metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: fill-mask
tags:
  - earth science
  - climate
  - biology
datasets:
  - nasa-impact/nasa-smd-IR-benchmark
  - nasa-impact/nasa-smd-qa-benchmark
  - ibm/Climate-Change-NER

Model Card for nasa-smd-ibm-v0.1 (Indus)

nasa-smd-ibm-v0.1 (Currently named as Indus) is a RoBERTa-based, Encoder-only transformer model, domain-adapted for NASA Science Mission Directorate (SMD) applications. It's fine-tuned on scientific journals and articles relevant to NASA SMD, aiming to enhance natural language technologies like information retrieval and intelligent search.

Model Details

Base Model: RoBERTa
Tokenizer: Custom
Parameters: 125M
Pretraining Strategy: Masked Language Modeling (MLM)
Distilled Version: You can download a distilled version of the model (30 Million Parameters) here: https://huggingface.co/nasa-impact/nasa-smd-ibm-distil-v0.1

Training Data

Wikipedia English (Feb 1, 2020)
AGU Publications
AMS Publications
Scientific papers from Astrophysics Data Systems (ADS)
PubMed abstracts
PubMedCentral (PMC) (commercial license subset)

Training Procedure

Framework: fairseq 0.12.1 with PyTorch 1.9.1
transformers Version: 4.2.0
Strategy: Masked Language Modeling (MLM)

Evaluation

BLURB Benchmark
Pruned SQuAD2.0 (SQ2) Benchmark (Amazon Rainforest, Oxygen, Geology and NASA ES QAs)
NASA SMD Expert QA Benchmark (WIP)

Please refer to the following dataset cards for further benchmarks and evaluation

NASA IR Benchmark - https://huggingface.co/datasets/nasa-impact/nasa-smd-IR-benchmark
NASA SMD Expert QA Benchmark - https://huggingface.co/datasets/nasa-impact/nasa-smd-qa-benchmark
Climate CHange Benchmark - https://huggingface.co/datasets/ibm/Climate-Change-NER

Uses

Named Entity Recognition (NER)
Information Retrieval
Sentence Transformers
Extractive QA

For NASA SMD related, scientific usecases.

Note

Accompanying paper can be found here: https://arxiv.org/abs/2405.10725

Citation

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

@misc {nasa-impact_2023,
    author       = {Masayasu Maraoka and Bishwaranjan Bhattacharjee and Muthukumaran Ramasubramanian and Ikhsa Gurung and Rahul Ramachandran and Manil Maskey and Kaylin Bugbee and Rong Zhang and Yousef El Kurdi and Bharath Dandala 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-v0.1 (Revision f01d42f) },
    year         = 2023,
    url          = { https://huggingface.co/nasa-impact/nasa-smd-ibm-v0.1 },
    doi          = { 10.57967/hf/1429 },
    publisher    = { Hugging Face }
}

Attribution

IBM Research

Masayasu Muraoka
Bishwaranjan Bhattacharjee
Rong Zhang
Yousef El Kurdi
Bharath Dandala

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 Encoder-only 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.