metadata
license: mit
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- interview
- software-engineering
- chat
- instruction-tuning
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
Interview Coach Dataset
Chat-format dataset for fine-tuning an AI interview coach on software engineering interview Q&A.
Dataset Summary
Each example is a single user/assistant turn in OpenAI-style messages format, suitable for instruction / chat fine-tuning (e.g. Unsloth, TRL, Hugging Face SFTTrainer).
- Train: ~1,017 examples
- Validation: ~114 examples
- Total: ~1,131 examples
- Split: 90% / 10% (seeded shuffle)
Data Structure
{
"messages": [
{"role": "user", "content": "...interview question..."},
{"role": "assistant", "content": "...model answer..."}
]
}
| Field | Type | Description |
|---|---|---|
messages |
list | Conversation turns |
messages[].role |
string | "user" or "assistant" |
messages[].content |
string | Question or answer text |
Source
Derived from generated interview Q&A (api_dataset.jsonl), then shuffled, split, and reformatted for chat fine-tuning.
Intended Use
Fine-tuning small/medium instruct models to practice answering technical interview questions (APIs, systems design, coding concepts, behavioral, etc.).
Limitations
- Synthetic / generated content — may contain inaccuracies.
- English only.
- Coverage is uneven across topics and difficulty.
- Not a substitute for real interview feedback.
Loading
from datasets import load_dataset
ds = load_dataset("shimogerald/interview-coach-dataset")
print(ds["train"][0])