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propertypilot-recommender
0tizm0/propertypilot-recommender
propertypilot-recommender is a machine learning model from 0tizm0. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Semantic ticket similarity engine for PropertyPilot — 13,798 maintenance tickets.
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172
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Repo size
131 MB
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0
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Repo makeup
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.parquet30.8 MB · 59%
At a glance
- Access
- Public
- Created
- Jun 18, 2026
- Updated
- Jun 23, 2026
- SHA
- 85d600e5
Model card
From the Hugging Face model README
PropertyPilot Recommender
Semantic ticket similarity engine for PropertyPilot — 13,798 maintenance tickets.
Winning model: BAAI/bge-small-en-v1.5
Bake-Off (200-query stratified eval set)
| Model | Precision@3 | MRR | Latency |
|---|---|---|---|
BAAI/bge-small-en-v1.5 | 0.337 | 0.453 | 9.2 ms |
sentence-transformers/all-MiniLM-L6-v2 | 0.333 | 0.488 | 7.2 ms |
intfloat/e5-small-v2 | 0.312 | 0.496 | 11.2 ms |
Files
| File | Description |
|---|---|
embeddings.parquet | 13,798 × 384 embeddings + ticket_id |
index.faiss | FAISS IndexFlatIP (cosine similarity) |
config.json | Model name, dim, query/doc prefixes |
eval_results.json | Full bake-off benchmark data |
bakeoff_chart.png | Visual comparison bar chart |
Usage
import faiss, json
import numpy as np
from sentence_transformers import SentenceTransformer
from huggingface_hub import hf_hub_download
with open(hf_hub_download("0tizm0/propertypilot-recommender", "config.json")) as f:
cfg = json.load(f)
index = faiss.read_index(hf_hub_download("0tizm0/propertypilot-recommender", "index.faiss"))
model = SentenceTransformer(cfg["model_name"])
query = "The pipe under my sink is leaking"
vec = model.encode([cfg["query_prefix"] + query], normalize_embeddings=True).astype("float32")
distances, indices = index.search(vec.reshape(1, -1), 3)
print("Top-3 similar ticket indices:", indices[0])
- Created
- Jun 18, 2026
- Updated
- Jun 23, 2026