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---
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-st-v2
`nasa-smd-ibm-st-v2` is a Bi-encoder sentence transformer model, that is fine-tuned from nasa-smd-ibm-v0.1 encoder model. it is an updated version of `nasa-smd-ibm-st` with better performance (shown below). 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.
you can also use distilled version of the model here: https://huggingface.co/nasa-impact/nasa-ibm-st.38m
## Model Details
- **Base Encoder Model**: nasa-smd-ibm-v0.1
- **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
Figure: Open dataset sources for sentence transformers (269M 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 [sentence-transformers/all-MiniLM-L6-v2]
2. BGE-base [BAAI/bge-base-en-v1.5]
3. RoBERTa-base [roberta-base]
4. nasa-smd-ibm-rtvr_v0.1 [nasa-impact/nasa-smd-ibm-st]
Figure: BEIR Evaluation Metrics
Figure: Retrieval Benchmark Evaluation
## Uses
- Information Retreival
- Sentence Similarity Search
For NASA SMD related, scientific usecases.
### Usage
```python
from sentence_transformers import SentenceTransformer, Util
model = SentenceTransformer("nasa-impact/nasa-smd-ibm-st-v2")
input_queries = [
'query: how much protein should a female eat', 'query: summit define']
input_passages = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day.
But, 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.",
"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."]
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)
print(util.cos_sim(query_embeddings, passage_embeddings))
```
## Citation
If you find this work useful, please cite using the following bibtex citation:
```bibtex
@misc {nasa-impact_2024,
author = { {NASA-IMPACT} },
title = { nasa-smd-ibm-st-v2 (Revision d249d84) },
year = 2024,
url = { https://huggingface.co/nasa-impact/nasa-smd-ibm-st-v2 },
doi = { 10.57967/hf/1800 },
publisher = { Hugging Face }
}
```
## 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.
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