Add accuracy tables for five families missing them

From each model card: all-MiniLM-L6-v2 (STS-B), MobileViT-Small
(ImageNet), paraphrase-multilingual-MiniLM-L12-v2 (multilingual STS),
SAM3 (SA-Co/Gold), Chronos-2 (WQL ratio, lower-is-better flag). Also
point RF-DETR accuracy_url at the renamed repo.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

config.json

@@ -328,7 +328,9 @@
         "agx_orin": "Values from the model card's Performance table: FP16 baseline (resized to 924) 763 ms / 1.31 qps vs Embedl-optimized 462 ms / 2.17 qps, batch 1.",
         "agx_thor": "Values from the published model-card benchmark card: 'bf16-torch' baseline vs Embedl-optimized 'fp8-fp16-trt', batch 1. Latency derived as 1000/FPS.",
         "l4": "Values from the published model-card benchmark card: 'bf16-torch' baseline vs Embedl-optimized 'fp16-int8-trt', batch 1. Latency derived as 1000/FPS."
-      }
+      },
+      "accuracy_file": "data/acc-SAM3.csv",
+      "accuracy_title": "SA-Co/Gold"
     },
     "SAM-3D-Body": {
       "data_file": "data/SAM-3D-Body.csv",
@@ -381,7 +383,10 @@
       "default_device": "agx_orin",
       "experiment_setup": {
         "agx_orin": "Values from the published model-card benchmark card: 'TensorRT FP16' baseline vs Embedl-optimized 'embedl int8', batch 1. Latency derived as 1000/FPS."
-      }
+      },
+      "accuracy_file": "data/acc-Chronos-2.csv",
+      "accuracy_title": "Forecast WQL / Seasonal-Naive, ctx=2048 (lower is better)",
+      "accuracy_higher_is_better": false
     },
     "MobileViT-Small": {
       "data_file": "data/MobileViT-Small.csv",
@@ -393,7 +398,9 @@
       "default_device": "agx_orin",
       "experiment_setup": {
         "agx_orin": "Values from the published model-card benchmark card: 'trtexec --fp16' baseline vs Embedl-optimized 'embedl int8', batch 1. Latency derived as 1000/FPS."
-      }
+      },
+      "accuracy_file": "data/acc-MobileViT-Small.csv",
+      "accuracy_title": "ImageNet"
     },
     "all-MiniLM-L6-v2": {
       "data_file": "data/all-MiniLM-L6-v2.csv",
@@ -405,7 +412,9 @@
       "default_device": "agx_orin",
       "experiment_setup": {
         "agx_orin": "Values from the published model-card benchmark card: 'trtexec --fp16' baseline vs Embedl-optimized 'embedl int8', batch 1. Latency derived as 1000/FPS."
-      }
+      },
+      "accuracy_file": "data/acc-all-MiniLM-L6-v2.csv",
+      "accuracy_title": "STS-B (Spearman ρ)"
     },
     "paraphrase-multilingual-MiniLM-L12-v2": {
       "data_file": "data/paraphrase-multilingual-MiniLM-L12-v2.csv",
@@ -417,11 +426,13 @@
       "default_device": "agx_orin",
       "experiment_setup": {
         "agx_orin": "Values from the published model-card benchmark card: 'trtexec --fp16' baseline vs Embedl-optimized 'embedl int8', batch 1. Latency derived as 1000/FPS."
-      }
+      },
+      "accuracy_file": "data/acc-paraphrase-multilingual-MiniLM-L12-v2.csv",
+      "accuracy_title": "Multilingual STS (Spearman ρ)"
     },
     "RF-DETR-Base": {
       "data_file": "data/RF-DETR-Base.csv",
-      "accuracy_url": "https://huggingface.co/embedl/rf-detr-base",
+      "accuracy_url": "https://huggingface.co/embedl/rf-detr-base-quantized-tensorrt",
       "chart": {
         "default_metric": "ips",
         "scenarios": []

data/acc-Chronos-2.csv

@@ -0,0 +1,3 @@
+Model,Geomean WQL / Seasonal-Naive
+amazon/chronos-2,0.549
+embedl/chronos-2-quantized-trt,0.618

data/acc-MobileViT-Small.csv

@@ -0,0 +1,3 @@
+Model,Top-1,Top-5
+apple/mobilevit-small,78.14,94.08
+embedl/mobilevit-small-quantized,74.83,92.28

data/acc-SAM3.csv

@@ -0,0 +1,3 @@
+Model,cgF1,IL_MCC,pos_µF1
+facebook/sam3,55.56,0.823,67.45
+embedl/sam3,53.77,0.809,66.36

data/acc-all-MiniLM-L6-v2.csv

@@ -0,0 +1,3 @@
+Model,Spearman ρ
+sentence-transformers/all-MiniLM-L6-v2,0.8672
+embedl/all-MiniLM-L6-v2-quantized-trt,0.8646

data/acc-paraphrase-multilingual-MiniLM-L12-v2.csv

@@ -0,0 +1,3 @@
+Model,Spearman ρ
+sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,0.8130
+embedl/paraphrase-multilingual-MiniLM-L12-v2-quantized-trt,0.8008