Text Ranking
sentence-transformers
Safetensors
Transformers
new
text-classification
text-embeddings-inference
custom_code
Instructions to use Alibaba-NLP/gte-multilingual-reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-multilingual-reranker-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Alibaba-NLP/gte-multilingual-reranker-base", trust_remote_code=True) query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use Alibaba-NLP/gte-multilingual-reranker-base with Transformers:
# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Alibaba-NLP/gte-multilingual-reranker-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
如何输出归一化的分数
#8
by haozhi - opened
我们看到阿里云版本似乎给了参考分数,都是在0-1之间的
https://help.aliyun.com/zh/model-studio/developer-reference/text-rerank-quick-start?spm=a2c4g.11186623.0.0.8fbe3d47ArelGw
但我们实际测试hf的模型输出分数是非归一化的
想请教下如何才能输出和阿里云上一样的分数用于做置信度参考
我自己试的最小值-3.3377,最大值3.30左右,同样请教一下如何归一化分数
可以在输出分数后添加一个sigmoid函数将分数进行归一化