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language:
- en
license: mit
pipeline_tag: sentence-similarity
library_name: sentence-transformers
base_model: Supabase/gte-small
tags:
- sentence-transformers
- embeddings
- semantic-search
- compact
- serverless
- neurosense
---
# Neurosense Compact GTE R1
Compact embedding model for semantic retrieval, designed to stay under 200MB while improving retrieval quality over the previous Neurosense checkpoint.
## Model Summary
- Base model: `Supabase/gte-small`
- Embedding dimension: `384`
- Max sequence length: `512`
- Checkpoint size (local): ~`128MB`
- Intended use: dense retrieval / semantic search
## Benchmark (Retrieval-Only, No Generation)
Evaluated on fixed 150-case benchmark (50 queries each from `mteb/fiqa`, `mteb/nfcorpus`, `mteb/scifact`) using cosine similarity.
### Aggregate Results
- `hit@1`: `0.6600`
- `hit@5`: `0.8133`
- `mrr@10`: `0.7311`
- `recall@10`: `0.6212`
- `map@10`: `0.5859`
### Comparison vs previous HF Neurosense
Previous Neurosense baseline (`models/Neurosense`):
- `mrr@10`: `0.7242`
This compact checkpoint:
- `mrr@10`: `0.7311`
Delta:
- `+0.0069` MRR@10 on the same benchmark.
## Usage
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Sharjeelbaig/Neurosense-Compact-GTE-R1")
embeddings = model.encode([
"what causes daytime sleepiness",
"Sleep apnea can cause fragmented sleep and daytime fatigue"
], normalize_embeddings=True)
```
## Notes
- This model is compact and practical for serverless constraints.
- It does not aim to replace very large embedding models globally; it is optimized for compact deployment with strong retrieval quality.
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