Remove SecureBERT from benchmark rankings (#15)

- Remove SecureBERT from benchmark rankings (279a525d191066e434ddaeb42796c72c22cacbd9)

README.md

@@ -44,8 +44,7 @@ AP (average precision) measures ranking across score cutoffs. For the other metr
 
 | Model | AP | Precision at 0.5 | Recall at 0.5 | F1 at 0.5 | TP | FP | FN | Pair order |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [SecureBERT2.0](https://huggingface.co/cisco-ai/SecureBERT2.0-code-vuln-detection)† | 43.6% | 43.5% | **99.8%** | **60.6%** | **1140** | 1479 | **2** | **50.2%** |
-| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B)† | 44.0% | 43.6% | 64.9% | 52.1% | 741 | 959 | 401 | 41.5% |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B)† | 44.0% | 43.6% | **64.9%** | **52.1%** | **741** | 959 | **401** | **41.5%** |
 | [JevK5](https://huggingface.co/alibiserikbay/JevK5) | 44.8% | 46.4% | 12.4% | 19.6% | 142 | 164 | 1000 | 30.6% |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **49.2%** | **54.4%** | 10.2% | 17.2% | 117 | 98 | 1025 | 40.5% |
 | [jev-lite](https://huggingface.co/vagmi/jev-lite) | 43.3% | 36.0% | 3.2% | 5.8% | 36 | **64** | 1106 | 22.9% |
@@ -54,15 +53,12 @@ AP (average precision) measures ranking across score cutoffs. For the other metr
 
 | Model | AP | Precision at 0.5 | Recall at 0.5 | F1 at 0.5 | TP | FP | FN | Pair order |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [SecureBERT2.0](https://huggingface.co/cisco-ai/SecureBERT2.0-code-vuln-detection)† | 43.0% | 45.4% | **99.7%** | **62.4%** | **397** | 478 | **1** | **55.8%** |
-| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B)† | 47.1% | 46.9% | 67.8% | 55.4% | 270 | 306 | 128 | 52.8% |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B)† | 47.1% | 46.9% | **67.8%** | **55.4%** | **270** | 306 | **128** | **52.8%** |
 | [JevK5](https://huggingface.co/alibiserikbay/JevK5) | 47.4% | 45.0% | 4.5% | 8.2% | 18 | 22 | 380 | 37.9% |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **48.9%** | **52.6%** | 2.5% | 4.8% | 10 | **9** | 388 | 36.2% |
 | [jev-lite](https://huggingface.co/vagmi/jev-lite) | 47.3% | 43.8% | 1.8% | 3.4% | 7 | **9** | 391 | 29.4% |
 
-† CLM-v0.1-8B uses its choice head with a 6,144-token encoder; SecureBERT2.0 uses its detection head with a 4,096-token input cap. All models are scored on the same method sides and labels within each track.
-
-At 0.5, SecureBERT2.0 flags nearly every security method. Its high F1 therefore does not indicate useful alerting precision.
+† CLM-v0.1-8B uses its choice head with a 6,144-token encoder. All models are scored on the same method sides and labels within each track.
 
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