---
license: cc-by-nc-4.0
viewer: false
---
# Baidu ULTR Dataset - UvA BERT-12l-12h
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).
The model is available at: https://huggingface.co/philipphager/baidu-ultr_uva-bert_naive-pointwise

## 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_baidu-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_baidu-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 |
| 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 |
| 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 |
| 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 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
| name                         | dtype          | description |
|------------------------------|----------------|-------------|
| query_id                     | string         | Baidu query_id |
| query_md5                    | string         | MD5 hash of query text |
| 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.
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)
```