|
|
--- |
|
|
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. |
