pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
widget:
- source_sentence: what components are vulnerable to fatigue crack?
sentences:
- >-
One of the first-stage compressor blades had fractued due to fatigue
cracking.
- >-
Witnesses and the fire department personnel noted fuel leaking due to a
cracked fuel line.
- >-
During periods of low visibility and night conditions, the supporting
sensors sometimes conflict.
example_title: Fatigue Crack Query
Manager for Intelligent Knowledge Access (MIKA)
Custom Information Retrieval Model
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The model is custom trained on engineering documents for asymmetric infromation retrieval. It is intended to be used to identify engineering documents relevant to a query for use in design time. For example, a repository can be queried to find support for requirements or learn more about a specific type of failure.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
This model was evaluated on three queries using precision at k for k=10,20, and 30. Mean average precision (MAP) was also calculated. The model was baselines against the pre-trained SBERT.
| IR Method | MAP |
|---|---|
| Pre-trained sBERT | 0.648 |
| Fine-tuned sBERT | 0.807 |
Training
The model was trained with the parameters:
DataLoader:
sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 693 with parameters:
{'batch_size': 32}
Loss:
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
Parameters of the fit()-Method:
{
"epochs": 2,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
Citing & Authors
Walsh, HS, & Andrade, SR. "Semantic Search With Sentence-BERT for Design Information Retrieval." Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 42nd Computers and Information in Engineering Conference (CIE). St. Louis, Missouri, USA. August 14–17, 2022. V002T02A066. ASME. https://doi.org/10.1115/DETC2022-89557