nasa-smd-ibm-st, Also Known as Indus-st`, is a Bi-encoder sentence transformer model, that is fine-tuned from nasa-smd-ibm-v0.1 encoder model. It's trained with 271 million examples along with a domain-specific dataset of 2.6 million examples from documents curated by NASA Science Mission Directorate (SMD). With this model, we aim to enhance natural language technologies like information retrieval and intelligent search as it applies to SMD NLP applications.
Model Details
Base Model: nasa-smd-ibm-v0.1 (Indus)
Tokenizer: Custom
Parameters: 125M
Training Strategy: Sentence Pairs, and score indicating relevancy. The model encodes the two sentence pairs independently and cosine similarity is calculated. the similarity is optimized using the relevance score.
Training Data
image/png
Figure: Open dataset sources for sentence transformers (269M in total)
Additionally, 2.6M abstract + title pairs collected from NASA SMD documents.
12from sentence_transformers import SentenceTransformer, util
3model = SentenceTransformer('path_to_slate_model')4input_queries =[5'query: how much protein should a female eat','query: summit define']6input_passages =[7"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70is46 grams per day.8But,as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",9"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]10query_embeddings = model.encode(input_queries)11passage_embeddings = model.encode(input_passages)12print(util.cos_sim(query_embeddings, passage_embeddings))
Note
This Model is released in support of the training and evaluation of the encoder language model "Indus".
If you find this work useful, please cite using the following bibtex citation:
bibtex
1@misc {nasa-impact_2023,
2 author = { Aashka Trivedi and Bishwaranjan Bhattacharjee and Muthukumaran Ramasubramanian and Iksha Gurung and Masayasu Maraoka and Rahul Ramachandran and Manil Maskey and Kaylin Bugbee and Mike Little and Elizabeth Fancher and Lauren Sanders and Sylvain Costes and Sergi Blanco-Cuaresma and Kelly Lockhart and Thomas Allen and Felix Grazes and Megan Ansdell and Alberto Accomazzi and Sanaz Vahidinia and Ryan McGranaghan and Armin Mehrabian and Tsendgar Lee},
3 title = { nasa-smd-ibm-st (Revision 08ac2b4) },
4 year = 2023,
5 url = { https://huggingface.co/nasa-impact/nasa-smd-ibm-st },
6 doi = { 10.57967/hf/1441 },
7 publisher = { Hugging Face }
8}
Attribution
IBM Research
Aashka Trivedi
Masayasu Muraoka
Bishwaranjan Bhattacharjee
NASA SMD
Muthukumaran Ramasubramanian
Iksha Gurung
Rahul Ramachandran
Manil Maskey
Kaylin Bugbee
Mike Little
Elizabeth Fancher
Lauren Sanders
Sylvain Costes
Sergi Blanco-Cuaresma
Kelly Lockhart
Thomas Allen
Felix Grazes
Megan Ansdell
Alberto Accomazzi
Sanaz Vahidinia
Ryan McGranaghan
Armin Mehrabian
Tsendgar Lee
Disclaimer
This sentence-transformer model is currently in an experimental phase. We are working to improve the model's capabilities and performance, and as we progress, we invite the community to engage with this model, provide feedback, and contribute to its evolution.
