add open source information

app.py

@@ -121,7 +121,7 @@ def init_leaderboard(ori_dataframe, model_info_df, sort_val: str | list | None =
     merged_df = merged_df[new_cols]
     if sort_val:
         if isinstance(sort_val, list):
-            assert sort_val[0] == 'TestData Leakage'
+            assert sort_val[0] == 'Test Leak.'
             # ipdb.set_trace()
             leakage_order = pd.Categorical(merged_df[sort_val[0]], categories=['No', 'Yes', 'N/A'], ordered=True)
             merged_df['leakage_order'] = leakage_order
@@ -170,10 +170,11 @@ def init_leaderboard(ori_dataframe, model_info_df, sort_val: str | list | None =
         # ],
         filter_columns=[
             ColumnFilter(ModelInfoColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(ModelInfoColumn.testdata_leakage.name, type="checkboxgroup", label="TestData Leakage"),
+            ColumnFilter(ModelInfoColumn.testdata_leakage.name, type="checkboxgroup", label="Test Leak."),
+            ColumnFilter(ModelInfoColumn.code_available.name, type="checkboxgroup", label="OSS"),
         ],
         # bool_checkboxgroup_label="",
-        column_widths=[40, 150] + [180 for _ in range(len(merged_df.columns)-2)],
+        column_widths=[30, 180] + [130 for _ in range(len(merged_df.columns)-2)],
         interactive=False,
     )
 
@@ -185,7 +186,7 @@ with demo:
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem('🏅 Overall', elem_id="llm-benchmark-tab-table", id=5):
             # leaderboard = init_leaderboard(overall_df, model_info_df, sort_val='Rank')
-            leaderboard = init_leaderboard(overall_df, model_info_df, sort_val=['TestData Leakage', 'MASE_Rank'])
+            leaderboard = init_leaderboard(overall_df, model_info_df, sort_val=['Test Leak.', 'MASE_Rank'])
             print(f'FINAL Overall LEADERBOARD {overall_df}')
         with gr.TabItem("🏅 By Domain", elem_id="llm-benchmark-tab-table", id=0):
             leaderboard = init_leaderboard(domain_df, model_info_df)
@@ -217,7 +218,7 @@ with demo:
 
         if (!target) { return []; }           // safety guard
 
-        // Ask Gradio’s front-end to re-compute its filters:
+        // Ask Gradio's front-end to re-compute its filters:
         target.dispatchEvent(new Event('input', { bubbles: true }));
 
         return [];                            // load() must return something
@@ -225,6 +226,7 @@ with demo:
         """
     )
 
+
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
             citation_button = gr.Textbox(

results/Chronos_small/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/amazon/chronos-t5-small",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/chronos.ipynb",
     "org": "AWS AI Labs",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/DLinear/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "The Chinese University of Hong Kong",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/FlowState-9.1M/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/ibm-research/flowstate",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/flowstate.ipynb",
     "org": "IBM Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/Lag-Llama/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/time-series-foundation-models/Lag-Llama",
     "org": "Morgan Stanley & Service Now",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/Moirai2/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/Salesforce/moirai-2.0-R-small",
     "code_link": "https://github.com/SalesforceAIResearch/uni2ts",
     "org": "Salesforce AI Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/Moirai_base/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/Salesforce/moirai-1.1-R-base",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/moirai.ipynb",
     "org": "Salesforce AI Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/Moirai_large/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/Salesforce/moirai-1.1-R-large",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/moirai.ipynb",
     "org": "Salesforce AI Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/Moirai_small/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/Salesforce/moirai-1.1-R-large",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/moirai.ipynb",
     "org": "Salesforce AI Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/N-BEATS/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "ServiceNow",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/PatchTST/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "Princeton University",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/TSOrchestra-test/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/Melady/TEMPO",
     "org": "Melady Lab @ USC",
-    "testdata_leakage": "Yes"
-}
+    "testdata_leakage": "Yes",
+    "code_available": "No"
+}

results/TSOrchestra/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://github.com/DC-research/TSorchestra",
     "org": "Melady Lab @ USC",
-    "testdata_leakage": "No"
-}
+    "testdata_leakage": "No",
+    "code_available": "No"
+}

results/TTM-R1-Pretrained/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/ibm-granite/granite-timeseries-ttm-r1",
     "org": "IBM Research",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/TTM-R2-Finetuned/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/ttm.ipynb",
     "org": "IBM Research",
-    "testdata_leakage": "Yes"
-}
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
+}

results/TTM-R2-Pretrained/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2",
     "org": "IBM Research",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/TiRex/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/NX-AI/TiRex",
     "org": "NXAI",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/TimeCopilot/config.json

@@ -3,5 +3,6 @@
     "model_type": "agentic",
     "model_dtype": "float32",
     "model_link": "https://github.com/AzulGarza/TimeCopilot",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/TimesFM-2.5/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/google/timesfm-2.5-200m-pytorch",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/timesfm2p5.ipynb",
     "org": "Google Research",
-    "testdata_leakage": "No"
-}
+    "testdata_leakage": "No",
+    "code_available": "Yes"
+}

results/Toto_Open_Base_1.0/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/Datadog/Toto-Open-Base-1.0",
     "org": "Datadog",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/YingLong_110m/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "bf16",
     "model_link": "https://huggingface.co/qcw2333/YingLong_110m",
     "org": "Alibaba",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/YingLong_300m/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "bf16",
     "model_link": "https://huggingface.co/qcw2333/YingLong_300m",
     "org": "Alibaba",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/YingLong_50m/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "bf16",
     "model_link": "https://huggingface.co/qcw2333/YingLong_50m",
     "org": "Alibaba",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/YingLong_6m/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "bf16",
     "model_link": "https://huggingface.co/qcw2333/YingLong_6m",
     "org": "Alibaba",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/auto_arima/config.json

@@ -2,5 +2,6 @@
     "model": "Auto_Arima",
     "model_type": "statistical",
     "model_dtype": "float32",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/auto_ets/config.json

@@ -2,5 +2,6 @@
     "model": "Auto_ETS",
     "model_type": "statistical",
     "model_dtype": "float32",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/auto_theta/config.json

@@ -2,5 +2,6 @@
     "model": "Auto_Theta",
     "model_type": "statistical",
     "model_dtype": "float32",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/chronos_base/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/amazon/chronos-t5-base",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/chronos.ipynb",
     "org": "AWS AI Labs",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/chronos_bolt_base/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/amazon/chronos-bolt-base",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/chronos.ipynb",
     "org": "AWS AI Labs",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/chronos_bolt_small/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/amazon/chronos-bolt-small",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/chronos.ipynb",
     "org": "AWS AI Labs",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/chronos_large/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/amazon/chronos-t5-large",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/chronos.ipynb",
     "org": "AWS AI Labs",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/crossformer/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "Shanghai Jiao Tong University",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/deepar/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "Amazon Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/granite-flowstate-r1/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/ibm-granite/granite-timeseries-flowstate-r1",
     "code_link": "https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/flowstate_gift_eval.ipynb",
     "org": "IBM Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/iTransformer/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "Tsinghua University",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/naive/config.json

@@ -2,5 +2,6 @@
     "model": "Naive",
     "model_type": "statistical",
     "model_dtype": "float32",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/seasonal_naive/config.json

@@ -2,5 +2,6 @@
     "model": "Seasonal_Naive",
     "model_type": "statistical",
     "model_dtype": "float32",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/sundial_base_128m/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/thuml/sundial-base-128m",
     "org": "Tsinghua University",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/tabpfn_ts/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://github.com/liam-sbhoo/tabpfn-time-series/tree/main",
     "org": "PriorLabs",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/tempo_ensemble/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/Melady/TEMPO",
     "org": "Melady Lab @ USC",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "No"
 }

results/tft/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "Google Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/tide/config.json

@@ -3,5 +3,6 @@
     "model_type": "deep-learning",
     "model_dtype": "float32",
     "org": "Google Research",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

results/timesfm/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://huggingface.co/google/timesfm-1.0-200m",
     "org": "Google Research",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/timesfm_2_0_500m/config.json

@@ -5,5 +5,6 @@
     "model_link": "https://huggingface.co/google/timesfm-2.0-500m-jax",
     "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/timesfm.ipynb",
     "org": "Google Research",
-    "testdata_leakage": "Yes"
+    "testdata_leakage": "Yes",
+    "code_available": "Yes"
 }

results/visionts/config.json

@@ -4,5 +4,6 @@
     "model_dtype": "float32",
     "model_link": "https://github.com/Keytoyze/VisionTS",
     "org": "Zhejiang University",
-    "testdata_leakage": "No"
+    "testdata_leakage": "No",
+    "code_available": "Yes"
 }

src/display/utils.py

@@ -35,7 +35,8 @@ model_info_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "n
 model_info_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, True)])
 model_info_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
 model_info_dict.append(["org", ColumnContent, ColumnContent("Organization", "str", True, hidden=False)])
-model_info_dict.append(["testdata_leakage", ColumnContent, ColumnContent("TestData Leakage", "str", True, hidden=False)])
+model_info_dict.append(["testdata_leakage", ColumnContent, ColumnContent("Test Leak.", "str", True, hidden=False)])
+model_info_dict.append(["code_available", ColumnContent, ColumnContent("OSS", "str", True, hidden=False)])
 # model_info_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
 
 # We use make dataclass to dynamically fill the scores from Tasks

src/leaderboard/read_evals.py

@@ -26,6 +26,7 @@ class ModelConfig:
     likes: int = 0
     num_params: int | str = 0
     testdata_leakage: str = "NA"
+    code_available: str = "NA"
 
     @classmethod
     def init_from_json_file(cls, json_filepath):
@@ -43,8 +44,9 @@ class ModelConfig:
         code_link = data.get("code_link", "")
         org = data.get("org", "")
         testdata_leakage = data.get("testdata_leakage", "N/A")
+        code_available = data.get("code_available", "NA")
         return cls(model=model, model_link=model_link, model_type=model_type, code_link=code_link, org=org,
-                   precision=precision, testdata_leakage=testdata_leakage)
+                   precision=precision, testdata_leakage=testdata_leakage, code_available=code_available)
 
     def to_dict(self):
         """Converts the model info to a dict compatible with our dataframe display"""
@@ -60,6 +62,7 @@ class ModelConfig:
             ModelInfoColumn.params.name: self.num_params,
             ModelInfoColumn.org.name: self.org,
             ModelInfoColumn.testdata_leakage.name: self.testdata_leakage,
+            ModelInfoColumn.code_available.name: self.code_available,
         }
 
         return data_dict