update

aicrowd.json

 {
     "challenge_id": "esci-challenge-for-improving-product-search",
-    "task": "task_3_product_substitute_identification", 
+    "task": "task_1_query-product_ranking", 
     "gpu": true, 
     "authors": ["aicrowd-username"],
-    "description": "task_1_query-product_ranking, task_2_multiclass_product_classification",
+    "description": "task_3_product_substitute_identification, task_2_multiclass_product_classification",
     "license": "MIT"
 }
\ No newline at end of file

utils/dataset.py

@@ -340,6 +340,9 @@ class Task1DatasetWithDescription(Task1Dataset):
             "attention_mask": attention_mask,
             "speical_token_pos": input_ids_pos,
         }
+        
+        if self.model_type == 'bigbird':
+            feature['token_type_ids'] = torch.zeros_like(input_ids)
 
         meta = {
             "product_id": row['product_id'],

utils/lgb_predict_task1.py

@@ -6,21 +6,21 @@ __MAP__ = ["irrelevant", "complement", "substitute", "exact"]
 
 LGB_CONFIG = {
     "us": {
-        "product_feat": "/models/us-product-feat.csv",
+        "product_feat": "/models/us-product-feat-task-1-only.csv",
         "model_file": "/models/lgb-us-task-1.txt",
         "features": ['pred_0','pred_1','pred_2','pred_3',
             'label_0','label_1','label_2','label_3',
             'query_count','is_isbn','has_isbn','fold']
     },
     "jp": {
-        "product_feat": "/models/jp-product-feat.csv",
+        "product_feat": "/models/es-jp-product-feat-task-1-only.csv",
         "model_file": "/models/lgb-es-jp-task-1.txt",
         "features": ['pred_0','pred_1','pred_2','pred_3',
             'label_0','label_1','label_2','label_3',
             'query_count','is_isbn','has_isbn','fold','locale']
     },
     "es": {
-        "product_feat": "/models/es-product-feat.csv",
+        "product_feat": "/models/es-jp-product-feat-task-1-only.csv",
         "model_file": "/models/lgb-es-jp-task-1.txt",
         "features": ['pred_0','pred_1','pred_2','pred_3',
             'label_0','label_1','label_2','label_3',
@@ -30,7 +30,7 @@ LGB_CONFIG = {
 
 LOCALE_MAP = {'jp':0, 'es':1, 'us':2}
 COL_NAME = "product_id"
-WEIGHT_MAP = {0:0.5, 1:0.5, 2:0.5, 3:0.5, 4:0.25, 5:0.25}
+WEIGHT_MAP = {0:0.5, 1:0.5, 2:0.5, 3:0.5, 4:0.25, 5:0.25, 6:0.5, 7:0.5} # Need modification
 
 def lgb_predict_task1(df, locale):
     df = df.reset_index(drop=True)

utils/onnx_predict.py

@@ -43,11 +43,6 @@ def onnx_predict(sub_test, config):
     all_example = []
     
     for data in tqdm(loader):
-        # inputs = {
-        #     "input_ids": data["features"]["input_ids"],
-        #     "attention_mask": data["features"]["attention_mask"],
-        #     "speical_token_pos": data["features"]["speical_token_pos"],
-        # }
         for i, s in enumerate(session):
             all_output[i] += list(
                 s.run(output_names=["output"], input_feed=dict(data['features']))[0]  # type: ignore
@@ -97,14 +92,9 @@ def onnx_predict_task1(sub_test, config):
     all_product = []
 
     for data in tqdm(loader):
-        inputs = {
-            "input_ids": data["features"]["input_ids"],
-            "attention_mask": data["features"]["attention_mask"],
-            "speical_token_pos": data["features"]["speical_token_pos"],
-        }
         for i, s in enumerate(session):
             all_output[i] += list(
-                s.run(output_names=["output"], input_feed=dict(inputs))[0]  # type: ignore
+                s.run(output_names=["output"], input_feed=dict(data['features']))[0]  # type: ignore
             )
         all_query += data["meta"]["query_id"]
         all_product += data['meta']['product_id']

utils/run_task1.py

@@ -24,7 +24,7 @@ CONFIG = {
             "product": "/models/product.h5",
             "key": "us",
             "type": "deberta",
-            "fold_offset": 0,
+            "fold_offset": 0, # fold 0, 1
             "description": False,
         },
         {
@@ -43,7 +43,7 @@ CONFIG = {
             "product": "/models/cocolm.h5",
             "key": "us",
             "type": "cocolm",
-            "fold_offset": 2,
+            "fold_offset": 2, # fold 2, 3
             "description": False,
         },
         {
@@ -62,9 +62,29 @@ CONFIG = {
             "product": "/models/distilbart.h5",
             "key": "us",
             "type": "distilbart",
-            "fold_offset": 4,
+            "fold_offset": 4, # fold 4, 5
             "description": False,
         },
+        {
+            "clean": DeBertaCleanV2,
+            "encode": {
+                "query": 12506,
+                "product_title": 3771,
+                "product_id": 4787,
+                "index": 6477,
+                "product_description": 6865,
+                "product_bullet_point": 10739,
+                "product_brand": 4609,
+                "product_color_name": 3225,
+            },
+            "model": ["/models/us-bigbird-des-0-fp16.onnx", "/models/us-bigbird-des-1-fp16.onnx"],
+            "product": "/models/bigbird.h5",
+            "key": "us",
+            "type": "bigbird",
+            "tokenizer": "/models/bigbird.model",
+            "fold_offset": 6, # 6, 7 
+            "description":True,
+        },
     ],
     "jp": [
         {
@@ -85,6 +105,7 @@ CONFIG = {
             "key": "jp",
             "type": "mdeberta",
             "fold_offset": 0,
+            "description":False,
         },
     ],
     "es": [
@@ -106,6 +127,7 @@ CONFIG = {
             "key": "es",
             "type": "mdeberta",
             "fold_offset": 0,
+            "description":False,
         },
     ],
 }