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--- |
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license: apache-2.0 |
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pipeline_tag: text-classification |
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--- |
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## Description: |
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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. |
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The Model is Finetuned using MS-Marco, and tested using Science QA datasets. |
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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). |
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## Evaluation: |
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Model Evaluation on msmarco dev set, and NASA Science Questions: |
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/61099e5d86580d4580767226/jJnEkMijBvnTSN_cb_lDn.png) |
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## Intended uses & limitations |
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Both query and passagehave to fit in 512 Tokens. The intended use is to rerank the first dozens of embedding search results. |
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## How to use |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("nasa-impact/nasa-smd-ibm-ranker") |
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model = AutoModelForSequenceClassification.from_pretrained("nasa-impact/nasa-smd-ibm-ranker") |
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``` |
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## Cite this Model |
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``` |
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@misc {nasa-impact_2024, |
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author = { {NASA-IMPACT} }, |
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title = { nasa-smd-ibm-ranker (Revision 4f42d19) }, |
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year = 2024, |
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url = { https://huggingface.co/nasa-impact/nasa-smd-ibm-ranker }, |
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doi = { 10.57967/hf/1849 }, |
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publisher = { Hugging Face } |
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} |
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``` |