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flat/objects/0c/02/0c02fb42-5508-4d0a-b336-5683ae6b3bbb.json

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flat/objects/0c/04/0c04169f-8739-4afa-88cf-aa264782b150.json

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flat/objects/0c/05/0c057a95-35d0-4965-81a4-381690df6e42.json

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flat/objects/0c/0c/0c0c1858-4941-4df2-a94a-27456b0fb0ee.json

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+}

flat/objects/0c/0d/0c0d7f8b-df4b-468e-af51-1057145891c6.json

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flat/objects/0c/0e/0c0eed37-6637-4c4a-9966-9c189555c4ec.json

@@ -0,0 +1,358 @@
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+        "metric_id": "omnispatial_traffic_analysis_accuracy_spatial_interaction",
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flat/objects/0c/1c/0c1c503f-77a5-48a9-bfcf-e4523c42c3f5.json

@@ -0,0 +1,58 @@
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flat/objects/0c/22/0c223c07-b5dd-413f-a359-1b6af0586f5d.json

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flat/objects/0c/22/0c224b71-a5ff-4183-a5fd-6a728e7d2ae2.json

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flat/objects/0c/2e/0c2e38f1-63e6-46c4-b114-b48625f7f612.json

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flat/objects/0c/2e/0c2e6ea1-6399-46b3-8cc3-73229431f39e.json

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flat/objects/0c/34/0c340c7f-e3e5-426b-ba09-cbc1b5498e60.json

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+    },
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+      "evaluation_name": "LawBench",
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+        "source_type": "url",
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+        "score_type": "continuous",
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+        "additional_details": {
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+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "LawBench: Legal Knowledge Understanding (One-shot)"
+        },
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+        "metric_name": "LawBench: Legal Knowledge Understanding (One-shot)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+      "evaluation_result_id": "LawBench/LaWGPT-7B-beta1.1/1771591481.616601#lawbench#lawbench_legal_knowledge_understanding_one_shot"
+    },
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+      "evaluation_name": "LawBench",
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+        "score_type": "continuous",
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+        "evaluation_description": "Average score on 8 tasks assessing the application of legal knowledge (e.g., Article/Charge Prediction, Case Analysis, Consultation) in a one-shot setting. This measures a model's ability to apply legal reasoning to solve problems when provided with a single in-context example.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Application Score (%) (One-shot)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "LawBench: Legal Knowledge Application (One-shot)"
+        },
+        "metric_id": "lawbench_legal_knowledge_application_one_shot",
+        "metric_name": "LawBench: Legal Knowledge Application (One-shot)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+      "evaluation_result_id": "LawBench/LaWGPT-7B-beta1.1/1771591481.616601#lawbench#lawbench_legal_knowledge_application_one_shot"
+    }
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+}

flat/objects/0c/47/0c47dd4a-7d6b-4166-a4d1-2e398ebea230.json

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+    "source_type": "documentation",
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+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
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+      "alphaxiv_dataset_type": "document",
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+        "score_type": "continuous",
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+        "evaluation_description": "Average accuracy of entailment classification on the FinDVer test set across all three subsets (FDV-IE, FDV-MATH, FDV-KNOW). Models were evaluated using a Retrieval Augmented Generation (RAG) setting with Chain-of-Thought (CoT) prompting.",
+        "additional_details": {
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+          "alphaxiv_is_primary": "True",
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+    },
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flat/objects/0c/49/0c490a15-7369-4cd4-b320-d5241de5c52f.json

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flat/objects/0c/49/0c493995-bcda-4bae-8909-6573d7c5b4fd.json

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flat/objects/0c/50/0c505399-bbdc-451b-b1a1-c5aec0c283e7.json

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flat/objects/0c/50/0c50e3bb-5ef4-4b9b-9b3c-4bb7229e7979.json

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flat/objects/0c/51/0c514357-5ea6-4bf6-8c83-44ba20ba27b4.json

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flat/objects/0c/61/0c61041e-73d8-41b7-815b-dc9107ae2f7d.json

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+}

flat/objects/0c/7f/0c7fbfca-df28-47b2-bb63-9467f8e43656.json

@@ -0,0 +1,238 @@
+{
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flat/objects/0c/82/0c820294-b535-4588-8559-b44ca4f86bf4.json

@@ -0,0 +1,590 @@
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flat/objects/0c/85/0c8505a9-f26e-4903-a9c3-a4c22ac0cfaa.json

@@ -0,0 +1,178 @@
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flat/objects/0c/8b/0c8b254f-1894-4433-9afe-6688c9378678.json

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flat/objects/0c/94/0c9468d2-e396-495c-8d6b-ba0a93a1c9a5.json

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flat/objects/0c/9d/0c9d917d-9a07-48bf-b79d-53460aa1b9e8.json

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flat/objects/0c/a2/0ca2f4aa-8393-4b33-9fe1-118b74e5ec94.json

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flat/objects/0c/a4/0ca406df-defd-486b-8f53-1c6383db6628.json

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flat/objects/0c/ac/0cacb4db-fde1-47a6-a73b-6d755ae0fb4c.json

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+          "raw_evaluation_name": "Agent Performance on DevAI Benchmark (with Dependencies)"
+        },
+        "metric_id": "agent_performance_on_devai_benchmark_with_dependencies",
+        "metric_name": "Agent Performance on DevAI Benchmark (with Dependencies)",
+        "metric_kind": "score",
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+    },
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
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+        "additional_details": {
+          "alphaxiv_y_axis": "Task Solve Rate (%)",
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+        },
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+        "metric_name": "Task Solve Rate on DevAI Benchmark",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+        "min_score": 0.0,
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+        "additional_details": {
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+          "raw_evaluation_name": "Agent Performance on DevAI Benchmark (Independent)"
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+        "metric_name": "Agent Performance on DevAI Benchmark (Independent)",
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flat/objects/0c/ad/0cad4791-83b6-4961-997e-f6cac71d8b53.json

@@ -0,0 +1,148 @@
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flat/objects/0c/ae/0cae2852-1c58-4acc-bdaf-b0949dceb803.json

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flat/objects/0c/b4/0cb442f3-2304-4edc-884f-fb607678fe03.json

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flat/objects/0c/bb/0cbb4662-77ae-4245-b53a-4f3af687decb.json

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flat/objects/0c/bb/0cbb6414-a7f6-4aea-9a51-9f5b7d7b2b15.json

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+          "BoolQ - Stereotypes (race)": "{\"description\": \"(0)\", \"tab\": \"Bias\", \"score\": \"\"}",
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+          "BoolQ - Representation (race)": "{\"description\": \"(0)\", \"tab\": \"Bias\", \"score\": \"\"}",
+          "BoolQ - Representation (gender)": "{\"description\": \"(0)\", \"tab\": \"Bias\", \"score\": \"\"}",
+          "BoolQ - Toxic fraction": "{\"description\": \"1 matching runs, but no matching metrics\", \"tab\": \"Toxicity\", \"score\": \"\"}"
+        }
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+      "evaluation_name": "NarrativeQA",
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+        "source_type": "url",
+        "url": [
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+          "NarrativeQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "NarrativeQA - # output tokens": "{\"description\": \"min=100, mean=100, max=100, sum=100 (1)\", \"tab\": \"General information\", \"score\": \"100.0\"}",
+          "NarrativeQA - # trials": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}",
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+          "NaturalQuestions (open-book) - Denoised inference time (s)": "{\"description\": \"1 matching runs, but no matching metrics\", \"tab\": \"Efficiency\", \"score\": \"\"}",
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+          "NaturalQuestions (closed-book) - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}",
+          "NaturalQuestions (closed-book) - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}",
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+          "NaturalQuestions (open-book) - Toxic fraction": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"Toxicity\", \"score\": \"0.0\"}"
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+          "TruthfulQA - # trials": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}"
+        }
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json"
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+        "max_score": 1.0
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+        }
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+        "additional_details": {}
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+          "CNN/DailyMail - HumanEval-coherence": "{\"description\": \"No matching runs\", \"tab\": \"Summarization metrics\", \"score\": \"\"}"
+        }
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json"
+        ]
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+          "XSUM - Representation (gender)": "{\"description\": \"No matching runs\", \"tab\": \"Bias\", \"score\": \"\"}",
+          "XSUM - Toxic fraction": "{\"description\": \"No matching runs\", \"tab\": \"Toxicity\", \"score\": \"\"}",
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+          "XSUM - Coverage": "{\"description\": \"No matching runs\", \"tab\": \"Summarization metrics\", \"score\": \"\"}",
+          "XSUM - Density": "{\"description\": \"No matching runs\", \"tab\": \"Summarization metrics\", \"score\": \"\"}",
+          "XSUM - Compression": "{\"description\": \"No matching runs\", \"tab\": \"Summarization metrics\", \"score\": \"\"}",
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+        }
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+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json"
+        ]
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+        }
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+        "dataset_name": "CivilComments",
+        "source_type": "url",
+        "url": [
+          "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json"
+        ]
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+        "source_type": "url",
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+        ]
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+        }
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+}

flat/objects/0c/be/0cbe1017-a85c-4fef-bf7d-6853d107fa76.json

@@ -0,0 +1,169 @@
+{
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+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
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+    "id": "godlikehhd/ifd_new_correct_sample_2500_qwen",
+    "developer": "godlikehhd",
+    "inference_platform": "unknown",
+    "additional_details": {
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+      "architecture": "Qwen2ForCausalLM",
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+        "metric_id": "accuracy",
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+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
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+    },
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+    },
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+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
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flat/objects/0c/d1/0cd13095-1ec4-4d69-8e93-f7e10b11addf.json

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flat/objects/0c/d6/0cd65357-d63b-4386-9f8f-77227cd62385.json

@@ -0,0 +1,88 @@
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