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nasa-smd-ibm-st

Sentence Similarity
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metadata
license: apache-2.0
language:
  - en
library_name: sentence-transformers
tags:
  - earth science
  - climate
  - biology
pipeline_tag: sentence-similarity

Model Card for nasa-smd-ibm-v0.1

nasa-smd-ibm-stv0.1 is a Bi-encoder sentence transformer model, that is adapted 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
  • Tokenizer: Custom
  • Parameters: 125M
  • Training Strategy: Sentence Pairs, and score indicating relevancy. The model encodes the two sencence pairs indepenently and cosine similarity is calculated. the similarity is optimized using the relevance score.

Training Data

image/png Figure:Open dataset sources for sentence transformers (271M in total)

Additionally, 2.6M abstract + title pairs collected from NASA SMD documents.

Training Procedure

  • Framework: PyTorch 1.9.1
  • sentence-transformers version: 4.30.2
  • Strategy: Sentence Pairs

Evaluation

Following models are evaluated:

  1. All-MiniLM-l6-v2
  2. BGE-base
  3. roberta-base
  4. A smaller version of nasa-smd-ibm_v0.1
  5. nasa-smd-ibm-rtvr_v0.1

image/png

Uses

  • Information Retreival
  • Sentence Similarity Search

For NASA SMD related, scientific usecases.

Citation

If you find this work useful, please cite using the following bibtex citation:

@misc {nasa-impact_2023,
    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},
    title        = { nasa-smd-ibm-st (Revision 08ac2b4) },
    year         = 2023,
    url          = { https://huggingface.co/nasa-impact/nasa-smd-ibm-st },
    doi          = { 10.57967/hf/1441 },
    publisher    = { Hugging Face }
}

Attribution

IBM Research

  • Masayasu Maraoka
  • Bishwaranjan Bhattacharjee
  • Aashka Trivedi

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.