Update README with reference to Acta-Proprietary

README.md

@@ -11,35 +11,35 @@ tags:
 - high-quality
 - conversations
 size_categories:
-- 10K<n<100K
+- n<1K
 ---
 
 # 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.
 
+> **Looking for the full dataset?** The complete 37K proprietary version is available at [DJLougen/Acta-Proprietary](https://huggingface.co/datasets/DJLougen/Acta-Proprietary).
+
 ## Overview
 
-This dataset 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.
+This public sample contains 1,200 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 (Public Sample):**
+**Key Statistics:**
 - **Train:** 1,000 samples
 - **Validation:** 200 samples
 - **Avg semantic diversity:** 0.53
 - **Avg lexical diversity:** 0.47
 - **Sources:** 5 datasets harmonized
 
-*Full 37K proprietary version available separately.*
-
 ## 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) |
+| 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 |
@@ -50,57 +50,11 @@ Each sample was scored using 8 factors weighted by their correlation with traini
 
 | Source | Samples | Avg Quality | Characteristics |
 |--------|---------|-------------|-----------------|
-| FlameFox Agentic | 22,000 | 0.819 | Highest diversity, balanced tools |
-| Hermes Reasoning | 10,245 | 0.598 | Good semantic diversity |
-| Hermes Multi-turn | 3,529 | 0.534 | Multi-turn, deduplicated |
-| Smolagents Code | 527 | 0.592 | Low redundancy |
-| SWE Agent GLM | 170 | 0.433 | Shortest, least repetitive traces |
-
-## Dataset Structure
-
-```json
-{
-  "conversations": [
-    {"role": "system", "content": "..."},
-    {"role": "user", "content": "..."},
-    {"role": "assistant", "content": "..."},
-    {"role": "tool", "content": "..."}
-  ],
-  "source": "flamefox_agentic",
-  "metadata": {
-    "original_keys": ["conversations", "tools", ...]
-  },
-  "quality_metrics": {
-    "semantic_diversity": 0.546,
-    "lexical_diversity": 0.400,
-    "verb_diversity": 0.750,
-    "quality_score": 0.900
-  }
-}
-```
-
-## Quality Thresholds
-
-Samples must pass all hard thresholds:
-- **Lexical diversity** ≥ 0.35
-- **Semantic diversity** ≥ 0.40
-- **Verb uniqueness** ≥ 0.01
-- **Turn count:** 2-12 turns
-
-**Pass rates by source:**
-- FlameFox: 80%
-- Hermes Reasoning: 74%
-- Hermes Multi-turn: 48%
-- Smolagents: 2% (heavily templated)
-- SWE Agent: 1% (verbose/repetitive)
-
-## 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
+| FlameFox Agentic | ~600 | 0.819 | Highest diversity, balanced tools |
+| Hermes Reasoning | ~300 | 0.598 | Good semantic diversity |
+| Hermes Multi-turn | ~100 | 0.534 | Multi-turn, deduplicated |
+| Smolagents Code | ~15 | 0.592 | Low redundancy |
+| SWE Agent GLM | ~5 | 0.433 | Shortest, least repetitive traces |
 
 ## Usage
 
@@ -113,8 +67,18 @@ 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
@@ -138,3 +102,7 @@ Source datasets:
 - [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)