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Baidu ULTR Dataset - UvA BERT-12l-12h

Query-document vectors and clicks for a subset of the Baidu Unbiased Learning to Rank dataset. 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
  2. Install pandas and pyarrow: pip install pandas pyarrow
  3. Optionally, you might need to install a 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:

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:

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:

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)
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