| --- |
| 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](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) |
