---
language:
- en
license: apache-2.0
library_name: datasets
tags:
- agentic
- tool-use
- function-calling
- curated
- high-quality
- conversations
size_categories:
- 1K<n<10K
---

# Acta

*Acta* (Latin: "acts, deeds, records") - A premium curated sample of high-quality agentic tool-use conversations, filtered using an 8-factor quality model based on statistical correlation analysis of diversity metrics.

## Overview

This public sample contains **5,000 conversations** from the full 37K Acta dataset. It was created by analyzing 5 major agentic datasets (FlameFox, Hermes Reasoning, Hermes Multi-turn, Smolagents, SWE Agent) and applying evidence-based quality filters to maximize training signal density.

**Key Statistics:**
- **Train:** 4,500 samples
- **Validation:** 500 samples
- **Avg semantic diversity:** 0.53
- **Avg lexical diversity:** 0.47
- **Sources:** 5 datasets harmonized

## The 8-Factor Quality Model

Each sample was scored using 8 factors weighted by their correlation with training quality:

| Factor | Weight | Threshold | Rationale |
|--------|--------|-----------|-----------|
| Lexical Diversity | 0.25 | >=0.35 | unique_words / total_words (r=+0.967 with quality) |
| Semantic Diversity | 0.20 | >=0.40 | content richness excluding stopwords (r=+0.947) |
| Verb Uniqueness | 0.15 | >=0.01 | unique_verbs / total_verbs (r=+0.852) |
| Turn Efficiency | 0.15 | 2-12 turns | Optimal conversation length (longer = repetitive) |
| Tool Pattern Novelty | 0.10 | - | Penalty for over-represented tool sequences |
| Reasoning Density | 0.08 | - | Thinking blocks per assistant turn |
| Role Balance | 0.05 | - | User/assistant turn ratio |
| Content Brevity | 0.02 | - | Information density (shorter = denser signal) |

## Source Distribution

| Source | Samples | Avg Quality | Characteristics |
|--------|---------|-------------|-----------------|
| FlameFox Agentic | ~2,600 | 0.819 | Highest diversity, balanced tools |
| Hermes Reasoning | ~1,400 | 0.598 | Good semantic diversity |
| Hermes Multi-turn | ~450 | 0.534 | Multi-turn, deduplicated |
| Smolagents Code | ~70 | 0.592 | Low redundancy |
| SWE Agent GLM | ~20 | 0.433 | Shortest, least repetitive traces |

## Usage

```python
from datasets import load_dataset

# Load the curated sample
dataset = load_dataset("DJLougen/Acta")

# Access quality metrics
sample = dataset["train"][0]
print(sample["quality_score"])  # 0.0 - 1.0
print(sample["semantic_diversity"])  # 0.546
print(sample["lexical_diversity"])  # 0.400
```

## Key Findings

1. **Quality ≠ Quantity** - 37K curated samples > 200K raw samples
2. **Lexical diversity is the strongest quality predictor** (r=+0.967)
3. **More content ≠ better signal** - SWE Agent has 5x more text but 3x lower quality
4. **4-10 turns optimal** - longer conversations become repetitive (r=-0.982)
5. **Tool redundancy is rampant** - some patterns repeat 3000+ times in raw data

## Citation

```bibtex
@dataset{acta_2026,
  title = {Acta: A Quality-Curated Agentic Tool-Use Dataset},
  author = {Lougen, Daniel},
  year = {2026},
  url = {https://huggingface.co/datasets/DJLougen/Acta}
}
```

## License

Apache 2.0

## Acknowledgments

Source datasets:
- [FlameF0X/agentic-code](https://huggingface.co/datasets/FlameF0X/agentic-code)
- [interstellarninja/hermes_reasoning_tool_use](https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use)
- [interstellarninja/tool-use-multiturn-reasoning](https://huggingface.co/datasets/interstellarninja/tool-use-multiturn-reasoning)
- [smolagents/codeagent-traces](https://huggingface.co/datasets/smolagents/codeagent-traces)
- [DCAgent/neulab-nebius-swe-agent-trajectories-sandboxes_glm_4.7_traces_jupiter](https://huggingface.co/datasets/DCAgent/neulab-nebius-swe-agent-trajectories-sandboxes_glm_4.7_traces_jupiter)

---

**Full 37K proprietary dataset:** [DJLougen/Acta-Proprietary](https://huggingface.co/datasets/DJLougen/Acta-Proprietary)