Update baidu-ultr_uva-mlm-ctr.py

baidu-ultr_uva-mlm-ctr.py

@@ -9,20 +9,23 @@ from datasets import Features, Value, Array2D, Sequence, SplitGenerator, Split
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-    title = {philipphager/baidu-ultr_baidu-mlm-ctr},
+    title = {philipphager/baidu-ultr_uva-mlm-ctr},
     author={Philipp Hager, Romain Deffayet},
     year={2023}
 }
 """
 
 _DESCRIPTION = """\
-Query-document vectors and clicks for a subset of the [Baidu Unbiased Learning to Rank dataset](https://arxiv.org/abs/2207.03051).
-This dataset uses a BERT cross-encoder with 12 layers trained on a Masked Language Modeling (MLM) and click-through-rate (CTR) prediction task to compute query-document vectors (768 dims).
+Query-document vectors and clicks for a subset of the Baidu Unbiased Learning to Rank
+dataset: https://arxiv.org/abs/2207.03051
 
-The model is available under `model/`.
+This dataset uses a Jax-based BERT cross-encoder with 12 layers pre-trained for 2 million steps
+on the Baidu ULTR dataset to create query-document embeddings (768 dims).
+
+We link the model checkpoint also under `model/`. 
 """
 
-_HOMEPAGE = "https://huggingface.co/datasets/philipphager/baidu-ultr_uva-mlm-ctr/"
+_HOMEPAGE = "https://huggingface.co/datasets/philipphager/baidu-ultr_baidu-mlm-ctr/"
 _LICENSE = "cc-by-nc-4.0"
 _VERSION = "0.1.0"
 
@@ -49,30 +52,69 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
 
     CLICK_FEATURES = Features(
         {
+            ### Query features
             "query_id": Value("string"),
             "query_md5": Value("string"),
+            "query": Sequence(Value("int32")),
+            "query_length": Value("int32"),
+            "n": Value("int32"),
+            ### Doc features
             "url_md5": Sequence(Value("string")),
             "text_md5": Sequence(Value("string")),
+            "title": Sequence(Sequence(Value("int32"))),
+            "abstract": Sequence(Sequence(Value("int32"))),
             "query_document_embedding": Array2D((None, 768), "float16"),
             "click": Sequence(Value("int32")),
-            "n": Value("int32"),
+            ### SERP features
             "position": Sequence(Value("int32")),
             "media_type": Sequence(Value("int32")),
             "displayed_time": Sequence(Value("float32")),
             "serp_height": Sequence(Value("int32")),
             "slipoff_count_after_click": Sequence(Value("int32")),
+            ### LTR features
+            "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")),
+            "ql_jelinek_mercer_short": Sequence(Value("float32")),
+            "ql_jelinek_mercer_long": Sequence(Value("float32")),
+            "ql_dirichlet": Sequence(Value("float32")),
+            "document_length": Sequence(Value("int32")),
+            "title_length": Sequence(Value("int32")),
+            "abstract_length": Sequence(Value("int32")),
         }
     )
 
     ANNOTATION_FEATURES = Features(
         {
+            ### Query features
             "query_id": Value("string"),
             "query_md5": Value("string"),
-            "text_md5": Value("string"),
+            "query": Sequence(Value("int32")),
+            "query_length": Value("int32"),
+            "frequency_bucket": Value("int32"),
+            "n": Value("int32"),
+            ### Doc features
+            "text_md5": Sequence(Value("string")),
+            "title": Sequence(Sequence(Value("int32"))),
+            "abstract": Sequence(Sequence(Value("int32"))),
             "query_document_embedding": Array2D((None, 768), "float16"),
             "label": Sequence(Value("int32")),
-            "n": Value("int32"),
-            "frequency_bucket": Value("int32"),
+            ### LTR features
+            "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")),
+            "ql_jelinek_mercer_short": Sequence(Value("float32")),
+            "ql_jelinek_mercer_long": Sequence(Value("float32")),
+            "ql_dirichlet": Sequence(Value("float32")),
+            "document_length": Sequence(Value("int32")),
+            "title_length": Sequence(Value("int32")),
+            "abstract_length": Sequence(Value("int32")),
         }
     )
 
@@ -104,19 +146,34 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
             query_columns = [
                 "query_id",
                 "query_md5",
+                "query",
+                "query_length",
             ]
 
             agg_columns = [
-                "query_md5",
                 "url_md5",
                 "text_md5",
-                "position",
-                "click",
+                "title",
+                "abstract",
                 "query_document_embedding",
+                "click",
+                "position",
                 "media_type",
                 "displayed_time",
                 "serp_height",
                 "slipoff_count_after_click",
+                "bm25",
+                "bm25_title",
+                "bm25_abstract",
+                "tf_idf",
+                "tf",
+                "idf",
+                "ql_jelinek_mercer_short",
+                "ql_jelinek_mercer_long",
+                "ql_dirichlet",
+                "document_length",
+                "title_length",
+                "abstract_length",
             ]
 
             return [
@@ -142,12 +199,28 @@ class BaiduUltrBuilder(datasets.GeneratorBasedBuilder):
             query_columns = [
                 "query_id",
                 "query_md5",
+                "query",
+                "query_length",
                 "frequency_bucket",
             ]
             agg_columns = [
                 "text_md5",
-                "label",
+                "title",
+                "abstract",
                 "query_document_embedding",
+                "label",
+                "bm25",
+                "bm25_title",
+                "bm25_abstract",
+                "tf_idf",
+                "tf",
+                "idf",
+                "ql_jelinek_mercer_short",
+                "ql_jelinek_mercer_long",
+                "ql_dirichlet",
+                "document_length",
+                "title_length",
+                "abstract_length",
             ]
 
             return [
@@ -187,21 +260,23 @@ 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
             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_no"]:
+                    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_no"]
+                    # Use the query number in sample_key as query_ids are not unique.
+                    sample_key = f"{file}-{current_query}"
                     sample = {"n": 0}
 
                     for column in query_columns: