license: mit
datasets:
- glyphsoftware/reasoning-router
language:
- en
base_model:
- distilbert/distilroberta-base
pipeline_tag: text-classification
Reasoning Router
A fine-tuned DistilRoBERTa model for classifying text based on reasoning depth. This model can categorize text into four reasoning levels: no-reasoning, low-reasoning, medium-reasoning, and high-reasoning.
Model Details
Model Description
The Reasoning Router is a text classification model designed to automatically categorize text based on the depth and complexity of reasoning present. It's particularly useful for:
Cost Optimization: Can be used in inference pipeline to route requests to appropriate models
Educational content analysis: Identifying the reasoning level of educational materials
Content filtering: Routing content to appropriate audiences based on complexity
Quality assessment: Evaluating the sophistication of written content
Research applications: Analyzing reasoning patterns in large text corpora
Developed by: Glyph Software LLP
Model type: DistilRoBERTa-based sequence classification model
Language(s) (NLP): English
License: MIT
Finetuned from model: distilbert/distilroberta-base
Model Sources
- Repository: glyphsoftware/reasoning-router
- Base Model: distilbert/distilroberta-base
- Training Dataset: glyphsoftware/reasoning-router
Uses
Direct Use
This model can be used directly for text classification tasks where you need to determine the reasoning depth of text content. It's particularly effective for:
- Cost Optimization: Can be used in inference pipeline to route requests to appropriate models
- Educational platforms: Automatically categorizing content by difficulty level
- Content moderation: Identifying complex reasoning that might require review
- Research tools: Analyzing reasoning patterns in academic or professional texts
- Quality control: Ensuring content meets specific reasoning requirements
Downstream Use
The model can be fine-tuned for specific domains or applications:
- Domain-specific reasoning classification (e.g., medical, legal, technical)
- Multi-language reasoning detection (with appropriate training data)
- Integration into larger NLP pipelines for content analysis
Out-of-Scope Use
This model is not designed for:
- General text classification beyond reasoning depth
- Reasoning generation or explanation
- Content creation or text generation
- Multilingual reasoning detection (trained only on English)
Bias, Risks, and Limitations
Limitations
- Language restriction: Only trained on English text
- Domain bias: Performance may vary across different domains and writing styles
- Context sensitivity: Reasoning depth can be subjective and context-dependent
- Training data limitations: Performance depends on the quality and representativeness of the training data
Recommendations
Users should:
- Validate results on their specific domain and use case
- Consider context when interpreting reasoning depth classifications
- Test thoroughly before deploying in production environments
- Monitor performance and retrain if necessary for new domains
How to Get Started with the Model
Using the Model
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load the model and tokenizer
model_name = "glyphsoftware/reasoning-router"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Prepare your text
text = "Your text here that you want to classify for reasoning depth."
# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(probabilities, dim=-1).item()
# Get the label
labels = ["no-reasoning", "low-reasoning", "medium-reasoning", "high-reasoning"]
predicted_label = labels[predicted_class]
confidence = probabilities[0][predicted_class].item()
print(f"Predicted reasoning level: {predicted_label}")
print(f"Confidence: {confidence:.3f}")
Using the Pipeline
from transformers import pipeline
classifier = pipeline("text-classification", model="glyphsoftware/reasoning-router")
result = classifier("Your text here")
print(result)
Evaluation
Factors
Evaluation considers:
- Reasoning level distribution across the test set
- Text length variations (up to 256 tokens)
- Domain diversity in the training data
Metrics
- Accuracy: Overall classification accuracy
- F1 Score: Weighted F1 score across all classes
- Per-class performance: Individual class precision and recall
Results
The model achieves competitive performance on reasoning depth classification, with optimized F1 score as the primary metric for model selection during training.
Model Examination
The model architecture is based on DistilRoBERTa, which provides:
- Efficient inference with reduced model size compared to full RoBERTa
- Robust representations for text classification tasks
- Fast tokenization with the Rust-backed BPE tokenizer
Technical Specifications
Model Architecture and Objective
- Architecture: DistilRoBERTa (6-layer transformer with 768 hidden dimensions)
- Objective: Sequence classification for reasoning depth detection
- Output: 4-class probability distribution
- Max sequence length: 256 tokens
Compute Infrastructure
Hardware
- Training: Compatible with CUDA, MPS, and CPU
- Inference: Optimized for CPU and GPU deployment
Software
- PyTorch: 2.8.0+
- Transformers: 4.55.0+
- Python: 3.12+
Glossary
- Reasoning Depth: The level of complexity and sophistication in logical thinking and argumentation present in text
- No-reasoning: Text that presents information without logical connections or argumentation
- Low-reasoning: Text with basic logical connections and simple argumentation
- Medium-reasoning: Text with moderate complexity in logical structure and argumentation
- High-reasoning: Text with sophisticated logical reasoning, complex argumentation, and deep analysis
More Information
For more details about the training process, dataset, and usage examples, please refer to the project repository and documentation.