Dataset Card for bfl_sft_data

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/poorval/bfl_sft_data/raw/main/pipeline.yaml"

or explore the configuration:

distilabel pipeline info --config "https://huggingface.co/datasets/poorval/bfl_sft_data/raw/main/pipeline.yaml"

Dataset structure

The examples have the following structure per configuration:

Configuration: default

---

{
    "completion": "Bajaj Finance offers an attractive two-wheeler loan scheme. Here\u0027s an overview of the loan process:\n\n**Eligibility Criteria:**\n\n1. Age: The applicant must be at least 21 years old (for loans up to \u20b9 1 lakh) and 28 years old (for loans above \u20b91 lakh) at the time of loan maturity.\n2. Income: Minimum income of \u20b9 8,000 per month.\n3. Employment: Employed individuals, self-employed, or those with a regular income are eligible.\n4. KYC Documents: You will need to provide proof of identity, address, income, and age.\n\n**Loan Amount:**\n\n1. Maximum loan amount: Up to \u20b9 11.79 lakhs.\n2. Loan tenure: 3-60 months.\n\n**Interest Rates:**\n\nBajaj Finance offers interest rates as low as 9.95% - 15.00% for Two Wheeler Loans, depending on your credit score and profile.\n\n**Loan Process:**\n\n1. **Online Application:**\n    - Visit the Bajaj Finserv website and fill in the pre-assessment form with your details.\n    - You will be provided with a pre-approved loan offer with crucial details like EMI, loan amount, and interest rates.\n\n2. **Application Submission:**\n    - If you\u0027re satisfied with the pre-approved loan offer, proceed with the online application process.\n    - You can apply by visiting a nearby Bajaj Finserv branch or through an advisor.\n\n3. **Documentation:**\n    - You will need to submit the following documents:\n        * PAN Card\n        * Proof of identity\n        * Proof of income (income proof for self-employed individuals)\n        * KYC documents\n        - Utility bills and property documents as an address proof\n    - Ensure all documents are authentic and complete.\n\n4. **Loan Disbursement:**\n    - Once your loan application is accepted, the loan amount will be disbursed to the dealership or directly to you.\n\n5. **EMI Calculation:**\n    - The EMI will be computed based on the loan tenure, interest rate, and loan amount.\n\n**Additional Benefits:**\n\n1. **Free insurance cover:** Bajaj Finance offers a free accident insurance cover of \u20b9 50,000 for female riders and \u20b9 20,000 for male riders with a loan amount of more than \u20b950,000.\n\n2. **Insurance Discounts:** If you purchase a comprehensive insurance plan, you can get a discount on the premium rates.\n\n**Note:** Bajaj Finance may charge service fees (usually \u20b9 1,200-8,000) and processing fees for loan applications. Bajaj Finserv may vary over time, so keep an eye on their official website for up-to-date information.",
    "prompt": "I am planning to buy a two-wheeler and I am planning to take a loan from Bajaj Finance.  Can you tell me about the loan process for two wheeler loan at Bajaj Finance? \n\n",
    "system_prompt": "You are a chatbot designed to assist users with inquiries and provide information about Bajaj Finance, a leading NBFC in India offering a range of financial products and services. Your purpose is to help users find accurate details on existing schemes, understand terms and conditions, and offer guidance on loan applications, insurance, and other products. Answer frequently asked questions, clarify doubts, and provide up-to-date information about Bajaj Finance, its products, and services."
}

This subset can be loaded as:

from datasets import load_dataset

ds = load_dataset("poorval/bfl_sft_data", "default")

Or simply as it follows, since there's only one configuration and is named default:

from datasets import load_dataset

ds = load_dataset("poorval/bfl_sft_data")