Upload folder using huggingface_hub

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+reddit_threads_constructive.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,129 @@
+---
+language:
+- en
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+- conversational
+- text-classification
+task_ids:
+- dialogue-generation
+- conversational-response-selection
+pretty_name: Reddit Constructive Conversations
+dataset_info:
+  features:
+  - name: sdid
+    dtype: string
+  - name: text
+    dtype: string
+  config_name: default
+  download_size: 864000000
+  dataset_size: 864000000
+tags:
+- reddit
+- constructive-dialogue
+- conversations
+- social-media
+- discourse-analysis
+- high-quality
+- curated
+---
+
+# Reddit Constructive Conversations
+
+A curated collection of **802,066 constructive conversations** from Reddit. This dataset contains only the constructive discussions filtered from a larger corpus. 
+
+## Dataset Description
+
+This dataset contains Reddit conversations that have been identified as constructive through automated classification. Each sample represents a multi-author discussion thread that demonstrates positive discourse patterns like respectful disagreement, information sharing, collaborative problem-solving, and constructive engagement.
+
+### Key Features
+
+- **High Quality**: Only constructive conversations (filtered from 1.4M samples)  
+- **Substantial Scale**: 802,066 conversation examples  
+- **Efficient Format**: Clean JSON with just essential fields  
+- **Multi-Domain**: Reddit discussions across diverse topics  
+- **Research Ready**: Perfect for studying positive online discourse  
+- **Training Ready**: Ideal for training dialogue systems and conversation models  
+
+## Dataset Structure
+
+### Data Fields
+
+- **`sdid`** (string): Unique sample identifier for the Reddit discussion
+- **`text`** (string): Multi-author conversation text with anonymized author markers (`[author0]`, `[author1]`, etc.)
+
+### Data Format
+
+```json
+{
+  "sdid": "fp43zz2",
+  "text": "[author0] This is a great discussion starter. [author1] I agree, and here's some additional context that might be helpful..."
+}
+```
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+import json
+
+# Option 1: Using Hugging Face datasets
+dataset = load_dataset("NiklasKoch/reddit-constructive")
+
+# Option 2: Load directly from JSONL
+conversations = []
+with open('reddit_threads_constructive.jsonl', 'r', encoding='utf-8') as f:
+    for line in f:
+        conversations.append(json.loads(line))
+
+print(f"Loaded {len(conversations)} constructive conversations")
+print(f"First example: {conversations[0]}")
+```
+
+## Limitations and Considerations
+
+### Data Limitations
+- **Platform-Specific**: Reflects Reddit's culture and user demographics  
+- **Time-Bound**: Conversations from May 2020
+- **English Only**: No multilingual coverage
+- **Automated Selection**: Based on model predictions, not human annotation
+- **Context Removal**: Individual threads extracted from broader discussions
+
+### Bias Considerations
+- **Platform Bias**: Reddit users may not represent general population
+- **Topic Bias**: Some subjects may be over/under-represented
+- **Model Bias**: Selection reflects training model's understanding of "constructive"
+
+## Ethical Considerations
+
+### Responsible Use
+- Dataset intended for research and educational purposes
+- Users should consider platform-specific biases
+- Human oversight recommended for production applications
+
+## Dataset Creation
+
+This dataset was created by:
+1. Starting with 1.4M Reddit conversations from the **Pushshift Reddit dataset** (https://archive.org/download/pushshift_reddit_200506_to_202212/)
+2. Using a trained classification model (https://huggingface.co/NiklasKoch/qwen-discussion-classifier) to identify constructive discussions
+3. Filtering for high-confidence constructive predictions (prediction = 1.0)
+4. Retaining only essential fields (`sdid`, `text`) for clean format
+5. Anonymizing all usernames with generic author markers
+6. Quality validation and sample verification
+
+**Source Data**: Public Reddit discussions collected by Pushshift (May 2020)
+**Model Source**: https://github.com/Niklas257/Reddit-Constructiveness-Classification.git
+**Processing**: Automated classification and quality filtering  
+**Result**: 802,066 high-quality constructive conversations (56.3% of original dataset)
+
+## Dataset Card Authors
+
+Niklas Koch, Georg August University of Göttingen
+
+## Dataset Card Contact
+
+[email protected]

reddit_threads_constructive.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24231d7d4721d796f8a9cc6f4e8e3f22c2596ea848cabb77e65c8445fb18033a
+size 905602683