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 | | |
| | 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) | |
| ``` | |