Datasets:
ArXiv:
License:
license: cc-by-nc-4.0 | |
viewer: false | |
# Baidu ULTR Dataset - Baidu 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 the BERT cross-encoder with 12 layers from Baidu released in the [official starter-kit](https://github.com/ChuXiaokai/baidu_ultr_dataset/) 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_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 | | |
| title_bm25 | Tensor[float32] | BM25 score for document titles | | |
| abstract_bm25 | 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 | | |
| title_bm25 | Tensor[float32] | BM25 score for document titles | | |
| abstract_bm25 | 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) | |
``` | |