Datasets:
ArXiv:
License:
| from enum import Enum | |
| from typing import List | |
| import datasets | |
| import pandas as pd | |
| from datasets import Features, Value, Array2D, Sequence, SplitGenerator, Split | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {philipphager/baidu-ultr_baidu-mlm-ctr}, | |
| author={Philipp Hager, Romain Deffayet}, | |
| year={2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| 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 to compute query-document vectors (768 dims): | |
| https://github.com/ChuXiaokai/baidu_ultr_dataset/ | |
| We link the model checkpoint also under `model/`. | |
| """ | |
| _HOMEPAGE = "https://huggingface.co/datasets/philipphager/baidu-ultr_baidu-mlm-ctr/" | |
| _LICENSE = "cc-by-nc-4.0" | |
| _VERSION = "0.1.0" | |
| class Config(str, Enum): | |
| ANNOTATIONS = "annotations" | |
| CLICKS = "clicks" | |
| class BaiduUltrBuilder(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version(_VERSION) | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name=Config.CLICKS, | |
| version=VERSION, | |
| description="Load train/val/test clicks from the Baidu ULTR dataset", | |
| ), | |
| datasets.BuilderConfig( | |
| name=Config.ANNOTATIONS, | |
| version=VERSION, | |
| description="Load expert annotations from the Baidu ULTR dataset", | |
| ), | |
| ] | |
| CLICK_FEATURES = Features( | |
| { | |
| ### Query features | |
| "query_id": Value("string"), | |
| "query_md5": Value("string"), | |
| "query": Sequence(Value("int32")), | |
| "query_length": Value("int32"), | |
| "n": Value("int32"), | |
| ### Doc features | |
| "url_md5": Sequence(Value("string")), | |
| "text_md5": Sequence(Value("string")), | |
| "title": Sequence(Sequence(Value("int32"))), | |
| "abstract": Sequence(Sequence(Value("int32"))), | |
| "query_document_embedding": Array2D((None, 768), "float16"), | |
| "click": Sequence(Value("int32")), | |
| ### SERP features | |
| "position": Sequence(Value("int32")), | |
| "media_type": Sequence(Value("int32")), | |
| "displayed_time": Sequence(Value("float32")), | |
| "serp_height": Sequence(Value("int32")), | |
| "slipoff_count_after_click": Sequence(Value("int32")), | |
| ### LTR features | |
| "bm25": Sequence(Value("float32")), | |
| "title_bm25": Sequence(Value("float32")), | |
| "abstract_bm25": Sequence(Value("float32")), | |
| "tf_idf": Sequence(Value("float32")), | |
| "tf": Sequence(Value("float32")), | |
| "idf": Sequence(Value("float32")), | |
| "ql_jelinek_mercer_short": Sequence(Value("float32")), | |
| "ql_jelinek_mercer_long": Sequence(Value("float32")), | |
| "ql_dirichlet": Sequence(Value("float32")), | |
| "document_length": Sequence(Value("int32")), | |
| "title_length": Sequence(Value("int32")), | |
| "abstract_length": Sequence(Value("int32")), | |
| } | |
| ) | |
| ANNOTATION_FEATURES = Features( | |
| { | |
| ### Query features | |
| "query_id": Value("string"), | |
| "query_md5": Value("string"), | |
| "query": Sequence(Value("int32")), | |
| "query_length": Value("int32"), | |
| "frequency_bucket": Value("int32"), | |
| "n": Value("int32"), | |
| ### Doc features | |
| "text_md5": Sequence(Value("string")), | |
| "title": Sequence(Sequence(Value("int32"))), | |
| "abstract": Sequence(Sequence(Value("int32"))), | |
| "query_document_embedding": Array2D((None, 768), "float16"), | |
| "label": Sequence(Value("int32")), | |
| ### LTR features | |
| "bm25": Sequence(Value("float32")), | |
| "title_bm25": Sequence(Value("float32")), | |
| "abstract_bm25": Sequence(Value("float32")), | |
| "tf_idf": Sequence(Value("float32")), | |
| "tf": Sequence(Value("float32")), | |
| "idf": Sequence(Value("float32")), | |
| "ql_jelinek_mercer_short": Sequence(Value("float32")), | |
| "ql_jelinek_mercer_long": Sequence(Value("float32")), | |
| "ql_dirichlet": Sequence(Value("float32")), | |
| "document_length": Sequence(Value("int32")), | |
| "title_length": Sequence(Value("int32")), | |
| "abstract_length": Sequence(Value("int32")), | |
| } | |
| ) | |
| DEFAULT_CONFIG_NAME = Config.CLICKS | |
| def _info(self): | |
| if self.config.name == Config.CLICKS: | |
| features = self.CLICK_FEATURES | |
| elif self.config.name == Config.ANNOTATIONS: | |
| features = self.ANNOTATION_FEATURES | |
| else: | |
| raise ValueError( | |
| f"Config {self.config.name} must be in ['clicks', 'annotations']" | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| if self.config.name == Config.CLICKS: | |
| train_files = self.download_clicks(dl_manager, parts=[1]) | |
| test_files = self.download_clicks(dl_manager, parts=[0]) | |
| query_columns = [ | |
| "query_id", | |
| "query_md5", | |
| "query", | |
| "query_length", | |
| ] | |
| agg_columns = [ | |
| "text_md5", | |
| "title", | |
| "abstract", | |
| "query_document_embedding", | |
| "click", | |
| "position", | |
| "media_type", | |
| "displayed_time", | |
| "serp_height", | |
| "slipoff_count_after_click", | |
| "bm25", | |
| "title_bm25", | |
| "abstract_bm25", | |
| "tf_idf", | |
| "tf", | |
| "idf", | |
| "ql_jelinek_mercer_short", | |
| "ql_jelinek_mercer_long", | |
| "ql_dirichlet", | |
| "document_length", | |
| "title_length", | |
| "abstract_length", | |
| ] | |
| return [ | |
| SplitGenerator( | |
| name=Split.TRAIN, | |
| gen_kwargs={ | |
| "files": train_files, | |
| "query_columns": query_columns, | |
| "agg_columns": agg_columns, | |
| }, | |
| ), | |
| SplitGenerator( | |
| name=Split.TEST, | |
| gen_kwargs={ | |
| "files": test_files, | |
| "query_columns": query_columns, | |
| "agg_columns": agg_columns, | |
| }, | |
| ), | |
| ] | |
| elif self.config.name == Config.ANNOTATIONS: | |
| test_files = dl_manager.download(["parts/validation.feather"]) | |
| query_columns = [ | |
| "query_id", | |
| "query_md5", | |
| "query", | |
| "frequency_bucket", | |
| ] | |
| agg_columns = [ | |
| "url_md5", | |
| "text_md5", | |
| "title", | |
| "abstract", | |
| "query_document_embedding", | |
| "label", | |
| "bm25", | |
| "title_bm25", | |
| "abstract_bm25", | |
| "tf_idf", | |
| "tf", | |
| "idf", | |
| "ql_jelinek_mercer_short", | |
| "ql_jelinek_mercer_long", | |
| "ql_dirichlet", | |
| "document_length", | |
| "title_length", | |
| "abstract_length", | |
| ] | |
| return [ | |
| SplitGenerator( | |
| name=Split.TEST, | |
| gen_kwargs={ | |
| "files": test_files, | |
| "query_columns": query_columns, | |
| "agg_columns": agg_columns, | |
| }, | |
| ) | |
| ] | |
| else: | |
| raise ValueError("Config name must be in ['clicks', 'annotations']") | |
| def download_clicks(self, dl_manager, parts: List[int], splits_per_part: int = 10): | |
| urls = [ | |
| f"parts/part-{p}_split-{s}.feather" | |
| for p in parts | |
| for s in range(splits_per_part) | |
| ] | |
| return dl_manager.download(urls) | |
| def _generate_examples( | |
| self, | |
| files: List[str], | |
| query_columns: List[str], | |
| agg_columns: List[str], | |
| ): | |
| """ | |
| Reads dataset partitions and aggregates document features per query. | |
| :param files: List of .feather files to load from disk. | |
| :param query_columns: Columns with one value per query. E.g., query_id, | |
| frequency bucket, etc. | |
| :param agg_columns: Columns with one value per document that should be | |
| aggregated per query. E.g., click, position, query_document_embeddings, etc. | |
| :return: | |
| """ | |
| for file in files: | |
| df = pd.read_feather(file) | |
| current_query_id = None | |
| sample_key = None | |
| sample = None | |
| for i in range(len(df)): | |
| row = df.iloc[i] | |
| if i > 50_000: | |
| return | |
| if current_query_id != row["query_id"]: | |
| if current_query_id is not None: | |
| yield sample_key, sample | |
| current_query_id = row["query_id"] | |
| sample_key = f"{file}-{current_query_id}" | |
| sample = {"n": 0} | |
| for column in query_columns: | |
| sample[column] = row[column] | |
| for column in agg_columns: | |
| sample[column] = [] | |
| for column in agg_columns: | |
| sample[column].append(row[column]) | |
| sample["n"] += 1 | |
| yield sample_key, sample | |