Update README.md

README.md

@@ -19,7 +19,7 @@ The model is available under `model/`.
 from datasets import load_dataset
 
 dataset = load_dataset(
-    "philipphager/baidu-ultr_uva-mlm-ctr",
+    "philipphager/baidu-ultr_baidu-mlm-ctr",
     name="clicks",
     split="train", # ["train", "test"]
     cache_dir="~/.cache/huggingface",
@@ -33,7 +33,7 @@ dataset.set_format("torch") #  [None, "numpy", "torch", "tensorflow", "pandas",
 from datasets import load_dataset
 
 dataset = load_dataset(
-    "philipphager/baidu-ultr_uva-mlm-ctr",
+    "philipphager/baidu-ultr_baidu-mlm-ctr",
     name="annotations",
     split="test",
     cache_dir="~/.cache/huggingface",
@@ -50,16 +50,32 @@ Each row of the click / annotation dataset contains the following attributes. Us
 |------------------------------|----------------|-------------|
 | query_id                     | string         | Baidu query_id |
 | query_md5                    | string         | MD5 hash of query text |
-| url_md5                      | List[string]   | MD5 hash of document url, most reliable document identifier |
+| query                        | List[int32]    | List of query tokens |
+| query_length                 | int32          | Number of query tokens |
+| n                            | int32          | Number of documents for current query, useful for padding |
+| url_md5                      | List[string]   | MD5 hash of document URL, most reliable document identifier |
 | text_md5                     | List[string]   | MD5 hash of document title and abstract |
-| query_document_embedding     | Tensor[float16]| BERT CLS token |
+| title                        | List[List[int32]]    | List of tokens for document titles |
+| abstract                     | List[List[int32]]    | List of tokens for document abstracts |
+| query_document_embedding     | Tensor[Tensor[float16]]| BERT CLS token |
 | click                        | Tensor[int32]  | Click / no click on a document |
-| n                            | int32          | Number of documents for current query, useful for padding |
 | position                     | Tensor[int32]  | Position in ranking (does not always match original item position) |
-| media_type                   | Tensor[int32]  | Document type (label encoding recommended as ids do not occupy a continous integer range) |
-| displayed_time               | Tensor[float32]| Seconds a document was displayed on screen |
-| serp_height                  | Tensor[int32]  | Pixel height of a document on screen |
-| slipoff_count_after_click    | Tensor[int32]  | Number of times a document was scrolled off screen after previously clicking on it |
+| media_type                   | Tensor[int32]  | Document type (label encoding recommended as IDs do not occupy a continuous integer range) |
+| displayed_time               | Tensor[float32]| Seconds a document was displayed on the screen |
+| 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 |
+| 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 |
+| ql_jelinek_mercer_short      | Tensor[float32]  | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.1) |
+| ql_jelinek_mercer_long       | Tensor[float32]  | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.7) |
+| ql_dirichlet                 | Tensor[float32]  | Query likelihood score for documents using Dirichlet smoothing (lambda = 128) |
+| document_length              | Tensor[int32]    | Length of documents |
+| title_length                 | Tensor[int32]    | Length of document titles |
+| abstract_length              | Tensor[int32]    | Length of document abstracts |
 
 
 ### Expert annotation dataset
@@ -67,11 +83,28 @@ Each row of the click / annotation dataset contains the following attributes. Us
 |------------------------------|----------------|-------------|
 | query_id                     | string         | Baidu query_id |
 | query_md5                    | string         | MD5 hash of query text |
-| text_md5                     | List[string]   | MD5 hash of document title and abstract |
-| query_document_embedding     | Tensor[float16]| BERT CLS token |
-| label                        | Tensor[int32]  | Relevance judgment on a scale from 0 (bad) to 4 (excellent) |
-| n                            | int32          | Number of documents for current query, useful for padding |
+| query                        | List[int32]    | List of query tokens |
+| query_length                 | int32          | Number of query tokens |
 | frequency_bucket             | int32          | Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency) |
+| n                            | int32          | Number of documents for current query, useful for padding |
+| url_md5                      | List[string]   | MD5 hash of document URL, most reliable document identifier |
+| text_md5                     | List[string]   | MD5 hash of document title and abstract |
+| title                        | List[List[int32]]    | List of tokens for document titles |
+| abstract                     | List[List[int32]]    | List of tokens for document abstracts |
+| 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 |
+| 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 |
+| ql_jelinek_mercer_short      | Tensor[float32]  | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.1) |
+| ql_jelinek_mercer_long       | Tensor[float32]  | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.7) |
+| ql_dirichlet                 | Tensor[float32]  | Query likelihood score for documents using Dirichlet smoothing (lambda = 128) |
+| document_length              | Tensor[int32]    | Length of documents |
+| title_length                 | Tensor[int32]    | Length of document titles |
+| abstract_length              | Tensor[int32]    | Length of document abstracts |
 
 ## Example PyTorch collate function
 Each sample in the dataset is a single query with multiple documents.
@@ -95,7 +128,9 @@ def collate_clicks(samples: List):
         batch["n"].append(sample["n"])
 
     return {
-        "query_document_embedding": pad_sequence(batch["query_document_embedding"], batch_first=True),
+        "query_document_embedding": pad_sequence(
+            batch["query_document_embedding"], batch_first=True
+        ),
         "position": pad_sequence(batch["position"], batch_first=True),
         "click": pad_sequence(batch["click"], batch_first=True),
         "n": torch.tensor(batch["n"]),
@@ -103,3 +138,4 @@ def collate_clicks(samples: List):
 
 loader = DataLoader(dataset, collate_fn=collate_clicks, batch_size=16)
 ```
+