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flat/objects/0c/00/0c00d874-f4fd-49b1-9770-d02a4b91f86b.json

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flat/objects/0c/01/0c013cf2-4516-4f13-8e9b-1c6d1edfb07d.json

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flat/objects/0c/06/0c06bd38-c22d-436e-b8d5-24494f44e53d.json

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flat/objects/0c/08/0c089573-56c6-470d-b6b2-e746ab474d65.json

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flat/objects/0c/09/0c096bff-bdc2-44ec-b4fd-53d855f61131.json

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flat/objects/0c/0f/0c0f446a-6c2a-47c7-a120-4fafb90f92a3.json

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flat/objects/0c/0f/0c0f6ed6-b994-42fa-91ab-2eec03f10b79.json

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flat/objects/0c/12/0c124fc7-9cbc-4173-97db-58ab6acb18e9.json

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flat/objects/0c/12/0c12cb55-bae8-40c5-a70f-ceddf3b8a3a8.json

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flat/objects/0c/15/0c15299f-9a14-4371-bed6-6f7c627cb548.json

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flat/objects/0c/17/0c170ef5-6abd-485d-b035-67120faed345.json

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flat/objects/0c/18/0c184f61-014c-4fcc-a565-86590a21bc54.json

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flat/objects/0c/20/0c20c9e6-0f64-4ad2-b79a-47365e5f5e73.json

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+        "evaluation_description": "This metric evaluates a model's ability to solve math word problems by generating executable Python code. Performance is measured by execution accuracy on the SVAMP dataset using a 4-shot prompting setup.",
+        "additional_details": {
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+          "alphaxiv_is_primary": "False",
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+        "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,
+        "evaluation_description": "This metric evaluates a model's ability to generate SQL queries from natural language questions over semi-structured web tables. The evaluation is performed on the development split of the WikiTableQuestions (WikiTQ) dataset using a 2-shot prompting setup.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Execution Accuracy (%) - WikiTQ (2-shot)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Execution Accuracy on the WikiTQ Dataset (2-shot)"
+        },
+        "metric_id": "execution_accuracy_on_the_wikitq_dataset_2_shot",
+        "metric_name": "Execution Accuracy on the WikiTQ Dataset (2-shot)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+    },
+    {
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+        "score_type": "continuous",
+        "min_score": 0.0,
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flat/objects/0c/20/0c20ff50-30ca-45a2-ae7f-259e41d5fcec.json

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flat/objects/0c/21/0c2122ac-89c3-4651-b69a-601966d9a840.json

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flat/objects/0c/23/0c238b58-a197-4168-beaa-ae4615c3be74.json

@@ -0,0 +1,58 @@
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flat/objects/0c/23/0c2397df-92ba-4d90-8b22-b562eab7d0cf.json

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flat/objects/0c/29/0c295dcc-231b-4322-b64e-3dd1dc8b3a72.json

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flat/objects/0c/29/0c298e43-cd5b-4255-9718-90a7d79dc310.json

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flat/objects/0c/2a/0c2ab210-6539-432d-8aca-1043a4bf85e7.json

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flat/objects/0c/2a/0c2afd8d-6912-44d6-839f-8cb403a0e64f.json

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flat/objects/0c/2f/0c2f630b-ac50-48cf-8128-ede06b3c2603.json

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+          "raw_evaluation_name": "ROVER-TG: Overall Alignment of Visual Reasoning and Final Answer"
+        },
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flat/objects/0c/2f/0c2fa491-541a-44d9-bd3e-8bb990110313.json

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flat/objects/0c/39/0c39b9c1-1912-4c6b-befb-c8d19d72ce97.json

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flat/objects/0c/43/0c43c2c4-6df9-4c34-b3c3-8f508aa67fa1.json

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flat/objects/0c/4a/0c4ad1da-7369-41e2-a332-6c0b34a0d670.json

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flat/objects/0c/4c/0c4c2eef-1ab4-462e-b510-6be3b967f347.json

@@ -0,0 +1,328 @@
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flat/objects/0c/4c/0c4c741c-8012-4dfc-8200-041ee6c158a7.json

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flat/objects/0c/4d/0c4ddacb-72ee-4c91-8c0f-d0be1577ffd6.json

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flat/objects/0c/6e/0c6e3b28-fe41-4df3-b68b-ffe19b8e4864.json

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flat/objects/0c/74/0c740a8f-cd90-4203-840c-13e0f0853acc.json

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flat/objects/0c/74/0c74ebdd-3927-4943-87e5-a75a2d1c1bbb.json

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flat/objects/0c/7b/0c7bbed9-d684-4762-8b33-b3b1d9c05ffd.json

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flat/objects/0c/7c/0c7c81f8-688d-44e5-8217-f5b9cc0b1039.json

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flat/objects/0c/7e/0c7e1c01-8415-401a-9866-e5e06201449c.json

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flat/objects/0c/80/0c8019f4-eedd-4e36-9c15-47a4767f80d0.json

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flat/objects/0c/80/0c80fb0e-3849-4323-8e33-6d893cf87fb1.json

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+      "evaluation_result_id": "ANNA/Stable Diffusion 1.5 (Base)/1771591481.616601#anna#human_preference_alignment_on_anna_benchmark_imagereward"
+    },
+    {
+      "evaluation_name": "ANNA",
+      "source_data": {
+        "dataset_name": "ANNA",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2301.02160"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Frechet Inception Distance with CLIP embeddings (FID_CLIP) measures the realism and diversity of images generated from abstractive news captions in the ANNA dataset. It compares the distribution of generated images to ground truth images in the CLIP embedding space. A lower score indicates better performance.",
+        "additional_details": {
+          "alphaxiv_y_axis": "FID_CLIP",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Image Realism and Diversity on ANNA Benchmark"
+        },
+        "metric_id": "image_realism_and_diversity_on_anna_benchmark",
+        "metric_name": "Image Realism and Diversity on ANNA Benchmark",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 7.7008
+      },
+      "evaluation_result_id": "ANNA/Stable Diffusion 1.5 (Base)/1771591481.616601#anna#image_realism_and_diversity_on_anna_benchmark"
+    },
+    {
+      "evaluation_name": "ANNA",
+      "source_data": {
+        "dataset_name": "ANNA",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2301.02160"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The Human Preference Score V2 (HPS V2) is a metric that predicts human preference scores for image-caption pairs on the ANNA dataset. It serves as an indicator of how well a generated image aligns with human perceptions of the abstractive news caption. A higher score is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "HPS V2 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Human Preference Alignment on ANNA Benchmark (HPS V2)"
+        },
+        "metric_id": "human_preference_alignment_on_anna_benchmark_hps_v2",
+        "metric_name": "Human Preference Alignment on ANNA Benchmark (HPS V2)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2312
+      },
+      "evaluation_result_id": "ANNA/Stable Diffusion 1.5 (Base)/1771591481.616601#anna#human_preference_alignment_on_anna_benchmark_hps_v2"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}