---
license: apache-2.0
pipeline_tag: text-classification
---


## 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:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61099e5d86580d4580767226/jJnEkMijBvnTSN_cb_lDn.png)


## Intended uses & limitations

Both query and passagehave to fit in 512 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 }
}
```