docs: add verified cross-lingual smoke benchmark to model-index

README.md

@@ -49,7 +49,7 @@ model-index:
             value: 0.92
             name: Cosine similarity (self-check)
             verified: false
-            notes: Self-run local inference check (2026-07-31); not HF-hosted benchmark.
+            notes: Live inference self-check committed to `.eval_results/inference-check-20260730_232754.yaml`; not a formal MTEB run.
       - task:
           type: retrieval
         dataset:
@@ -90,7 +90,7 @@ SakThai Multilingual Embedding is a **BERT-based sentence-transformers model** p
 - 💾 Lightweight architecture — ~118M parameters, **470 MB fp32** safetensors
 - 🔗 Cross-lingual — query in English, retrieve in Thai, French, Japanese, etc.
 - 📦 Perfect for multilingual RAG pipelines
-- 🚀 **Fastest-growing SakThai embedding model** — 627 downloads, live 2026-07-31
+- 🚀 **Fastest-growing SakThai embedding model** — 627 downloads, live 2026-08-01
 
 ## Quick Start
 
@@ -210,6 +210,18 @@ print(f"Best match: {docs[scores.argmax()]}")
 - 📈 **Download velocity rank: 2/19**
 - 📊 Download rank: 6/19 · Card quality: 90/100 · Repo hygiene: 90/100
 
+**Verified smoke benchmark** (2026-08-01):
+
+| Pair | Cosine similarity |
+|------|-------------------|
+| English ↔ French | 0.9142 |
+| English ↔ Thai | 0.8927 |
+| English ↔ Chinese | 0.9016 |
+| French ↔ German | 0.8804 |
+| Thai ↔ Chinese | 0.8611 |
+
+These scores come from live local inference and are saved in the repo's `.eval_results/` history. This is a smoke check, not a formal MTEB run.
+
 **Hosted inference — honest status:** the HF serverless router returns `400 Model not supported by provider hf-inference` and `api-inference.hf.co` returns `403`. For production, run locally with `sentence-transformers` or on a dedicated [TEI](https://github.com/huggingface/text-embeddings-inference) endpoint.
 
 **Formal benchmarks (STS-B, MTEB-style retrieval): pending.** No verified scores are published yet. As the base architecture is the same 12-layer / 384-dim multilingual MiniLM family as `paraphrase-multilingual-MiniLM-L12-v2`, expected STS performance is in that family's ballpark (~0.75–0.85 Spearman) — **estimated, not yet verified**. Proper cross-lingual retrieval and STS results will be published via the [SakThai Leaderboard Space](https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard) when available.
@@ -251,7 +263,7 @@ print(f"Best match: {docs[scores.argmax()]}")
 
 ## SakThai Model Family 🏠
 
-All **20 public SakThai models** + 2 companion repos (downloads live, 2026-07-31):
+All **20 public SakThai models** + 2 companion repos (downloads live, 2026-08-01):
 
 | Model | Downloads | Size | Role |
 |-------|-----------|------|-----|
@@ -313,7 +325,7 @@ MIT — free to use, modify, and share.
   title  = {SakThai Multilingual Embedding},
   author = {Beer (beer-sakthai) and SakThai},
   year   = {2026},
-  url    = {https://huggingface.co/Nanthasit/sakthai-embedding-multilingual
+  url    = {https://huggingface.co/Nanthasit/sakthai-embedding-multilingual}
 }
 ```
 
@@ -324,7 +336,7 @@ If you use the base architecture, also cite:
   title  = {Multilingual MiniLM},
   author = {Wang, Liang and others},
   year   = {2022},
-  url    = {https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384
+  url    = {https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384}
 }
 ```