File size: 1,650 Bytes
0fa331b 4f42d19 0fa331b ccb2d20 4f42d19 1aa10ca ddba5e8 5538fa7 4f42d19 5538fa7 4f42d19 be5acba 4f42d19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
---
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:
## 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 }
}
``` |