Datasets:
text stringclasses 10
values | label class label 2
classes |
|---|---|
The analysis of the transactional dataset reveals a significant correlation between the retry event count and the payment amount, suggesting that higher amounts are more likely to be retried; however, the coefficient of determination (R-squared) for the relationship between retry event count and payment success is only... | 1failure |
A payment retry event was triggered after a 500 HTTP status code was received due to an insufficient funds error and the payment amount is greater than $500. Additionally, the merchant category code is related to food delivery services and the payment method used is credit card. | 1failure |
This payment transaction has a retry after 24 hours if the initial payment is declined due to an insufficient fund error and the retry amount is lower than the initial payment by 10%. | 0success |
Analysis of historical payment transaction data reveals that the probability of a retry being successful is inversely proportional to the time interval between the initial and subsequent transaction attempts. Furthermore, the likelihood of success is also influenced by the presence of errors in the initial transaction,... | 1failure |
The transaction was initiated with a payment gateway that utilized a secure socket layer protocol to encrypt the sensitive information, but the retry event was triggered due to a timeout error after 5 seconds of inactivity, which may indicate a potential issue with the network connectivity or the payment processing sys... | 1failure |
There was a significant increase in payment retry events in the first quarter of 2022, with 45.67% of all payment transactions exhibiting multiple retry attempts. However, it's worth noting that the overall payment retry rate has decreased by 12.15% compared to the same period in 2021, and the average number of retry a... | 0success |
Given a sequence of payment transactions with retry events, where each event contains attributes such as transaction ID, retry count, timestamp, and error code, develop a probabilistic model to predict the likelihood of a payment retry being successful based on the inherent characteristics of the transaction and the hi... | 0success |
The consumer reports a payment retry with a merchant whose 30-day failure rate exceeds the industry average, where the amount is below the average daily limit, the payment method is digital wallet, and the customer has a history of successful transactions with the same merchant but the attempted transaction was sent ou... | 1failure |
The payment transaction of £500 was initiated on 2022-01-01 and was retried after 30 minutes due to a temporary network outage, with the second attempt resulting in a successful charge but with an unexpected merchant fee of £10. | 1failure |
The probability of payment retry success increases when the initial transaction is declined due to insufficient funds, but a subsequent retry after a 24-hour delay results in a success rate of 0.85, whereas a retry after a 3-day delay yields a success rate of 0.92. | 0success |
Dataset Card for my-distiset-d286a459
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/Vijay045/my-distiset-d286a459/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/Vijay045/my-distiset-d286a459/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"label": 1,
"text": "The analysis of the transactional dataset reveals a significant correlation between the retry event count and the payment amount, suggesting that higher amounts are more likely to be retried; however, the coefficient of determination (R-squared) for the relationship between retry event count and payment success is only 0.32, indicating a moderate relationship, while the multivariate logistic regression model including retry event count, payment amount, and the interaction term between them yields an accuracy of 87.5% in predicting payment success."
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("Vijay045/my-distiset-d286a459", "default")
Or simply as it follows, since there's only one configuration and is named default:
from datasets import load_dataset
ds = load_dataset("Vijay045/my-distiset-d286a459")
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