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
是否可以考虑修改config.json文件
"auto_map": {
"AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
"AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
"AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
"AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
"AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
"AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
"AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
}
可以看到需要有configuration和modeling脚本,但是对于离线用户显然不是很友好(需要在联网环境加载模型拿到脚本),是否可以修改config.json文件以及仓库文件结构