Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
Chinese
qwen2
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use infly/inf-retriever-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use infly/inf-retriever-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("infly/inf-retriever-v1", trust_remote_code=True) 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] - Transformers
How to use infly/inf-retriever-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("infly/inf-retriever-v1", trust_remote_code=True) model = AutoModel.from_pretrained("infly/inf-retriever-v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
AIR-Bench_24.05榜单评测结果需求
#6
by MMACM - opened
我是 北京理工大学 的研究人员,近期在关注 AIR-Bench_24.05 榜单时注意到贵团队提交的 inf-retriever-v1 模型取得了第一名的优异成绩,表现非常出色!
我们目前正在开展与检索增强生成(RAG)相关的研究工作,希望能将贵团队的方法作为强有力的基线进行对比分析。因此,冒昧致信,想请问是否方便分享贵方在 AIR-Bench_24.05 上的具体提交结果(例如:预测文件、评估脚本输出或相关指标细节以及提交到榜单的结果源数据)?如有公开的代码或技术报告,也十分欢迎提供。
我们承诺仅将数据用于非商业的学术研究,并会在任何相关成果中明确引用贵团队的工作。非常感谢您在百忙之中阅读此消息,期待您的回复!
EasonYao changed discussion status to closed