Update README.md

README.md

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----
-license: mit
----
+---
+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.