---
license: mit
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
datasets:
- ChiekoSeren/RWKV-Thinking-problem-classify-v1
language:
- zh
- en
- fr
- ja
- ru
---

# RWKV-Thinking Problem Difficulty Classification

This model is designed to predict the difficulty of problems within the RWKV-Thinking dataset.    This prediction is used to estimate the number of reasoning paths required for multi-path reasoning.

**Model Overview:**

This model leverages the `RWKV-Thinking-problem-classify-v1` dataset to classify the difficulty of problems.     The difficulty classification is a crucial step in determining the complexity of reasoning required to solve a problem, which directly influences the number of reasoning paths explored during multi-path reasoning.

**Intended Use:**

*   Predicting the difficulty level of problems in the RWKV-Thinking dataset.
*   Estimating the number of reasoning paths needed for multi-path reasoning.
*   Evaluating the performance of language models in understanding and classifying problem complexity.
*   Supporting research in reasoning, problem-solving, and natural language understanding.

**Dataset Details:**

*   **Dataset Name:** `RWKV-Thinking-problem-classify-v1`
*   **Dataset Description:** This dataset assesses the diversity of problem types and the probability of successful problem-solving across various contexts.    It includes a range of problem statements, classifications, and associated metadata.
*   **Dataset Creation:**
*   **Curation Rationale:** Created to provide a benchmark for evaluating how well models like RWKV can handle diverse problem types and predict solution success.
*   **Source Data:** Problems may be sourced from synthetic generation, educational materials, or curated problem-solving repositories.
*   **Preprocessing:** Problems were standardized, categorized, and assigned diversity and success probability scores.
*   **Annotations:** Manual annotation by domain experts or automated scoring based on predefined criteria.    Annotators assessed problem complexity, uniqueness, and solvability.
*   **Fine-tuning Dataset Size:** 1K < n < 10K

**Model Training:**

*   **Model Architecture:** BERT
*   **Training Data:** `RWKV-Thinking-problem-classify-v1` dataset.

**Ethical Considerations:**

*   **Social Impact:** This model can advance AI research in reasoning and education, potentially aiding in personalized learning systems or automated tutoring tools.
*   **Biases:** Potential biases may arise from the selection of problem categories or the subjectivity in assigning diversity and success scores.    Users should evaluate these factors for their specific use case.
*   **Limitations:** Limited scope to predefined categories.    Success probability may vary based on model capability or user expertise.