Merge branch 'main' of hf.co:datasets/philipphager/baidu-ultr_baidu-mlm-ctr

README.md

@@ -65,8 +65,8 @@ Each row of the click / annotation dataset contains the following attributes. Us
 | serp_height                  | Tensor[int32]  | Pixel height of a document on the screen |
 | slipoff_count_after_click    | Tensor[int32]  | Number of times a document was scrolled off the screen after previously clicking on it |
 | bm25                         | Tensor[float32]  | BM25 score for documents |
-| title_bm25                   | Tensor[float32]  | BM25 score for document titles |
-| abstract_bm25                | Tensor[float32]  | BM25 score for document abstracts |
+| bm25_title                   | Tensor[float32]  | BM25 score for document titles |
+| bm25_abstract                | Tensor[float32]  | BM25 score for document abstracts |
 | tf_idf                       | Tensor[float32]  | TF-IDF score for documents |
 | tf                           | Tensor[float32]  | Term frequency for documents |
 | idf                          | Tensor[float32]  | Inverse document frequency for documents |
@@ -94,8 +94,8 @@ Each row of the click / annotation dataset contains the following attributes. Us
 | query_document_embedding     | Tensor[Tensor[float16]]  | BERT CLS token |
 | label                        | Tensor[int32]    | Relevance judgments on a scale from 0 (bad) to 4 (excellent) |
 | bm25                         | Tensor[float32]  | BM25 score for documents |
-| title_bm25                   | Tensor[float32]  | BM25 score for document titles |
-| abstract_bm25                | Tensor[float32]  | BM25 score for document abstracts |
+| bm25_title                   | Tensor[float32]  | BM25 score for document titles |
+| bm25_abstract                | Tensor[float32]  | BM25 score for document abstracts |
 | tf_idf                       | Tensor[float32]  | TF-IDF score for documents |
 | tf                           | Tensor[float32]  | Term frequency for documents |
 | idf                          | Tensor[float32]  | Inverse document frequency for documents |

baidu-ultr_baidu-mlm-ctr.py

@@ -74,8 +74,8 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
             "slipoff_count_after_click": Sequence(Value("int32")),
             ### LTR features
             "bm25": Sequence(Value("float32")),
-            "title_bm25": Sequence(Value("float32")),
-            "abstract_bm25": Sequence(Value("float32")),
+            "bm25_title": Sequence(Value("float32")),
+            "bm25_abstract": Sequence(Value("float32")),
             "tf_idf": Sequence(Value("float32")),
             "tf": Sequence(Value("float32")),
             "idf": Sequence(Value("float32")),
@@ -105,8 +105,8 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
             "label": Sequence(Value("int32")),
             ### LTR features
             "bm25": Sequence(Value("float32")),
-            "title_bm25": Sequence(Value("float32")),
-            "abstract_bm25": Sequence(Value("float32")),
+            "bm25_title": Sequence(Value("float32")),
+            "bm25_abstract": Sequence(Value("float32")),
             "tf_idf": Sequence(Value("float32")),
             "tf": Sequence(Value("float32")),
             "idf": Sequence(Value("float32")),
@@ -164,8 +164,8 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
                 "serp_height",
                 "slipoff_count_after_click",
                 "bm25",
-                "title_bm25",
-                "abstract_bm25",
+                "bm25_title",
+                "bm25_abstract",
                 "tf_idf",
                 "tf",
                 "idf",
@@ -211,8 +211,8 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
                 "query_document_embedding",
                 "label",
                 "bm25",
-                "title_bm25",
-                "abstract_bm25",
+                "bm25_title",
+                "bm25_abstract",
                 "tf_idf",
                 "tf",
                 "idf",
@@ -261,21 +261,26 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
         aggregated per query. E.g., click, position, query_document_embeddings, etc.
         :return:
         """
+        
         for file in files:
             df = pd.read_feather(file)
-            current_query_id = None
+            current_query = None
+            query_no = 0
             sample_key = None
             sample = None
 
             for i in range(len(df)):
                 row = df.iloc[i]
 
-                if current_query_id != row["query_id"]:
-                    if current_query_id is not None:
+                if current_query != row["query_md5"]:
+                    query_no += 1
+                    
+                    if current_query is not None:
                         yield sample_key, sample
 
-                    current_query_id = row["query_id"]
-                    sample_key = f"{file}-{current_query_id}"
+                    current_query = row["query_md5"]
+                    # Use the query number in sample_key as query_ids are not unique.
+                    sample_key = f"{file}-{query_no}"
                     sample = {"n": 0}
 
                     for column in query_columns: