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
| 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 | |
| [Arxiv](https://arxiv.org/abs/2408.10613) | [Github](https://github.com/tdro-llm/tdro) | |
| [tDRO: Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval](https://arxiv.org/abs/2408.10613). 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](https://huggingface.co/datasets/tdro-llm/finetune_data). | |