Datasets:

InserloftResearch
/

ChatESP

license: cc-by-4.0
task_categories:
  - text-generation
language:
  - es
tags:
  - Research
  - Synthetic
pretty_name: ChatESP
size_categories:
  - 100K<n<1M

ChatESP: A Massive Spanish Instruction Dataset for CausalLM Alignment

ChatESP is a publication-grade, synthetically generated dataset in Spanish tailored for training and aligning Large Language Models (LLMs) via Supervised Fine-Tuning (SFT) and instruction tuning. It is designed to emulate authentic human-AI interactions spanning technical, lifestyle, and highly emotional scenarios.

Total Sample Size: 256,000 unique records.
High Density: Extremely detailed responses structured in professional Markdown format, averaging over 1,400 characters per interaction (well over the 504-byte enterprise SFT criteria).

Features & Schema

Each record in the dataset is structured with the following metadata columns:

id (string): A unique conversational identifier formatted as ESP-XXXXXX.
hash (string): Unique SHA-256 footprint computed from the raw instruction to guarantee absolute mathematical uniqueness and prevent training leakage.
context (string): Situational setup identifying the domain difficulty, style, and assistant expectations.
instruction (string): Complex prompts mixing technical queries, semantic variables, and emotional situations.
response (string): Comprehensive, production-ready, veracious response formatted with rich Markdown headings (#, ##), bullet points, and codeblocks.
difficulty (string): Balanced tiers (Fácil, Medio, Difícil, Avanzado).
tone (string): Balanced persona styles (Formal, Informal, Técnico, Amigable, Humorístico).
category (string): Covering 20+ specialized categories spanning advanced computer science to emotional and psychological support.
char_count (integer): Accurate character length of the generated response.
quality_score (float): Synthesized quality metric (scaled 4.85 - 5.00) mimicking human evaluation labels.

Dataset Analytics & Visualization Gallery

All visualizations are rendered using a 100% black background (#000000) and high-contrast neon/glowing palettes optimized for Hugging Face dark layouts.

1. Dataset Categories Distribution

Proportions of conversational topics, showcasing a high density in emotional support and software engineering domains.

2. Conversational Tone Volumes

Uniform representation of conversational personas to prevent LLM alignment biases.

3. Difficulty Tiers Proportion

Perfect 25.0% split among Easy, Medium, Hard, and Advanced problem statements.

4. Mean Character Lengths per Domain

Evaluation of verbosity per category, displaying rich outputs across emotional support and systems engineering.

5. Quality Score Distributions across Categories

Dense boxplot validating that SFT quality scores consistently sit at production thresholds (4.85 to 5.00).

6. Probability Density of Output Response Size

Kernel Density Estimate (KDE) demonstrating response size concentration peaks around 1,410 characters.

7. Global Instruction Quality profile

High-density uniform-like histogram representing synthetic human evaluator scoring profiles.

8. Bivariate Correlation: Output Length vs Quality Metric

Scatter plot showing SFT response length clusters correlated with difficulty levels.

9. Mean Dataset Quality Index per Tone

Detailed zoom-in verifying semantic consistency across humor, technical, and formal personas.

10. Crosstabulated Heatmap: Difficulty Level vs Tone

A dense heatmap showcasing homogeneous sample volumes across cross-tabulated labels.

How to Load the Dataset

import pandas as pd

# Load a specific shard
df = pd.read_parquet('ChatESP/train/ChatESP-Part1.parquet')
display(df.head())

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.