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README.md

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 license: cc-by-nc-4.0
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+# Baidu ULTR Dataset - Tencent BERT-12l-12h
+Query-document vectors and clicks for a subset of the [Baidu Unbiased Learning to Rank](https://arxiv.org/abs/2207.03051) dataset.
+This dataset uses the pretrained [BERT cross-encoder (Bert_Layer12_Head12) from Tencent](https://github.com/lixsh6/Tencent_wsdm_cup2023/tree/main/pytorch_unbias) published as part of the WSDM cup 2023 to compute query-document vectors (768 dims).
+
+## Setup
+1. Install huggingface [datasets](https://huggingface.co/docs/datasets/installation)
+2. Install [pandas](https://github.com/pandas-dev/pandas) and [pyarrow](https://arrow.apache.org/docs/python/index.html): `pip install pandas pyarrow`
+3. Optionally, you might need to install a [pyarrow-hotfix](https://github.com/pitrou/pyarrow-hotfix) if you cannot install `pyarrow >= 14.0.1` 
+4. You can now use the dataset as described below.
+
+## Load train / test click dataset:
+```Python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "philipphager/baidu-ultr_tencent-mlm-ctr",
+    name="clicks",
+    split="train", # ["train", "test"]
+    cache_dir="~/.cache/huggingface",
+)
+
+dataset.set_format("torch") #  [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]
+```
+
+## Load expert annotations:
+```Python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "philipphager/baidu-ultr_tencent-mlm-ctr",
+    name="annotations",
+    split="test",
+    cache_dir="~/.cache/huggingface",
+)
+
+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: 
+
+```Python
+import torch
+from typing import List
+from collections import defaultdict
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader
+
+
+def collate_clicks(samples: List):
+    batch = defaultdict(lambda: [])
+
+    for sample in samples:
+        batch["query_document_embedding"].append(sample["query_document_embedding"])
+        batch["position"].append(sample["position"])
+        batch["click"].append(sample["click"])
+        batch["n"].append(sample["n"])
+
+    return {
+        "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"]),
+    }
+
+loader = DataLoader(dataset, collate_fn=collate_clicks, batch_size=16)
+```