readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - competition_math
  model-index:
  - name: bert-finetuned-math-prob-classification
    results: []
  widget:
  - text: "Find the number of positive divisors of 9!."
    example_title: "Number theory"
  - text: "Quadrilateral $ABCD$ is a parallelogram. If the measure of angle $A$ is 62 degrees and the measure of angle $ADB$ is 75 degrees, what is the measure of angle $ADC$, in degrees?"
    example_title: "Prealgebra"
  - text: "Suppose $x \\in [-5,-3]$ and $y \\in [2,4]$. What is the largest possible value of $\\frac{x+y}{x-y}$?"
    example_title: "Intermediate algebra"
  ---
  
  # bert-finetuned-math-prob-classification
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the part of the [competition_math dataset](https://huggingface.co/datasets/competition_math). Specifically, it was trained as a multi-class multi-label model on the problem text. The problem types (labels) used here are "Counting & Probability", "Prealgebra", "Algebra", "Number Theory", "Geometry", "Intermediate Algebra", and "Precalculus".
  
  ## Model description
  
  See the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model for more details. The only architectural modification made was to the classification head. Here, 7 classes were used.
  
  ## Intended uses & limitations
  
  This model is intended for demonstration purposes only. The problem type data was in English and contains many LaTeX tokens.
  
  ## Training and evaluation data
  
  The `problem` field of [competition_math dataset](https://huggingface.co/datasets/competition_math) was used for training and evaluation input data. The target data was taken from the `type` field. 
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  This fine-tuned model achieves the following result on the problem type competition math test set:
  ```
                          precision    recall  f1-score   support
  
                 Algebra       0.78      0.79      0.79      1187
  Counting & Probability       0.75      0.81      0.78       474
                Geometry       0.76      0.83      0.79       479
    Intermediate Algebra       0.86      0.84      0.85       903
           Number Theory       0.79      0.82      0.80       540
              Prealgebra       0.66      0.61      0.63       871
             Precalculus       0.95      0.89      0.92       546
  
                accuracy                           0.79      5000
               macro avg       0.79      0.80      0.79      5000
            weighted avg       0.79      0.79      0.79      5000
  ```
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  