The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: OverflowError
Message: Python int too large to convert to C long
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows_from_streaming.py", line 171, in compute_first_rows_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2206, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in _infer_features_from_batch
pa_table = pa.Table.from_pydict(batch)
File "pyarrow/table.pxi", line 1812, in pyarrow.lib._Tabular.from_pydict
File "pyarrow/table.pxi", line 5275, in pyarrow.lib._from_pydict
File "pyarrow/array.pxi", line 374, in pyarrow.lib.asarray
File "pyarrow/array.pxi", line 344, in pyarrow.lib.array
File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/types.pxi", line 88, in pyarrow.lib._datatype_to_pep3118
OverflowError: Python int too large to convert to C longNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Avazu_x4
Dataset description:
This dataset contains about 10 days of labeled click-through data on mobile advertisements. It has 22 feature fields including user features and advertisement attributes. Following the same setting with the AutoInt work, we split the data randomly into 8:1:1 as the training set, validation set, and test set, respectively.
The dataset statistics are summarized as follows:
Dataset Total #Train #Validation #Test Avazu_x4 40,428,967 32,343,172 4,042,897 4,042,898 Avazu_x4_001
In this setting, we preprocess the data split by removing the
idfield that is useless for CTR prediction. In addition, we transform the timestamp field into three fields: hour, weekday, and is_weekend. For all categorical fields, we filter infrequent features by setting the threshold min_category_count=2 (performs well) and replace them with a default<OOV>token. Note that we do not follow the exact preprocessing steps in AutoInt, because the authors neither remove the uselessidfield nor specially preprocess the timestamp field. We fix embedding_dim=16 following the existing AutoInt work.Avazu_x4_002
In this setting, we preprocess the data split by removing the
idfield that is useless for CTR prediction. In addition, we transform the timestamp field into three fields: hour, weekday, and is_weekend. For all categorical fields, we filter infrequent features by setting the threshold min_category_count=1 and replace them with a default<OOV>token. Note that we found that min_category_count=1 performs the best, which is surprising. We fix embedding_dim=40 following the existing FGCNN work.
Download: https://huggingface.co/datasets/reczoo/Avazu_x4/tree/main
RecZoo Datasets: https://github.com/reczoo/Datasets
Used by papers:
- Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. In CIKM 2019.
- Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. BARS-CTR: Open Benchmarking for Click-Through Rate Prediction. In CIKM 2021.
Check the md5sum for data integrity:
$ md5sum train.csv valid.csv test.csv de3a27264cdabf66adf09df82328ccaa train.csv 33232931d84d6452d3f956e936cab2c9 valid.csv 3ebb774a9ca74d05919b84a3d402986d test.csv
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