---
license: mit
datasets:
- glyphsoftware/reasoning-router
language:
- en
base_model:
- distilbert/distilroberta-base
pipeline_tag: text-classification
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64bac13e81caff7f18e0a507/jAJcsbFx3dbkJB9eB7hEK.png)

# 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](https://huggingface.co/glyphsoftware/reasoning-router)
- **Base Model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base)
- **Training Dataset:** [glyphsoftware/reasoning-router](https://huggingface.co/datasets/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

```python
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

```python
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.

## Model Card Authors

[Glyph Software](https://huggingface.co/glyphsoftware)

## Model Card Contact

[Contact Us](mailto:contact@glyphsoftware.org)