nasa-impact/nasa-smd-ibm-ranker

Description:

Passage Re-Ranker a encoder based model that takes a search query, and a passage, and calculates the relevancy of the passage to the query. This is used in conjunction with sentence transformers to re-rank the passages matched by the sentence transformer, there-by improving relevance of Information Retrieval processes.

The Model is Finetuned using MS-Marco, and tested using Science QA datasets.

The Model is an integral part of Neural Search Information Retreival process used by the Science Discovery Engine, Along with the finetuned sentence transformer (https://huggingface.co/nasa-impact/nasa-smd-ibm-st-v2).

Evaluation:

Model Evaluation on msmarco dev set, and NASA Science Questions:

Intended uses & limitations

Both query and passage have to fit in 512 Tokens (along with [CLS] and [SEP] special tokens). The intended use is to rerank the first dozens of embedding search results.

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nasa-impact/nasa-smd-ibm-ranker")
model = AutoModelForSequenceClassification.from_pretrained("nasa-impact/nasa-smd-ibm-ranker")

Cite this Model

@misc {nasa-impact_2024,
    author       = { {NASA-IMPACT} },
    title        = { nasa-smd-ibm-ranker (Revision 4f42d19) },
    year         = 2024,
    url          = { https://huggingface.co/nasa-impact/nasa-smd-ibm-ranker },
    doi          = { 10.57967/hf/1849 },
    publisher    = { Hugging Face }
}