Dataset Card for FinBench

Dataset Statistics

[Introduction]

Task Statistics

The following table reports the task description, dataset name (for datasets loading), the number and positive ratio of train/validation/test sets, the number of classification classes (all is 2), and the number of features.

Task	Description	Dataset	#Classes	#Features	#Train [Pos%]	#Val [Pos%]	#Test [Pos%]
Credit-card Default	Predict whether a user will default on the credit card or not.	cd1	2	9	2738 [7.0%]	305 [6.9%]	1305 [6.2%]
cd2	2	23	18900 [22.3%]	2100 [22.3%]	9000 [21.8%]
Loan Default	Predict whether a user will default on the loan or not.	ld1	2	12	2118 [8.9%]	236 [8.5%]	1010 [9.0%]
ld2	2	11	18041 [21.7%]	2005 [20.8%]	8592 [21.8%]
ld3	2	35	142060 [21.6%]	15785 [21.3%]	67648 [22.1%]
Credit-card Fraud	Predict whether a user will commit fraud or not.	cf1	2	19	5352 [0.67%]	595 [1.1%]	2550 [0.90%]
cf2	2	120	5418 [6.0%]	603 [7.3%]	2581 [6.0%]
Customer Churn	Predict whether a user will churn or not. (customer attrition)	cc1	2	9	4189 [23.5%]	466 [22.7%]	1995 [22.4%]
cc2	2	10	6300 [20.8%]	700 [20.6%]	3000 [19.47%]
cc3	2	21	4437 [26.1%]	493 [24.9%]	2113 [27.8%]

Task	#Train	#Val	#Test
Credit-card Default	21638	2405	10305
Loan Default	162219	18026	77250
Credit-card Fraud	10770	1198	5131
Customer Churn	14926	1659	7108
Total	209553	23288	99794

Data Source

Task	Dataset	Source
Credit-card Default	cd1	Kaggle
cd2	Kaggle
Loan Default	ld1	Kaggle
ld2	Kaggle
ld3	Kaggle
Credit-card Fraud	cf1	Kaggle
cf2	Kaggle
Customer Churn	cc1	Kaggle
cc2	Kaggle
cc3	Kaggle

Language: English

Dataset Structure

Data Fields

import datasets

datasets.Features(
    {
        "X_ml": [datasets.Value(dtype="float")],  # (The tabular data array of the current instance)
        "X_ml_unscale": [datasets.Value(dtype="float")],  # (Scaled tabular data array of the current instance)
        "y": datasets.Value(dtype="int64"),  # (The label / ground-truth)
        "num_classes": datasets.Value("int64"),  # (The total number of classes)
        "num_features": datasets.Value("int64"),  # (The total number of features)
        "num_idx": [datasets.Value("int64")],  # (The indices of the numerical datatype columns)
        "cat_idx": [datasets.Value("int64")],  # (The indices of the categorical datatype columns)
        "cat_dim": [datasets.Value("int64")],  # (The dimension of each categorical column)
        "cat_str": [[datasets.Value("string")]],  # (The category names of categorical columns)
        "col_name": [datasets.Value("string")],  # (The name of each column)
        "X_instruction_for_profile": datasets.Value("string"),  # instructions (from tabular data) for profiles
        "X_profile": datasets.Value("string"),  # customer profiles built from instructions via LLMs
    }
)

Data Loading

HuggingFace Login (Optional)

# OR run huggingface-cli login
from huggingface_hub import login

hf_token = "hf_xxx"  # TODO: set a valid HuggingFace access token for loading datasets/models
login(token=hf_token)

Loading a Dataset

from datasets import load_dataset

ds_name = "cd1"  # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)

Loading the Splits

from datasets import load_dataset

ds_name = "cd1"  # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)

train_set = dataset["train"] if "train" in dataset else []
validation_set = dataset["validation"] if "validation" in dataset else []
test_set = dataset["test"] if "test" in dataset else []

Loading the Instances

from datasets import load_dataset

ds_name = "cd1"  # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
train_set = dataset["train"] if "train" in dataset else []

for train_instance in train_set:
    X_ml = train_instance["X_ml"]  # List[float] (The tabular data array of the current instance)
    X_ml_unscale = train_instance["X_ml_unscale"]  # List[float] (Scaled tabular data array of the current instance)
    y = train_instance["y"]  # int (The label / ground-truth)
    num_classes = train_instance["num_classes"]  # int (The total number of classes)
    num_features = train_instance["num_features"]  # int (The total number of features)
    num_idx = train_instance["num_idx"]  # List[int] (The indices of the numerical datatype columns)
    cat_idx = train_instance["cat_idx"]  # List[int] (The indices of the categorical datatype columns)
    cat_dim = train_instance["cat_dim"]  # List[int] (The dimension of each categorical column)
    cat_str = train_instance["cat_str"]  # List[List[str]] (The category names of categorical columns)
    col_name = train_instance["col_name"]  # List[str] (The name of each column)
    X_instruction_for_profile = train_instance["X_instruction_for_profile"]  # instructions for building profiles
    X_profile = train_instance["X_profile"]  # customer profiles built from instructions via LLMs

Contributions

[Contributions]

Citation

yin2023finbench

References

[References]

作者:

yuweiyin

数据集大小:

695.22 MB