Update README.md

README.md

@@ -2,7 +2,6 @@
 license: cc-by-nc-4.0
 viewer: false
 ---
-
 # Baidu ULTR Dataset - Baidu BERT-12l-12h
 ## Setup
 1. Install huggingface [datasets](https://huggingface.co/docs/datasets/installation)
@@ -38,6 +37,37 @@ dataset = load_dataset(
 dataset.set_format("torch") #  [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]
 ```
 
+## Available features
+Each row of the click / annotation dataset contains the following attributes. Use a custom `collate_fn` to select specific features (see below):
+
+### Click dataset
+| name                         | dtype          | description |
+|------------------------------|----------------|-------------|
+| 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 |
+| text_md5                     | List[string]   | MD5 hash of document title and abstract |
+| query_document_embedding     | 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 |
+
+
+### Expert annotation dataset
+| name                         | dtype          | description |
+|------------------------------|----------------|-------------|
+| 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 |
+| frequency_bucket             | int32          | Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency) |
+
 ## Example PyTorch collate function
 Each sample in the dataset is a single query with multiple documents.
 The following example demonstrates how to create a batch containing multiple queries with varying numbers of documents by applying padding: 
@@ -60,7 +90,9 @@ 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"]),