DestinyOtto/astris-minilm-l6-assetops-reranker

language:

• —en license: apache-2.0 libraryname: transformers basemodel: cross-encoder/ms-marco-MiniLM-L6-v2 datasets:
• —ibm-research/AssetOpsBench tags:
• —reranker
• —cross-encoder
• —predictive-maintenance
• —industrial-ai
• —manufacturing
• —asset-operations
• —assetopsbench metrics:
• —accuracy
• —f1
• —mrr
• —ndcg pipeline_tag: text-classification ---

ASTRIS MiniLM-L6 AssetOps Reranker

This model is a fine-tuned cross-encoder reranker based on cross-encoder/ms-marco-MiniLM-L6-v2.

It was fine-tuned for industrial maintenance and asset operations ranking tasks using selected clean examples from ibm-research/AssetOpsBench.

Intended Use

This reranker scores (query, candidate) pairs for maintenance retrieval workflows. It can be used after initial embedding retrieval to rerank candidate passages, sensors, failure modes, diagnostic hypotheses, work-order actions, or maintenance knowledge chunks.

In ASTRIS, this model is intended to improve retrieval quality for industrial maintenance reasoning, predictive maintenance support, failure-mode analysis, sensor-to-fault mapping, and work-order recommendation workflows.

Training Data

Selected clean AssetOpsBench files were used:

• —failuresensoriq_standard/all.jsonl
• —failuresensoriq_standard/all_multi_answers.jsonl
• —task/failure_mapping_senarios.jsonl
• —task/phm_utterance.jsonl
• —task/rule_monitoring_scenarios.jsonl
• —asset/compressor_utterance.jsonl
• —asset/hydrolicpump_utterance.jsonl

The perturbed AssetOpsBench files were not used for training in this run.

Base Model

cross-encoder/ms-marco-MiniLM-L6-v2

Model Size

The main trained weight file is approximately 86.66 MB.

Evaluation

The repository includes corrected reranker metrics comparing the raw base model and the fine-tuned model on the same held-out AssetOpsBench test groups.

Metrics include:

• —Top-1 accuracy
• —Exact match@k
• —Precision@k
• —Recall@k
• —F1@k
• —MRR
• —nDCG@k

The corrected evaluation compares two separately loaded models:

1. 1.The raw base model: cross-encoder/ms-marco-MiniLM-L6-v2
2. 2.The fine-tuned ASTRIS reranker

This avoids accidentally evaluating the same trained model twice.

Usage

~~~python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch

model_id = "Desimulator/astris-minilm-l6-assetops-reranker"

tokenizer = AutoTokenizer.frompretrained(modelid) model = AutoModelForSequenceClassification.frompretrained(modelid)

query = "For compressor, what are the key failure modes when speed has abnormal readings?"

candidates = [ "valve fault", "cooling system fault", "compressor stall", "damaged impeller", "misalignment" ]

inputs = tokenizer( [query] * len(candidates), candidates, padding=True, truncation=True, return_tensors="pt" )

with torch.no_grad(): scores = torch.sigmoid(model(**inputs).logits.squeeze(-1))

ranked = sorted( zip(candidates, scores.tolist()), key=lambda x: x[1], reverse=True )

print(ranked) ~~~

Dataset Citation

This model was trained using selected clean examples from ibm-research/AssetOpsBench.

If you use this model or the dataset-derived training setup, please cite the original AssetOpsBench work:

~~~bibtex @misc{patel2025assetopsbenchbenchmarkingaiagents, title={AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance}, author={Dhaval Patel and Shuxin Lin and James Rayfield and Nianjun Zhou and Roman Vaculin and Natalia Martinez and Fearghal O'donncha and Jayant Kalagnanam}, year={2025}, eprint={2506.03828}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2506.03828} } ~~~

Limitations

This model was fine-tuned on a compact industrial maintenance benchmark, not a full proprietary plant history. It should be used as a reranking component, not as the only source of truth for safety-critical maintenance decisions.

The model scores relevance between a query and candidate text. It does not directly calculate RUL, inspect raw sensor time series, or replace certified maintenance judgment.